Jurnal Ilmiah Ilmu Terapan Universitas Jambi

ORGANISATIONAL SUPPORT AND TEACHERS' ENGAGEMENT FOR PROFESSIONAL DEVELOPMENT IN SECONDARY SCHOOLS OF NEPAL

Sushma Tamang — 2024-09-14

The teacher professional development program in Nepal is an ambitious initiative designed to support teachers' professional growth. Organizational support and teacher engagement are of the utmost importance to ensure professional development. This study signifies the role of organizational support in enabling professional development and engaging teachers in teacher growth and development. This study aims to propose and rest an integrative model that views organizational supoort practices as a synergistic component that influences the sustainbility of teacher engagement. Survey-based cross-sectional data were collected from secondary schools in Kathmandu Valley using a structured questionnaire to teachers for this non-experimental correlational research design. Likert scales were used to express the perception-based data quantitatively and analyzed using regression models. This study confirms that organizational support procedures strongly influence professional development, highlighting organizational support as a moderator between teacher engagement and professional development. Organizational support also contributes to improving organizational performance. These practices are standard in Nepal schools but at a moderate level. However, schools are run for the benefit of one person only, so schools must be institutionalized with standard norms and values of participatory practices.

ENHANCING COMPETITIVENESS OF PERUVIAN TEXTILE MSES THROUGH QUALITY MANAGEMENT: A FOCUS ON LEADERSHIP, TRAINING, AND CONTINUOUS IMPROVEMENT

Marco Antonio Ledesma Munive — 2024-09-21

This study examines the impact of quality management on the competitiveness of micro and small enterprises (MSEs) in the Peruvian textile clusters, a sector that is a significant driver of local economic activity. Unlike previous studies, this research provides a comprehensive analysis of the combined influence of leadership, employee training, customer orientation, continuous improvement, and benchmarking on MSE competitiveness. Utilizing a robust quantitative research design, data were collected from 347 participants through a structured survey. The findings reveal a high level of engagement in quality management practices among the surveyed MSEs, with the identified factors significantly contributing to improved productivity, customer satisfaction, and market presence. The originality of this study lies in its in-depth exploration of these quality management dimensions within the specific context of Peruvian textile clusters, offering new insights into their role in driving competitiveness. The implications are particularly relevant for business owners, managers, and policymakers seeking to enhance the sustainability and competitive advantage of textile MSEs in a highly dynamic market environment.

COMPETENCE OF VILLAGE APPARATUS: IS IT TRAINING MATERS?

Arief Rohman — 2024-09-23

The training that has been attended by village officials so far has not yielded optimal results. Meanwhile, training from various overseeing organizations is conducted annually. This study aims to investigate the role of motivation and perceived organizational support in mediating and moderating the impact of training on competence. The unit of analysis for this research is the village officials serving in all villages in Central Java Province, with a sample of 468 village officials determined using the Slovin method. Questionnaires were distributed based on the area random sampling method, and the data collected were analyzed using the SEM-PLS method with the WARP-PLS model. The results of the study show that both motivation and perceived organizational support mediate and moderate the impact of training on competence. Relevant organizations, including village governments, village empowerment services, and the Ministry of Villages, need to develop sustainable training policies and programs for village officials that are evaluated periodically.

VILLAGE APPARATUS PERFORMANCE: THE ROLE OF CULTURE, ORGANIZATION SUPPORT AND JOB SATISFACTION?

Muhammad Asnawi Sabil — 2024-09-23

The performance of village officials has always been a focal point for various parties because the foundation of urban and national progress lies in rural development. However, the performance of village officials in Indonesia is still not optimal. This study aims to investigate the roles of job satisfaction, organizational culture, and perceived organizational support as mediators and moderators in the relationship between competence and performance. The research involved 368 village officials across 19 regencies in West Java province, selected using the inverse root square method. Data collection was conducted through questionnaire distribution using area random sampling methods. Data analysis was performed using Warp PLS method. The findings indicate that job satisfaction successfully mediates the relationship between competence and performance. Organizational culture and perceived organizational support significantly moderate the influence of competence on performance. Village government bodies, village community empowerment agencies, and the Ministry of Villages need to formulate policies that support improving job satisfaction, foster a healthy organizational culture, and provide facilitative support, both in infrastructure and in developing hard and soft skills for village officials.

THE NEXUS BETWEEN TRADE OPENNESS AND ENVIRONMENTAL DEGRADATION : A VECM ANALYSIS

Tania Wicaksana — 2024-09-23

This research aimed to analyze the influence of trade openness, ICT, per capita income, and Human Development Index on environmental quality in Upper-Middle Income countries in the short and long term and how per capita income influenced environmental quality in the U-shaped EKC hypothesis in Upper-Middle Income countries over the 1990-2020 periods using the VECM Panel analysis. Several findings were revealed in this research. First, the coefficient of trade openness in the long-term and short-term on environmental quality was positive and statistically significant at the 1% level. Second, the relationship between ICT and environmental quality had a positive influence in the short term and a negative influence in the long term. Third, the relationship between per capita income and environmental quality positively influenced the long- and short-term. In these findings, an increase in per capita income increased carbon dioxide, which caused a decrease in environmental quality and emerged an inverted U relationship in the long- and short-term of the EKC hypothesis in UMI countries, with the turning point in the short-term being US$ 728,530 and in the long-term US$ 772,839. Finally, the Human Development Index in this research positively influences environmental quality in the short- and long-term. The outcomes of this study have considerable Policy implications regarding trade openness policy formulation to reduce environmental degradation, especially in Upper Middle-Income Countries.

