Publication Search

Students who silmutaneously work and study often face dual responsibilities that require careful planning of their time between job duties and academic obligations. This scenario can influence academic perfomance, highlighting the importance of effective time management for optimal study outcomes. The present study investigates the influence of study time management on the academic perfomance of working student. A quantitative research method with a correlational design was employed, the study sampled 108 working the sample consisted of students selected via puposive sampling. Data were collected via an online questionnaire consisting of 12 items and acdemic achievment was measured using the responden semester grade point average (GPA). To ensure the instrument was reliable Cronbach’s alpha was calculated, resulting in a value of 0.607, indicating acceptable consistency. Analysis using sample linear regression in spss revealed that study time management significantly and positively affects academic perfomance (p=0.012<0.05). Study time management explains approximately 5.9% of the variance in academic achievement, as indicated by an (R²) of 0.059. whereas the remaining 94.1% is determined by other influences.

Hypertension is a non-communicable disease often referred to as a silent killer because it generally does not present noticeable symptoms. Several risk factors contribute to the development of hypertension, including high body mass index (BMI) and low levels of physical activity. However, data regarding the association between BMI, physical activity, and hypertension at Pakuan Baru Public Health Center, Jambi City, remain limited. Therefore, this study aimed to determine the relationship between body mass index and physical activity with hypertension among adults at Pakuan Baru Public Health Center, Jambi City. This study employed a quantitative approach with a cross-sectional design. The research was conducted at Pakuan Baru Public Health Center, Jambi City, from November 19 to December 6, 2025. The study population consisted of all adult patients with hypertension at Pakuan Baru Public Health Center, totaling 2,797 individuals. A sample of 93 respondents was selected using an accidental sampling technique. Data were collected using observation sheets through measurements of height and weight to calculate BMI, as well as the Global Physical Activity Questionnaire (GPAQ). Data analysis was performed using univariate and bivariate analyses with the chi-square statistical test. The univariate analysis showed that 27 respondents (29.0%) were classified as severely obese, 42 respondents (45.2%) had a low level of physical activity, and 28 respondents (30.1%) were categorized as having stage 2 hypertension at Pakuan Baru Public Health Center, Jambi City. The bivariate analysis revealed a significant relationship between body mass index and physical activity with blood pressure among patients with hypertension (p = 0.000). It is expected that healthcare providers at Pakuan Baru Public Health Center will continue to provide education regarding the importance of controlling body mass index and engaging in regular physical activity, as these factors contribute to increased blood pressure among patients with hypertension.

This study aims to predict student graduation at Universitas Nahdlatul Ulama (UNU) Lampung using academic data with the Decision Tree C4.5 algorithm in RapidMiner. This research is based on the problem that some students graduate late and academic data is not fully used for decision-making. The method used is a quantitative approach with an experimental design following the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data used consists of 389 student records from four study programs. The variables include GPA, semester GPA, gender, and study program, while graduation status is used as the target variable. The results show that GPA is the most important factor affecting graduation. Students with GPA ≤ 3.00 tend to graduate late. The model produced an accuracy of 85.34%, precision of 87.50%, and recall of 97.03%. Therefore, it can function as an early warning mechanism to support academic programs in increasing on-time graduation rates.

The determination of scholarship recipients is a very important process in supporting students’ educational success, particularly in providing fair opportunities for high-achieving students who require financial assistance. However, in practice, this process often faces various challenges, such as assessor subjectivity and uncertainty in evaluating the applied criteria. Therefore, a decision support system is needed to assist decision-making in an objective and measurable manner. This study aims to implement the Fuzzy Tsukamoto method as a decision support system for determining scholarship eligibility. The criteria used in this study include Grade Point Average (GPA) as an indicator of academic achievement and parents’ income as an indicator of students’ economic conditions. The Fuzzy Tsukamoto method was selected because it is capable of producing crisp output values based on predefined fuzzy rules. Student data were processed through several stages, namely fuzzification to transform input data into fuzzy values, inference using the minimum operator, and defuzzification using the weighted average method. The results of the study indicate that the application of the Fuzzy Tsukamoto method is able to generate more objective, consistent, and measurable decisions. Based on the calculation results, a scholarship eligibility score of 63.9 was obtained, which falls into the eligible category. Thus, the Fuzzy Tsukamoto method can be considered an effective alternative to support fair, systematic, and transparent decision-making in determining scholarship recipients.

