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All Journal Indonesian Journal of Data and Science International Journal of Artificial Intelligence in Medical Issues Solidarity : Journal of Education, Social dan Culture Jurnal Pendidikan Guru Profesional
Sumiyatun
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Health Professions Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Implementation of Support Vector Machine Algorithm for Classification of Study Period and Graduation Predicate of Students Sumiyatun; Cahyadi, Yagus; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.214

Abstract

Introduction: Accurately predicting the duration of study and graduation predicates in higher education is essential for improving academic outcomes and decision-making. This study aims to classify students’ study period and graduation predicates in the Information Systems program at UTDI using the Support Vector Machine (SVM) algorithm. Methods: A dataset of 500 student records containing academic and demographic variables—including GPA, age, semesters, and graduation predicates—was processed through data cleaning, normalization, and feature selection. Study duration was categorized into three classes: short (≤4 years), medium (4–6 years), and long (>6 years). An SVM with a linear kernel was applied, and the model was evaluated using accuracy, precision, recall, and F1-score. Results: The SVM model achieved perfect classification for study duration, with 100% accuracy, precision, recall, and F1-score across all categories. For graduation predicate classification, the model attained 95.18% accuracy. While it performed well overall, it faced some difficulty distinguishing between "Cum Laude" and "Very Satisfactory" due to overlapping GPA ranges. The analysis identified GPA as the most influential feature in both classification tasks, while age and the number of semesters played supporting roles. Conclusions: The SVM model demonstrates strong capability in classifying study duration and graduation predicates, offering valuable insights for academic management. Although performance was high, especially for study period prediction, further refinement is suggested to enhance classification in overlapping categories. Future work may benefit from larger, more balanced datasets and exploration of advanced models to increase prediction reliability.
Penerapan model PJBL untuk meningkatkan numerasi materi bangun ruang pada siswa kelas IV SDN Peterongan Novita Estyawati; Fine Reffiane; Sumiyatun
Jurnal Pendidikan Guru Profesional Vol. 3 No. 1 (2025): JURNAL PENDIDIKAN GURU PROFESIONAL
Publisher : Pascasarjana Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/vcb0zn24

Abstract

This research aims to improve the numeracy skills of class IV students at SDN Peterongan on spatial building material through the application of the Project Based Learn ing Model. The research method used is Classroom Action Research (PTK) involving collaboration between researchers and teachers as research partners. At the planning stage, researchers designa learning plan that includes learning objectives, material to be taught, activities to be carried out, and evaluations to be carried out. The Project Based Learning model is used as a student-centered learning approach through providing projects at the beginning of learning. The implementation of the Project Based Lea rning Model is carried out in 3 cycles. Observations are carried out to observe students' interactions, their involvement in learning, and changes in their understanding of comparison. Students' numeracy abilities are evaluated through evaluation scores. The research results show an increase in numeracy skills after implementing the Project Based Learning model. Learners show more active involvement in the project and there is interest. They also demonstrated a better understanding of spatial material, which was reflected in increased evaluation scores. Based on these findings, it is recommended that teachers and schools consider using the Project Based Learning model in teaching spatial building material to class IV students. Collaboration between researchers and teachers is also important in developing effective learning methods. Further research can be carried out to involve more schools and classes in order to expand the scope of the research.
Proses Habituasi Disiplin Pada Santri TPQ Miftachussa’adah Desa Kalinanas Kecamatan Japah Kabupaten Blora Sumiyatun; Fajar
Solidarity: Journal of Education, Society and Culture Vol. 14 No. 1 (2025): SOLIDARITY
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/solidarity.v14i1.35763

Abstract

Lembaga Pendidikan Al-Qur’an TPQ Miftachussa’adah adalah sebuah TPQ yang berada di Desa Kalinanas Kecamatan Japah Kabupaten Blora Jawa Tengah sejak berdirinya berusaha menerapkan habituasi disiplin pada santri-santrinya demi menjaga generasi bangsa di tengah menurunya karakter di era digital. Penelitian ini bertujuan untuk mengetahui proses, bentuk, dan hasil habituasi disiplin santri TPQ Miftachussa’adah Desa Kalinanas Kecamatan Japah Kabupaten Blora. Penelitian ini menggunakan pendekatan kualitatif dengan metode penggalian data melalui observasi dan wawancara pada beberapa responden penelitian. Hasil penelitian menunjukkan bahwa proses habituasi disiplin yang dilakukan oleh para tutor TPQ Miftachussa’adah ditaati dengan baik meski masih terdapat beberapa santri yang kesulitan beradaptasi terhadap aturan disiplin tersebut, hal itu tergolong wajar karena beberapa santri tersebut masuk kategori santri baru. Bentuk habituasi disiplin yang diterapkan oleh para tutor diantaranya adalah penegakan aturan kehadiran santri dan sholat berjamaah serta tuntutan untuk menjalankan ajaran-ajaran akhlak di kehidupan sehari-hari. Hasil habituasi disiplin santri memuaskan dari para tutor, wali santri, dan masyarakat sekitar TPQ.
Machine Learning-Based Prediction of HIV/AIDS Infection and Treatment Effectiveness: A Clinical Dataset Analysis Jiwa Permana, Agus Aan; Wikranta Arsa, I Gusti Ngurah; Naswin, Ahmad; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.362

Abstract

The early and accurate prediction of HIV/AIDS infection is critical to improving clinical decision-making and ensuring effective patient management. This study presents a comprehensive machine learning-based approach to predict HIV/AIDS infection status and evaluate the effectiveness of antiretroviral treatments using a well-documented clinical dataset from 1996, comprising 2,139 patient records and 34 features. Through rigorous preprocessing, exploratory data analysis, and feature engineering, several new clinically relevant attributes were constructed, such as CD4/CD8 ratios and immunological change metrics. Four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting—were trained and evaluated. Among these, the Gradient Boosting classifier achieved the highest ROC-AUC score of 0.9335, while Random Forest provided strong predictive performance with a ROC-AUC of 0.9180 and was selected for further evaluation due to its model transparency. Key features influencing infection prediction included CD4+ and CD8+ dynamics, baseline immunological levels, and treatment history. Additionally, the study examined treatment effectiveness by analyzing CD4+ cell count responses across different therapy types. The combination of ZDV and ddI emerged as the most effective regimen, improving immune outcomes and lowering infection rates, while ZDV monotherapy showed the least favorable results. This work underscores the potential of machine learning as a clinical decision support tool in HIV/AIDS care and provides data-driven insights into treatment optimization. Future studies should incorporate longitudinal patient data and real-world clinical environments for broader applicability.
Co-Authors Agus Aan Jiwa Permana Edi Faizal Fajar Fine Reffiane I Gusti Ngurah Wikranta Arsa, I Gusti Ngurah Naswin, Ahmad Novita Estyawati Yagus Cahyadi