DOES COVID-19 AFFECT SHARIAH COMPLIANT STOCK? EVIDENCE FROM SELECTED OIC COUNTRIES

Zakir Hossen Shaikh — 2024-10-10

This study aims to examine the movements of Islamic stock markets in ten selected OIC (Organization of Islamic Cooperation) countries in relation to Covid-19 cases, providing a comprehensive analysis of market behavior during the pandemic. The countries—Saudi Arabia, Pakistan, Bangladesh, Turkey, Indonesia, Oman, Qatar, UAE, Kuwait, and Bahrain—were chosen based on their large Muslim populations. Data was collected over a one-year period from January 1, 2020, to January 31, 2021, analyzing the relationship between Covid-19 cases and Islamic stock market indices. The study employed co-integration tests to identify long-term relationships and the Vector Error Correction Model (VECM) to explore short-run dynamics. The co-integration test results show a significant long-run relationship between Covid-19 cases and Islamic stock markets in most of the selected OIC countries. Specifically, the Shariah indices in Pakistan, Bangladesh, Turkey, Qatar, UAE, Kuwait, and Bahrain have a positive and significant relationship with Covid-19 cases. Conversely, Saudi Arabia, Indonesia, and Oman exhibit a negative long-term relationship with Covid-19 cases, suggesting a different market response. These results suggest that countries with diversified economies, particularly those relying on natural resources such as oil and agriculture, were more resilient during the pandemic. This study provides novel insights into the unique responses of Islamic stock markets in OIC countries during the pandemic, highlighting regional differences in market behavior and recovery. It suggests that despite the global economic downturn, OIC countries present attractive investment opportunities, particularly due to their swift recovery and resource-based economies, offering a robust portfolio for investors during crises.

INSTRUCTIONAL LEADERSHIP AND TEACHER SELF-EFFICACY ON JOB SATISFACTION: THE MEDIATING EFFECT OF SCHOOL CLIMATE IN INDONESIAN ISLAMIC SENIOR HIGH SCHOOLS

Ahmad Ridwan — 2024-10-11

This study explores the impact of instructional leadership and teacher self-efficacy on job satisfaction among teachers in Indonesian Islamic Senior High Schools (Madrasah Aliyah), focusing on the mediating role of school climate. Instructional leadership plays a crucial role in shaping a positive school environment, which can enhance teacher job satisfaction by providing necessary guidance, resources, and support. A mixed-method approach was employed, with data collected from 219 teachers using validated survey instruments. The Content Validity Index (CVI) was used to ensure instrument reliability. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess relationships between variables and the mediating effect of school climate. The results show that instructional leadership significantly impacts both school climate and teacher job satisfaction, with path coefficient values of .463 and .818, respectively. However, school climate did not significantly mediate the relationship between instructional leadership and job satisfaction. Teacher self-efficacy, while positively influencing school climate (p < .05), did not have a substantial direct effect on job satisfaction (ß = -0.058). This research provides fresh insights into the nuanced interplay between leadership, teacher self-efficacy, and job satisfaction in the context of Islamic schools. The findings highlight school climate's indirect yet pivotal role in educational leadership dynamics. By integrating these variables, the study offers practical recommendations for improving leadership strategies and enhancing teacher well-being in faith-based educational settings.

A TRACER STUDY OF PHD IN EDUCATIONAL ADMINISTRATION GRADUATES: RECOMMENDATIONS TO STRENGTHEN THE PROGRAM

Jupeth T. Pentang — 2024-10-17

This study aims to evaluate the effectiveness and relevance of a PhD in Educational Administration program at a State University in Western Philippines through a tracer study, offering insights into the graduates’ demographic and academic profiles, career alignment, and recommendations for improvement. The study employed a descriptive research design, collecting data from 19 non-randomly selected graduates based on availability. Data were gathered using an instrument based on guidelines from the Philippine Commission on Higher Education, with additional elements drawn from previous tracer studies. Analytical tools such as frequency counts, percentages, and arithmetic means were used to describe the findings. Results highlight that the PhD in Educational Administration program effectively serves mid-career professionals, attracting individuals from diverse backgrounds and helping them meet evolving leadership demands in educational administration. Graduates reported that the program equipped them with the necessary skills to excel in their current roles. However, they suggested several areas for improvement, including enhancing the program's accessibility and inclusivity. These recommendations are crucial to ensuring the program remains relevant, especially in preparing leaders capable of addressing modern challenges in education. This research provides new insights into the strategic role of tracer studies in shaping the future of educational leadership programs. It aligns with Sustainable Development Goal 4 (Quality Education) by proposing a continuous curriculum review, which ensures the program evolves alongside educational leadership trends. The study also underscores the importance of equipping faculty with current insights to strengthen the program's ability to prepare leaders for a dynamic educational landscape.