Kebugaran jasmani berperan penting dalam menunjang prestasi belajar siswa dan dipengaruhi oleh aktivitas fisik serta status gizi. Aktivitas fisik yang kurang dapat menurunkan daya tahan tubuh, sedangkan gizi kurang maupun gizi lebih dapat memengaruhi tingkat kebugaran. Penelitian ini bertujuan untuk mengetahui hubungan aktivitas fisik dan status gizi dengan tingkat kebugaran jasmani pada siswa SMA Negeri 8 Maros. Penelitian kuantitatif dengan desain cross-sectional ini melibatkan 80 siswa yang dipilih secara purposive sampling. Aktivitas fisik diukur menggunakan kuesioner GPAQ (Global Physical Activity Questionnaire), status gizi dinilai berdasarkan Indeks Massa Tubuh/U (IMT/U), dan kebugaran jasmani dengan menggunakan beep test. Analisis data menggunakan uji korelasi Spearman Rank. Terdapat hubungan signifikan antara aktivitas fisik dan kebugaran jasmani (p = 0,013; r = 0,277) dengan arah positif lemah. Selain itu, status gizi juga berhubungan signifikan dengan kebugaran jasmani (p = 0,000; r = -0,405) dengan arah negatif sedang. Aktivitas fisik dan status gizi berhubungan dengan tingkat kebugaran jasmani siswa SMA Negeri 8 Maros.

Academic productivity is a vital indicator of student success in higher education, but it is often hampered by the complexity of tasks, transitions in the learning environment, and digital distractions that trigger procrastination. This phenomenon demands a high degree of adaptability so that students do not become trapped in physical and mental exhaustion due to unmanaged workloads. Therefore, this study aims to analyze in depth the causal relationship between time management skills and academic productivity, as well as investigate their role in mitigating academic stress levels in students. The research method applied is a literature review with a qualitative-descriptive approach. The research data was sourced from secondary data in the form of 21 reputable scientific articles (national and international journals) published between 2020 and 2025. The data analysis process was carried out through the stages of data reduction, synthesis of findings, and narrative conclusion drawing to systematically map the relationship between variables. The results and discussion of the study show that time management has a significant positive correlation with improved learning achievement. Specific indicators such as daily schedule planning, priority setting, and self-regulation have been empirically proven to increase task completion efficiency and Grade Point Average (GPA) achievement. Conversely, poor time management was identified as a major predictor of cognitive overload and exhaustion, especially among students with dense curricula such as engineering majors. This study concludes that mastery of time management is not merely a scheduling tool, but a fundamental cognitive strategy that functions as a coping mechanism to maintain mental health and achieve an optimal study-life balance.

Generation Z students face increasingly complex academic challenges within the competitive higher education ecosystem. The pressure to maintain academic performance, social expectations, and intense digital exposure contribute to rising mental health issues such as stress, anxiety, and burnout. This study aims to analyze the relationship between academic pressure and the psychological condition of Gen Z students using a quantitative-descriptive approach through an online survey. The research instrument adapted the Academic Stress Scale (ASS) and the Depression Anxiety Stress Scale (DASS-21). The results show that the majority of respondents experience significant academic pressure, which correlates positively with symptoms of stress and anxiety. These findings highlight the importance of educational system reform that not only emphasizes academic performance but also supports students' mental well-being. Higher education institutions are expected to provide safe spaces, reliable counseling services, and more empathetic teaching approaches in response to the mental health issues facing this generation.

The world of work is an environment related to the work we are currently in. In other words, it is a place where various individuals perform an activity. The quality of college graduates is not only seen in terms of high or good grades / GPA. There are many other considerations, where large companies see a potential possessed by the person concerned. The dataset in this study was taken from student respondents about the world of work. One way to classify the influence of competence on the world of work in machine learning is to use datasets as training data so that performance testing can be carried out with the right classification method. From the results of the tests carried out, it is concluded that the results of the comparison are different, which shows the accuracy value of KNN which is around 96%, while the results of the SVM accuracy tested are 98%, so that the accuracy of SVM is better than KNN.