A STUDY OF JAMBI BATIK ARTISANS IN INNOVATION AND STRATEGIC DECISION-MAKING TO INFLUENCE THE DEVELOPMENT AND RESILIENCE OF THE JAMBI BATIK INDUSTRY

Ade Octavia — 2024-11-01

This study investigates Jambi batik artisans' resilience and innovative strategies in overcoming economic challenges and disruption in the textile industry. Using a qualitative approach with data analysis based on Miles and Huberman’s interactive model, the research delves into how these artisans creatively adapt to changing market demands by incorporating alternative materials, eco-friendly dyes, and distinctive local designs to enhance product appeal. The study also explores their marketing strategies, which leverage digital platforms and direct sales at exhibitions, significantly expanding their consumer reach and maintaining competitiveness. This combination of traditional artistry and modern tactics supports the preservation of Jambi's cultural heritage. It aligns with Sustainable Development Goals (SDGs) related to responsible production, decent work, and gender equality. The findings reveal that Jambi batik artisans successfully merge traditional motifs with contemporary styles, allowing them to preserve local identity while meeting evolving consumer preferences. Notably, this research highlights how artisans overcome resource limitations and market fluctuations by engaging in strategic collaborations, fostering community support, and adopting digital innovations to broaden their reach. This study's novelty lies in its focus on the dual role of cultural preservation and sustainable economic practices, offering insights into how traditional industries can thrive in modern contexts. The outcomes emphasize that with strategic innovation, the Jambi batik industry has substantial potential for sustainable growth, presenting a model for other cultural enterprises seeking resilience in the face of globalization and economic challenges.

GROSS ALPHA/BETA ACTIVITY DETERMINATIONIN DRINKING WATER PROFICIENCY TESTS

Florinda Cfarku — 2024-09-14

Water intended for drinking purposes has to be analyzed first for gross alpha/beta activity according to national and international standards and recommendations. According to Albanian legislation, Article 6, the gross alpha/beta radioactivity concentration in water, should be below the level of 0.1 Bq/L and 1 Bq/L respectively for human consumption. Our laboratory participated in an interlaboratory comparison organized by IAEA Terrestrial Environmental Laboratory under suggestion of ALMERA members. Proficiency Test among environmental radioactivity monitoring laboratories for the determination of gross alpha/beta activity concentration in drinking water and contaminated surfaces (2018, 2020). Independent standard methods were used for the reference value determination. Each sample was pretreated on site with nitric acid until reaching a pH level below 2. By creating relatively non-polar surfaces, this procedure avoids a loss of radionuclide fractions due to absorption into the walls of the containers.The Gross alpha/beta activity measurements were done using gas-flow proportional counter (GPC) method. The total dissolved solids should not exceed a surface density of 5 mg/cm² for gross alpha determination and 10 mg/cm² for gross beta determination The performance of participating laboratories was evaluated with respect to the reference values using relative deviations. The results presented from our laboratory in both inter comparisons (IAEA-TEL-2018-03) and (IAEA-TEL-2020-03) were all acceptable. The laboratory for the measurement of gross alpha/beta radioactivity and our Institute as a part of Tirana University is in the process of accreditation, so far the participation in the inter comparisons exercise is very important.

URBAN FLOOD RESILIENCE: A MULTI-CRITERIA EVALUATION USING AHP AND TOPSIS

Robert Kosova — 2024-09-21

Floods are increasingly recognized as one of the most destructive natural disasters, driven by urban expansion, climate change, and unregulated development. This is particularly true in developing countries, where rapid urbanization has increased impervious surfaces, amplifying flood risks in urban areas. This study focuses on Albania, evaluating urban resilience against floods through the lens of water-related disaster data. Using a multi-criteria decision analysis (MCDA) approach, specifically the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the research assesses flood vulnerability and resilience in rapidly urbanizing regions. Integrating AHP and TOPSIS with mixed methods introduces a novel approach, offering a comprehensive evaluation framework for flood risk management. The findings highlight critical vulnerabilities and suggest that targeted urban planning and disaster mitigation efforts can enhance resilience. Future research could incorporate climate projections and granular urban data, supporting a more adaptive flood management strategy.

DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT

Dilovan Asaad Majeed — 2024-09-23

The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was conducted, utilizing databases such as Professor, Scholar, Embl, and Nih to identify relevant studies on air pollution epidemiology. The review highlights the integration of data mining, machine learning, and spatiotemporal modeling to improve the detection, analysis, and forecasting of air pollution-related health issues. The findings reveal a growing trend in applying data mining techniques within the field of air pollution epidemiology. Advanced methods, such as spatiotemporal analysis and geographic data mining, enable more precise tracking and forecasting of pollution-related health risks. Continuous advancements in artificial intelligence and the development of more sophisticated sensors and data storage technologies are enhancing the accuracy and reliability of air quality monitoring and public health predictions. This study highlights the transformative potential of integrating data mining and AI techniques into air pollution epidemiology. Exploring emerging technologies like spatiotemporal mining and next-generation sensors paves the way for more accurate, timely, and scalable solutions to monitor air quality and predict its impact on public health, opening new avenues for research and policy interventions.

COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS

Ahmed Alaa Hani — 2024-09-23

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

REGRESSION AND CLASSIFICATION MODELS FOR HUMAN AGE PREDICTION

Bhuvaneshwari Patil — 2024-10-10

This study aims to enhance automated age prediction from facial images, a task with significant potential in security, law enforcement, and Human-Computer Interaction (HCI). While age estimation has seen progress, it remains a challenging problem due to the diverse factors influencing facial aging, such as genetics, environment, lifestyle, and facial expressions. These variations result in individuals of the same chronological age looking markedly different. Most existing age estimation methods rely on computationally intensive pre-trained models, often treated as "black boxes" with predefined input sizes and limited flexibility. To address these limitations, we propose using Convolutional Neural Networks (CNNs) for age prediction. Our approach combines classification and regression techniques to predict age more accurately. We applied our model to publicly available datasets, including FGNET, Adience, APPA-REAL, UTKFace, and All-Age-Face, encompassing images from constrained and unconstrained environments. The proposed CNN model was evaluated against existing pre-trained models, demonstrating comparable performance in age prediction tasks. Both classification and regression results underscored the model's accuracy, offering additional benefits in reduced computational complexity, increased flexibility, and adaptability. This study introduces a CNN-based approach as a viable alternative to pre-trained models for automated age prediction. It offers competitive accuracy while addressing critical limitations of current models, such as computational demands and lack of flexibility, thus contributing a more efficient solution for age estimation tasks in various real-world applications.

TRANSFORMATION OF DIGITAL INNOVATION IN EDUCATION IN THE POST-COVID ERA: AN EXPLORATION CENTERED ON DRONES AND VIRTUAL REALITY

Jiaxin Lu — 2024-10-26

The relevance of this study is underscored by the significant shifts in traditional methods of teaching software disciplines in the post-COVID era. The widespread adoption of remote learning and integrating innovative technologies have necessitated re-evaluating educational practices. This study aims to explore the impact of modern innovative technologies on the effectiveness of education and the quality of learning in the post-COVID period. The primary method employed in this study was a survey, through which questions related to the use of drones and virtual reality (VR) technologies in the educational process were posed to students and educators. The results obtained during this study indicate a high level of acceptance by modern students and their educators of the role of drones and VR technologies in the educational environment of contemporary educational institutions. Participants in the surveys positively assessed the transformation trends of innovative technologies in the educational environment, which applies to almost all groups of students and educators without exception, regardless of their specialties. Respondents highlighted multiple positive effects of integrating drones and VR technologies into the educational space of educational institutions, including a high level of comfort when using these technical devices, improvement in the perception of educational material, and enhancement of the quality of mastering software disciplines. Both educators and students positively view the emerging prospects of practical use of drones and VR technologies in educational institutions' educational space, particularly in the post-COVID period and beyond.

SENTIMENT ANALYSIS OF PUBLIC OPINION ON PRESIDENTIAL ADVISORY APPOINTMENTS USING NAIVE BAYES CLASSIFICATION

Edi Surya Negara — 2024-11-10

Social media platforms such as Twitter, Facebook, and YouTube have become significant channels for public discourse, where users freely express opinions, including negative sentiments and hate speech. To better understand public opinion, particularly in politically charged contexts, sentiment analysis can classify user comments as either positive or negative. This study aims to analyze public sentiment regarding the formation of a special advisory team for President Jokowi, using a sentiment classification approach. The study employed a Naïve Bayes classifier to analyze sentiment from 3,000 comments gathered from Twitter, Facebook, and YouTube. The dataset was divided into 80% training data (used to train the model with known sentiment) and 20% test data. The Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling large datasets in text classification tasks. The Naive Bayes classification on sentiment analysis of public opinion regarding the appointment of presidential advisors achieved an overall accuracy of 71% in classifying the test data. Negative sentiment was classified with an accuracy of 71%, while positive sentiment was classified with an accuracy of 70%. The results demonstrate that the Naïve Bayes classifier is a viable method for sentiment analysis in political discourse, although the model's performance indicates room for improvement. The novelty of this research lies in its focus on sentiment analysis of public opinion specifically related to presidential advisory appointments, an area not yet extensively explored in sentiment analysis studies. This study contributes to the field by providing insights into the public’s perception of political decisions using machine learning techniques. The implications for future research include refining classification methods for better accuracy and applying the model to other political or governmental topics.

SENSORLESS SPEED ESTIMATION OF THREE PHASE INDUCTION MOTORS BASED ON DYNAMIC MODEL

Alfred Pjetri — 2024-09-14

In speed control systems of induction motor electrical drives, real-time speed monitoring is necessary. Speed monitoring can be done using the direct method, which uses a mechanical sensor mounted on the motor shaft, or the indirect method, which is based on estimation, mainly from the dynamic model of the motor. Speed estimation based on the dynamic model of the motor in orthogonal coordinates is the most widespread method in sensorless speed control systems of three-phase squirrel cage induction motor electrical drives, especially those of high accuracy. This paper presents the open-loop speed estimator used for speed estimation in three-phase induction motors. The proposed speed estimators are based on the orthogonal coordinate’s dynamic model of an induction motor in a stator reference frame. This technical solution is simple and has a low cost. The currents and voltages of the two motor phases are the input variables of the estimator, while the output variable is the induction motor speed. The speed estimator model is built using LabVIEW software. The dynamics and accuracy of the estimator proposed in this paper have been tested experimentally. The speed measured by the industrial incremental encoder is compared with that of a speed estimator modeled in LabVIEW software. The obtained experimental results show a good match between the measured and estimated speeds under the step torque load changes of the induction motor.

ESTIMATOR AND APPLIED MIXED KERNEL AND FOURIER SERIES MODELLING IN NONPARAMETRIC REGRESSION

Sudiarsa I Wayan Sudiarsa — 2024-11-09

There are three nonparametric regression approaches, namely parametric, nonparametric, and semiparametric regression. Nonparametric regression allows the response variable to follow a different curve from one predictor variable to another. In paired data, the components of the predictor variables and response variables are assumed to follow unknown data patterns, so they can be approached with kernel-based regression models and Fourier series. The basic components are approached with kernel functions and Fourier series functions. Errors are assumed to be normally distributed with zero mean and constant variance. The originality of this research is to obtain a mixed kernel and Fourier series model estimator and then apply it to poverty data in Bali Province. The research stage method begins with a nonparametric regression model estimator based on kernel and Fourier series. The next step is to research the regression curve estimation and obtain lemmas and theorems. The results of the function estimation are highly dependent on the bandwidth, smoothing, and oscillation parameters. In the application to the case of real data, the resulting model gives an R2 value of 0.6278, meaning that the variables used can explain the model by 62.78 percent. From the modeling results obtained, the Open Unemployment Rate has a positive effect on the percentage of poor people in Bali.