This study aims to analyze the transformation of the educational pattern at the Indonesian Naval Academy (AAL) from a four-year to a three-year system and its impact on graduate competencies and operational readiness. This educational system change responds to budget efficiency needs and accelerated officer formation filling, yet raises questions about the new system's ability to maintain the quality of officer education. The research employs a mixed methods approach with a concurrent embedded design, involving 250 respondents comprising graduates from both educational systems, AAL instructors, and unit commanders receiving graduates. Quantitative data were collected through structured questionnaires and analyzed using descriptive and inferential statistics (independent sample t-test and regression), while qualitative data were obtained through in-depth interviews, observations, and document studies analyzed using thematic analysis. Results indicate no significant difference in overall Grade Point Average (GPA) between the two groups (p=0.470), but significant differences in competency patterns emerged: four-year system graduates excelled in theoretical competencies (p=0.001), while three-year system graduates excelled in practical competencies (p=0.003). Operational readiness evaluation shows that four-year system graduates obtained higher scores (82.5) compared to three-year system graduates (78.3), with significant differences (p=0.012), particularly in strategic leadership and complex decision-making aspects. Qualitative analysis identified five major themes: high learning time pressure, orientation shift from comprehensive to pragmatic, gaps in character formation and corps values, concerns about theoretical understanding depth, and need for more intensive post-graduation mentoring programs. This study concludes that educational time compression successfully increased operational efficiency but involved trade-offs in learning depth and professional maturity. To optimize the three-year educational system, a comprehensive approach is required, including curriculum revision with integrated learning methods, utilization of modern learning technology, strengthening of leadership development programs, and development of continuous professional mentoring systems. The recommendations of this research are expected to provide input for AAL and the Indonesian Navy in refining the officer education system to produce officers who are not only technically competent but also mature in leadership and strong in character.

Rheumatism in the elderly can cause chronic pain that affects the quality of sleep. One of the factors influencing the sleep pattern of elderly patients with rheumatism is the level of physical activity. Insufficient physical activity can worsen joint stiffness and increase sleep disturbances. This study aims to examine the effect of physical activity on sleep patterns in elderly rheumatism patients at the Sumber Pucung Elderly Posyandu, Malang Regency. This study is a quantitative study with an analytical correlational design using a cross-sectional approach. The population of this study consisted of all elderly rheumatism patients at the Sumber Pucung Elderly Posyandu, totaling 33 people. The sampling technique used was total sampling. The study was conducted from July 20 to 27, 2025. Data collection instruments used the Global Physical Activity Questionnaire (GPAQ) and the Pittsburgh Sleep Quality Index (PSQI) questionnaires. The results showed that most respondents (90.9%) had low physical activity and all respondents (100%) had poor sleep patterns. The simple linear regression test yielded a significance value of p = 0.479 (p > 0.05) and R Square = 0.016, indicating no significant effect of physical activity on sleep patterns. Based on these results, it is expected that elderly rheumatism patients can increase physical activity according to their abilities to help maintain sleep quality, and Posyandu officers can provide education and elderly exercise programs appropriate for rheumatism conditions.

The prevalence of social media addiction is continuously increasing worldwide, including in Indonesia. Instagram has become the most widely used platform among students aged 18–25 years. Although Instagram provides many benefits, spending too much time on the application can cause learning concentration problems, procrastination, and a decrease in Grade Point Average (GPA). The purpose of this study was to determine whether there is a correlation between Instagram social media addiction and GPA among students of the Diploma Three Nursing Program at Universitas Abulyatama. This study applied a cross-sectional quantitative correlational design. The sampling technique used proportional random sampling, involving 78 students from a total population of 362 students. The research instruments consisted of cumulative GPA data and The Instagram Addiction Scale (TIAS). The study was conducted at the Diploma Three Nursing Program of Universitas Abulyatama from April 26 to May 9, 2025. Data analysis was performed using bivariate analysis with the Chi-Square test. The results showed that the majority of respondents were in the moderate category of Instagram addiction (56 students, 71.8%), followed by the high category (22 students, 28.2%). Meanwhile, GPA distribution indicated that 35 students (44.9%) were in the very high category, 24 students (30.8%) in the high category, and 19 students (24.3%) in the moderate category. The bivariate analysis with the Chi-Square test obtained a p-value of 0.001 (≤ 0.05), indicating a significant relationship between Instagram addiction and GPA. In conclusion, there is a correlation between Instagram social media addiction and students’ academic performance. Although Instagram use is widespread, students are expected to develop good time management skills and prioritize academic responsibilities to minimize the negative impact of social media use.