HONEY PUMPKIN STEM BORER, Apomecyna saltator FABRICIUS (COLEOPTERA: CERAMBYCIDAE) CAN BE CONTROLLED WITH PHOSPHORUS AND POTASSIUM FERTILIZER

Wilyus Wilyus — 2024-09-23

Apomecyna saltator, a notorious pest of honey pumpkin plants, poses a significant threat to both vegetative and generative phases, leading to considerable production losses. Despite its agricultural impact, there are limited studies on effective management strategies for this pest. This research investigates the effect of combined phosphorus and potassium fertilization on controlling A. saltator infestations in honey pumpkin plants. The study was conducted at the Teaching and Research Farm of the Faculty of Agriculture, Jambi University. A randomized block design with five treatments and five replications was used, testing different combinations of SP-36 phosphorus and potassium chloride (KCl) fertilizers: no fertilization (p0), SP-36 0.67 g/plant + KCl 1.25 g/plant (p1), SP-36 1.00 g/plant + KCl 1.88 g/plant (p2), SP-36 1.34 g/plant + KCl 2.51 g/plant (p3), and SP-36 1.67 g/plant + KCl 3.13 g/plant (p4). The variables observed included stem diameter, larval population, percentage of plant damage, number of attacked plant segments, fruit weight, and overall production. Data were analyzed using ANOVA and Duncan's Multiple Range Test (DMRT). The results indicated that combining phosphorus and potassium fertilizers significantly increased stem diameter, fruit weight, and yield while reducing larval populations, plant damage, and affected segments. The study demonstrates that phosphorus and potassium strengthen honey pumpkin plants and negatively impact A. saltator infestations. This research highlights a novel approach to pest control, showing that targeted fertilization promotes plant health and suppresses the honey pumpkin stem borer (A. saltator). This dual benefit of nutrient application provides a sustainable and efficient pest management strategy, offering a fresh perspective on integrated crop protection.

QUALITY OF LIFE: THE SPIRITUALITY AND SPIRITUAL WELLBEING OF ELDERLIES IN MALAYSIA

Fatimah Binti Yahya — 2024-09-14

With an increasing aging population worldwide, there is a need for greater focus on the spirituality of older people, to better support them and lead them to an enhancement of their inner peace. This study used a qualitative with exploratory sequential design approach (with semi-structured interviews) in exploring spirituality with older people in community of Federal Territory of Kuala Lumpur, which is related to a sense of personal belief and connection that helped them in their daily lives. The research involved a rigorous exploratory process. The fifteen (15) participants provided information about their rich experiences and their connections in four domains: (1) Faith, (2) Belief, (3) Practice and (4) Emotion. Results: The reliability of the dimensions was all high as the Cronbach’s Alpha coefficients were above 0.90. Therefore, this spirituality measurement tool can be considered highly reliable. Conclusion: This paper has developed as well as validate the spirituality measurement tool. It was used to construct a profile of elderly in Malaysia, and it may be applied to profile elderly in other countries.

SELF-EFFICACY REDUCES RELAPSE IN DRUG ABUSE WITH COGNITIVE BEHAVIOUR THERAPY

Meri Susanti — 2024-09-14

Maladaptive substance abuse (NAPZA) can lead to clinically significant disorders. According to the National Narcotics Agency (BNN) states that around 90 percent of former drug addicts who undergo the rehabilitation process experience a relapse and return to abusing drugs. The research results show that cognitive Behaviour Therapy (CBT) is an effective intervention method in treating drug abuse. This research aims to test the effectiveness of cognitive behavior therapy to increase self-efficacy to reduce the risk of relapse in drug abusers. The study used a pre-posttest experimental method with a control group. Research respondents were selected using a purposive sampling technique with a sample size of 30 people. Both groups were given the Drug Abstinence Self-Efficacy Scale (DASES) measurement to see the effectiveness of applying cognitive behavior therapy in increasing self-efficacy in addicts who have participated in the rehabilitation program. The change in self-efficacy in both groups from medium to high was 66.7% after the intervention. A comparison between the two groups was made before the intervention, where the significance value was p = 0.399, and after the intervention, there was a change in the score between the two groups to 0.001. There was a significant difference between the two groups before and after the intervention, with a value of p = 0.001. Meanwhile, there was no substantial change in the control group in p=0.177. CBT is effective in increasing abstinence self-efficacy in drug addicts as an effective therapy to reduce the risk of relapse in drug addicts in rehabilitation programs

IDENTIFYING RISK FACTORS FOR STUNTING AMONG UNDER-FIVE INDONESIAN CHILDREN

Mayang Sari Ayu — 2024-09-14

The prevalence of child stunting in Indonesia in 2023 at the national level from 24.5% to 21.6%. Despite declining, the figure is still relatively high, as 28 out of 100 babies suffer from stunting. The study aims to predict stunting risk factors in children under the age of 5. They use bivariate and multivariate logistic regression to identify significant factors associated with stunting risk. Stunting prevalence in Batu Bara District of North Sumatra Province was 18.35% out of 28.716 children under 5 years of age (967 children very short and 3,944 young children). The study was conducted on 100 households with brief and concise children living in rural areas. The results of the study showed maternal education (OR 5.27; 95% CI 1.04-5.62); low maternal nutritional status during pregnancy (OR 2.57, 95% CI 1.22-5.89); formula milk delivery less than 6 months (OR 3.47; 95% CI 1.40-8.28); low birth weight of less than 2500 grams (OR 4.94; 95% CI 1.30-18.80); lack of access to drinkable water (or 3.14; 95% CI 1.29-7.65); low family wealth index (OR 0.13; 95% CI 0.05-0.32). Environmental and household factors have a significant relationship with stunting risk control. Multilateral efforts from the level of family empowerment, increasing the role of health services, and improving nutritional status are access to public health status.

KNOWLEDGE, PRACTICES, AND CONSTRAINTS AMONG BREASTFEEDING EMPLOYEES TOWARDS IMPLEMENTATION OF LACTATION AREA

Joseph A. Villarama — 2024-10-14

Breastfeeding empowers women and children and working mothers who revolutionize the workplace globally, specifically in carrying mutual roles as employees delivering quality service to humanity and mothers nurturing their children. This study explored the knowledge, practices, and constraints among breastfeeding working mothers in Region III, Philippines, towards implementing lactation areas. A mixed-methods approach combined a quantitative survey with a qualitative semi-structured interview component. Findings showed a high prevalence of breastfeeding among working mothers, coupled with a good level of knowledge about breastfeeding practices. However, constraints such as lack of lactation areas in offices, heavy workload, and limited support were revealed, thus foregrounding the need for institutionalized policies and guidelines to establish lactation areas in offices, promote breastfeeding, and support working mothers and their welfare, anchored on sustainable development goals (SDGs). By addressing these issues, workplaces position a more conducive environment for breastfeeding mothers, contributing to improved maternal and infant health outcomes. Further, this research provides valuable insights for policymakers, employers, and healthcare providers in initiating more effective strategies to support breastfeeding working women. Through the documented findings and by providing concrete recommendations for policy and infrastructure, this research advances the shaping of a more breastfeeding-friendly environment, promotion of maternal and infant health, supporting SDG 3-good health and well-being, and enhancement of workplace productivity towards achieving SDG 8-decent work and economic growth and SDG 5-gender equality.

VALIDITY OF ENGAGEMENT INSTRUMENT DURING ONLINE LEARNING IN MATHEMATICS EDUCATION

Riyan Hidayat — 2024-09-14

The current study aimed to assess the validity of the engagement instrument during online learning in mathematics education. This study used a survey research design as its approach. The current research participants were 203 Generation Z students in West Nusa Tenggara Barat, Indonesia. Convenience sampling techniques were used to assess who had completed the online survey. Three procedures were used to analyze the data in this research: exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Rasch analysis. EFA revealed that the online engagement instrument had two sub-components: behavioral engagement and emotional engagement. At the same time, the CFA results showed that the model fit indices established the first- and second-order model's two-factor structure. Finally, the results showed that the online engagement instrument’s item and person reliability were good. The findings indicate a potential for enhancement even though the Rasch analysis largely supported the results of EFA and CFA. The current research’s novelty is that it provides a valid and reliable instrument to assess student`s engagement during online learning in a mathematical education context. Using the current instrument can ensure the accuracy, reliability, and credibility of research on student engagement during online learning in mathematics education.

THE INFLUENCE OF MANAGERIAL TEAM E-PROJECTS ON STUDENTS’ INDEPENDENT ATTITUDES

Retni S Budiarti — 2024-10-11

This study aims to evaluate the impact of the E-Project Managerial Team on fostering independence among Biology Education students at Universitas Jambi Faculty of Teacher Training and Education. The research investigates how managerial technology influences students' ability to manage tasks autonomously in an academic setting. Using a mixed-methods approach with an exploratory sequential design, the study combines quantitative data from questionnaires with qualitative insights. A random sampling method was applied to select participants from 143 students. Data analysis was conducted using IBM SPSS 24, employing descriptive statistics, normality, linearity, and regression tests to interpret the relationships between variables. The normality test returned a significance value of 0.614, while the linearity test showed a value of 0.482, confirming the appropriate distribution and relationships of the data. Results from the regression analysis (F value of 36.487, p = 0.001) revealed a statistically significant positive influence of the E-Project Managerial Team on students' independence. Descriptive analysis also indicated that while some students held neutral views, a considerable portion acknowledged improvements in their self-management and autonomy due to the team's involvement. This research uniquely integrates managerial technology with biology education, highlighting how digital project management tools can enhance student independence. It contributes to the growing literature on educational technology by offering innovative methods for promoting autonomy, time management, and individual skill development among students. This work provides practical insights for educators seeking to implement technological solutions to support self-directed learning.