This study aims to determine the effect of academic self-efficacy on academic performance in the 2021 intake of students at Padang State University. Academic self-efficacy is an individual's belief in their ability to complete academic tasks, which is believed to play a significant role in determining student academic achievement. This study used a quantitative approach with a survey method, involving 55 respondents as a sample. Data collection was carried out through a questionnaire using a Likert scale to measure students' academic self-efficacy levels, as well as data collection on their Grade Point Average (GPA) as an indicator of academic performance. Data analysis was carried out using the SPSS program, through several testing stages, namely instrument validity and reliability tests, and simple linear regression tests to determine the effect of independent variables on the dependent variable. The results of the analysis indicate a positive and significant influence between academic self-efficacy and academic performance. This is evidenced by the regression coefficient value of 0.009, with a calculated t-value of 15.278, which is greater than the t-table of 2.266, and a significance value of 0.006, which is less than 0.05. These findings indicate that the higher a student's level of confidence in their academic abilities, the better their academic achievement. Therefore, developing academic self-efficacy can be an important strategy for improving the quality of learning and student academic achievement.  

The selection of recipients of the Single Tuition Fee (UKT) assistance at universities must be carried out with the principles of fairness, openness, and efficiency to ensure that the assistance is distributed appropriately. However, the manual selection process is often subjective and prone to error. Therefore, this study developed a decision support system based on the Simple Additive Weighting (SAW) method to assist the selection process of students eligible for UKT assistance more systematically and accurately. The SAW method was chosen because of its ability to process data involving various criteria to produce objective decisions. In this study, the criteria used for the selection of UKT assistance recipients include several aspects, such as the Cumulative Grade Point Average (GPA), parental income, number of dependents, non-academic achievements, distance of the student's residence from campus, active student status, and involvement in student organizations. Data from each criterion is then processed through a normalization stage to ensure uniformity of values and weighting based on their level of importance. After the normalization and weighting process, the next step is to calculate the preference value for each student. The results of the implementation of this system show that the SAW method can accurately rank student priorities, with a student named Selly receiving the highest preference score, 0.673. This indicates that the student in question meets the selection criteria with the best score. This developed system can support the UKT assistance selection process to be fairer and more efficient. By using the SAW method, subjective and manual errors in decision-making can be reduced, resulting in more objective and accurate decisions. In addition, this system also allows universities to evaluate UKT assistance recipients more transparently and precisely, which ultimately can increase public trust in the management of educational assistance at universities.

The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor KNN, in predicting the accuracy of students' graduation times. Predictions are made based on academic attributes such as Grade Point Average GPA, number of credits that have been achieved, and Semester Grade Point Average IPS as input variables. The method used in this study is Knowledge Discovery in Database KDD which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: "graduated on time" and "not graduated on time". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76.08%, with a precision of 73.11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64.62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

This research aims to analyze optimal educational strategies for cadets of the Indonesian Navy Academy's Technical Corps Generation Z, considering their unique learning characteristics without compromising military values. Using a mixed-method approach with a sequential explanatory design, the research involved 180 cadets as quantitative respondents and 26 participants for the qualitative phase (15 cadets, 8 instructors, 3 AAL leaders). Data were collected through structured questionnaires, in-depth interviews, focus group discussions, and participatory observations. Quantitative analysis used descriptive statistics, comparative tests, and multiple regression, while qualitative data were analyzed thematically. The results show that Generation Z cadets have a preference for visual learning (68%), interactive technology-based learning (M=4.1), and require instant feedback. Blended learning proved to be the most effective with an GPA of 3.45 and an engagement rate of 85%, compared to traditional learning (GPA=3.18, engagement=61%). Predictor factors of learning effectiveness include interactivity (β=0.412), technology use (β=0.298), periodic feedback (β=0.267), and content relevance (β=0.231). The research produced the "ADAPTIVE" learning model, which integrates modern technology with traditional military values. This model can enhance educational effectiveness while maintaining the essence of military training, providing both theoretical and practical contributions to the transformation of military education in the digital era.