EVALUATING THE IMPACT OF AI–POWERED TUTORS MATHGPT AND FLEXI 2.0 IN ENHANCING CALCULUS LEARNING

Joel I. Alvarez — 2024-10-17

This study evaluates the effectiveness of AI-driven technologies, Flexi 2.0 and MathGPT, in enhancing personalized learning and advanced cognitive abilities among pre-service mathematics educators participating in Calculus I. Participants were allocated to a control group receiving conventional training and an experimental group utilizing AI technology, applying a quantitative experimental design. Data were gathered utilizing validated evaluations, surveys, and interviews. The results indicated that students employing AI tutors showed significant improvements in problem-solving and personalized learning compared to the control group. Concerns arose regarding potential over-reliance on AI, underscoring the need for rigorous criteria to guarantee that students engage actively in learning rather than passively receiving AI-generated responses. The findings suggest that educators should create activities requiring students to critically evaluate AI-generated responses to promote independent thinking. The findings highlight the potential of AI in mathematics education while stressing the importance of ethical considerations and teacher oversight. It requires both students and staff training to adapt to improvements in AI inside education. The study reveals that while AI can improve learning outcomes, careful implementation is essential for its effective and responsible use in educational settings.

OPTIMIZING LEARNING THROUGH GAGNE'S THEORY: TEACHING VIDEO ANALYSIS FOR PROSPECTIVE TEACHERS

Syahrial Syahrial — 2024-10-21

This study explores and describes the learning process of elementary school teachers and professional education students through the lens of Gagné's learning theory. Using a qualitative approach with a descriptive method, the research analyzes recorded learning sessions of students enrolled in the program. Data collection involves examining the recorded lessons, and the analysis follows a descriptive qualitative framework. The findings reveal that implementing Gagné’s nine instructional events, particularly goal-oriented and active learning strategies, positively influenced student interaction and comprehension of the material. Although there were variations in the ability to recall prior knowledge, all recordings demonstrated significant efforts in delivering content effectively, providing thorough guidance, and offering constructive feedback to enhance knowledge retention and transfer. This study provides a fresh perspective on applying Gagné’s learning theory in professional education, emphasizing its impact on teacher training. Unlike previous studies that often focus on theoretical aspects, this research offers empirical insights into how Gagné’s instructional events can foster deeper engagement and understanding in real-world classroom contexts. Additionally, the study highlights students' diverse experiences in applying Gagné's principles, offering new strategies for improving teaching methods. The implications suggest that educators in teacher preparation programs can use these findings to design more interactive, structured, and outcome-oriented learning environments, ultimately enhancing the quality of education in teacher training programs.

THE INFLUENCE OF GENERIC SKILLS ON GENDER DIFFERENCES IN SCIENTIFIC PROCESSING AND CRITICAL THINKING IN PHYSICS MOTION DYNAMICS

Darmaji Darmaji — 2024-10-21

This study investigates the gender differences in generic skills, scientific process abilities, and critical thinking among high school students during physics learning, specifically within motion dynamics. The research aims to uncover the impact of generic skills on students' scientific processing and critical thinking abilities while examining how gender influences these cognitive domains. Employing a comparative quantitative research design, data were gathered from 180 students (90 male and 90 female) in Jambi City using observation sheets, test questions, and questionnaires. The sample was selected through simple random sampling, and statistical analyses were performed to examine the differences in learning outcomes between genders. Results revealed notable gender disparities in generic skills, scientific processing, and critical thinking, with male and female students demonstrating distinct proficiencies in physics learning. This underscores the necessity of adopting gender-sensitive educational approaches to foster equitable learning experiences. The uniqueness of this research lies in its detailed exploration of the interconnectedness between generic skills, scientific processing, and critical thinking within the framework of gender. Unlike prior studies, which often focus on isolated skills, this study integrates these core competencies to provide a more holistic understanding of how gender influences physics learning. The findings emphasize the need for inclusive curricula and gender-tailored instructional strategies that promote equal opportunities for all students in science education, particularly in physics, thus contributing to a more balanced and equitable academic environment.

COGNITIVE PSYCHOLOGY STUDY AND MATHEMATICAL PROCESS SKILLS ON STUDENTS' ANSWERS IN MATHEMATICS LEARNING IN COMPARATIVE MATERIAL

Kamid Kamid — 2024-10-21

This research explores the differences between the two schools regarding three key variables: students' cognitive psychology, mathematical processing skills, and responses to mathematics learning, specifically within the context of comparative material in Grade VII. Additionally, it seeks to compare these three variables to understand how they vary across the two institutions. The study adopts a quantitative approach, with purposive sampling used to select a sample of 120 Grade VII students from two junior high schools. Data were gathered using a questionnaire based on a Likert scale, and analysis was conducted using comparison tests through specialized data processing software. The results revealed significant differences in students' cognitive psychology, mathematical processing skills, and responses to mathematics learning between the two schools. These variations suggest underlying factors related to the learning environment, teaching strategies, or institutional differences that influence student performance and engagement in mathematics. What sets this study apart is its comprehensive comparison of cognitive and skill-based factors in mathematics learning across different school settings. Unlike previous research that examines these variables in isolation, this study integrates cognitive psychology and mathematical processing skills with student feedback, providing a holistic view of the learning experience. Doing so offers more profound insights into how school-specific conditions can shape students’ cognitive and mathematical abilities, thereby informing more tailored educational strategies to improve mathematics learning outcomes across diverse educational contexts.

ASSEMBLER EDU E-MODULES: IMPROVING ARGUMENTATION SKILLS, PERSEVERANCE, AND CURIOSITY IN PHYSICS LEARNING

Astalini Astalini — 2024-10-21

This study examined the impact of Assembler Edu-based e-modules on enhancing students' argumentation skills, perseverance, and curiosity in high school physics learning. Employing a quantitative ex-post facto design, the research utilized a 4-point Likert scale questionnaire to gather data. Descriptive and inferential statistical methods, including simple linear regression, were used for analysis. The findings reveal that using Assembler Edu-based e-modules significantly positively affected students' argumentation skills, with a 42.2% influence. Moreover, student responses indicated a 47.6% improvement in perseverance, while curiosity in learning renewable energy topics increased by 47.4%. These results highlight the considerable potential of integrating digital tools like Assembler Edu into the learning environment. This study introduces an innovative approach to physics education by incorporating interactive e-modules based on the Assembler Edu platform. Unlike traditional learning materials, these e-modules foster a more dynamic and engaging learning experience, enhancing students' critical thinking, perseverance, and curiosity. The research adds a fresh dimension to the existing literature by demonstrating how digital technology in science education can significantly improve core skills necessary for 21st-century learners. The study's implications suggest that integrating Assembler Edu-based e-modules into the school curriculum can enrich students' learning experiences, promote a deeper understanding of complex topics like renewable energy, and cultivate essential skills such as argumentation and perseverance, which are vital for success in both academic and real-world contexts.

DEVELOPMENT AND IMPLEMENTATION OF A FIVE-TIER DIAGNOSTIC TEST TO IDENTIFY STUDENT MISCONCEPTIONS ON FRACTIONS: A SIGNIFICANT STEP TOWARDS IMPROVING MATHEMATICS EDUCATION

Herliana Herliana — 2024-10-25

Misconceptions in fractions, a significant challenge in mathematics education, occur when students' conceptions do not align with scientific conceptions, yet they firmly believe in the correctness of their conceptions. These deeply rooted misconceptions are difficult to eliminate and not easily identified with conventional instruments. Therefore, this study aims to develop a valid and reliable five-tier diagnostic test instrument to identify students' misconceptions about fractions effectively. The design of this development research involves qualitative and quantitative methods using the 4D model (define, design, develop, disseminate). Sixty-eight seventh-grade students were involved in quantitative instrument quality testing; data were analyzed using SPSS. The final result of the development of this instrument was 12 questions declared valid and reliable. Furthermore, the instrument was applied to 30 seventh-grade students in different schools to identify their misconceptions about fractions. Based on the analysis of the combination of students' answers, it was obtained that 36.4% of students understood the concept, 8.9% were in the false positive category, 7.2% were false negative, 9.7% lacked knowledge, and 37.8% had misconceptions. It was found that the most dominant description of misconceptions (33%), namely, students assume that the smaller the denominator of a fraction, the smaller the value of the fraction. Then, it was also found that students' thinking, in general, causes misconceptions to occur. The practical implication of the results of this study is that teachers can use the instrument and identify misconceptions to develop more effective teaching methods, thereby improving mathematics education by helping students avoid and anticipate misconceptions.

SCALE FOR PRE-SERVICE SCIENCE TEACHERS’ KNOWLEDGE IN DESIGNING INCLUSIVE LEARNING: CONSTRUCTION, VALIDATION, AND RELIABILITY

Jamil Suprihatiningrum — 2024-10-28

Scale development is essential in testing constructs and variables in social research, including education. Scales that meet the rules of validity and reliability are developed using standardized procedures. This research focuses on scale production to create reliable and valid construct measures to assess teacher knowledge in designing inclusive learning, using three frameworks, namely Technological Pedagogical and Content Knowledge, Backward Design, and Universal Design for Learning. Research on this issue involves measuring organizational and psychological constructs, which present unique challenges because they are generally unobservable, very abstract, often complex, and may consist of several different components. As a result of this complexity, developing this scale was challenging. The production of this scale followed Boateng et al., and the field test involved 259 pre-service teachers. The Rasch Rating Scale Model analysis was used to test the scale and produced 18 items that have high reliability (0.96). This scale can be used nationally to assess teachers' ability to implement inclusive education. The scale also applies as a teacher self-assessment tool for developing their competence.

INTEGRATING INTERTEXTUAL AND COMMUNITY RESPONSE ANALYTICAL LENSES TO TURN SYAIR CENDAWAN PUTIH’S A CHILDREN'S STORY “THE ADVENTURE OF CENDAWAN PUTIH” INTO A SCIENCE LEARNING

Nurhayati Nurhayati — 2024-11-09

This study explores the transformation of the Cendawan Putih poem into prose suitable for children's science learning, examining the adaptation process and public perception. Using a qualitative approach, the study employs intertextual theory to analyze textual transformations such as expansion, conversion, modification, and citation and community response theory to capture stakeholders’ views. The research investigates how the Cendawan Putih poem has been creatively adapted into the children’s story The Adventure of Cendawan Putih to enhance science learning while maintaining cultural and educational value. The findings reveal that adapting Cendawan Putih for children’s literature not only preserves core narrative elements but also integrates science concepts relevant to young readers. The adaptation utilizes techniques such as expansion and modification, enriching the original storyline to highlight nature, ecosystems, and environmental science in an accessible format for children. Public feedback, collected through Focus Group Discussions (FGDs) with cultural figures, educators, academics, and youth, shows broad support for this transformation. Stakeholders emphasize the story’s potential to foster cultural awareness and introduce foundational science concepts, making it an innovative tool for learning. This study highlights how traditional literary forms can be reimagined for children’s science learning, merging cultural heritage with modern educational needs. By presenting Cendawan Putih in a format that supports both cultural preservation and science education, this work positions the story as an intergenerational bridge and a valuable addition to literature-based science learning. It underscores the importance of community involvement in literary adaptation, providing new insights for literature, cultural education, and science pedagogy.