This study aims to analyze the impact of recruiting Vocational High School (SMK) Mechanical Engineering graduates on the quality of education at the Naval Academy (AAL) Technical Corps. Using a mixed methods approach with quasi-experimental design, the research involved 160 Naval Academy cadets consisting of 80 cadets with SMK Mechanical Engineering background and 80 cadets with Senior High School Science background as control. Data were collected over 4 semesters through academic achievement instruments, technical competency tests, academic adaptation scales, and in-depth interviews. Results showed that although SMK cadets experienced initial academic adaptation challenges with lower GPA in the first semester (3.12 vs 3.28), this gap narrowed over time and even showed positive trends in the final semester (3.41 vs 3.38). SMK cadets demonstrated significant superiority in technical subjects such as Ship Propulsion Systems (85.2 vs 78.9) and Auxiliary Machinery (83.7 vs 76.4), particularly in practical components. In terms of social adaptation, the SMK group consistently showed higher scores throughout the research period. Learning style analysis revealed dominance of kinesthetic (47%) and visual (31%) preferences in the SMK group, different from the SHS group which preferred read/write (38%) and auditory (29%). These findings indicate that recruiting SMK Mechanical Engineering graduates has a positive impact on the quality of AAL education, particularly in applied technical competencies and social adaptation abilities. Educational background diversity can become a strategic strength if managed through appropriate bridging programs and diversification of learning methods that accommodate the diversity of cadet learning styles.

This study aims to apply the concept of probability in the assessment system to assess students' eligibility to receive scholarships. By applying a probabilistic approach, especially Bayes' Theorem, this study develops a more objective and data-based evaluation method. The method used is a quantitative approach with probabilistic analysis, allowing the calculation of students' chances of getting scholarships based on factors such as Cumulative Achievement Index (IPK) and Single Tuition Fee (UKT). The data analyzed includes primary data from students applying for scholarships as well as secondary data from historical records of previous scholarship acceptances. The results of the study show that GPA plays a significant role in increasing the chances of receiving a scholarship, where students with higher GPAs are more likely to be accepted. Conversely, students with high UKT tend to have lower chances, indicating that financial conditions are also a major factor in selection. This probability-based approach increases transparency and fairness in the selection system and reduces subjectivity in decision making. This study confirms that a probability-based scoring system can be a fairer and more accurate solution in assessing the eligibility of scholarship recipients. It is hoped that the results of this study can be a reference for educational institutions in developing a more data-based and objective selection system.

The popularity of futsal continues to increase among teenagers, but many players have not mastered basic skills and also lack physical abilities, which has an impact on playing skills on the field. This study aims to analyze the effect of circuit training modifications to improve futsal playing skills in futsal extracurricular students at SMA Negeri 19 Garut. The research method used was an experiment with a One group pre-test post-test design. The sample consisted of 20 students who were members of the SMAN 19 Garut futsal extracurricular. The instrument used was a futsal playing skills test using the General Performance Assessment Instrument (GPAI). The results showed an increase in the average general performance score with the results of the Asymp. (2-tailed) = 0.403 <α = 5% = 0.05 (significant). Based on these results, it can be concluded that circuit training modifications can improve futsal playing skills.

Self-esteem exerts a significant influence on an individual's confidence and adaptability, indirectly influencing students' academic success. This research seeks to examine the association between self-esteem levels and the Cumulative Grade Point Average (GPA) of students at the Faculty of Economic,  Sultan Agung Islamic University, Semarang. This study employs a quantitative approach with a cross-sectional design. The research sample consists of 185 respondents selected randomly using the simple random sampling method. Data were collected through the Rosenberg Self-Esteem Scale (RSES) questionnaire to measure self-esteem levels and students' latest GPA records. Data analysis was conducted using the Spearman correlation test. The findings of this investigation reveal a robust positive correlation between self-esteem levels and students' GPA (p = 0.000; r = 0.774). This suggests that elevated self-esteem is intrinsically linked to improved scholastic performance. This study is anticipated to serve as a foundational reference for support programs aimed at enhancing students' self-confidence. In conclusion, a profound and statistically significant association is evident between self-esteem and students' academic performance.