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All Journal Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis JITK (Jurnal Ilmu Pengetahuan dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vygotsky: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Pendidikan Citra Bakti JURNAL AXIOMA : Jurnal Matematika dan Pembelajaran Akademika Discovery : Jurnal Ilmu Pengetahuan BERNAS: Jurnal Pengabdian Kepada Masyarakat E-Jurnal Matematika
M. Ulul Albab
Universitas Islam Lamongan
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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MACHINE LEARNING TO IDENTIFY ELIGIBILITY OF STUDENTS RECEIVING SINGLE TUITION RELIEF M. Ghofar Rohman; Zubaile Abdullah; Shahreen Kasim; M Ulul Albab
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7294

Abstract

The cost of higher education in Indonesia varies greatly and often becomes a financial burden for students. Socio-economic factors such as parental income, occupation, number of dependents, vehicle ownership, and place of residence influence the determination of single tuition as regulated by the Ministry of Education Regulation No. 55 of 2013. This study aims to classify freshmen eligibility for single tuition relief using five machine learning models: RF, LR, KNN, SVM, and NB. The dataset contains 2000 rows of data with six socio-economic attributes divided into two classes: eligible and ineligible. The data were split into 80% training and 20% testing, and model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Results show that without SMOTE, all models suffer from severe majority-class bias, yielding critically low recall for the minority class  SVM = 0.014; NB = 0.004. SMOTE significantly improves minority-class detection, with RF and SVM achieving the highest performance F1-scores of 0.820 and 0.801, and ROC-AUC of 0.966 and 0.990, respectively. SHAP analysis identifies Number of Dependents of Parents as the most influential predictor across all models, highlighting its central role in financial need assessment. These findings demonstrate that combining SMOTE with ensemble or margin-based models enhances classifiying  fairness and sensitivity in educational support systems. The future work recommend expanding features to include behavioral, academic, and regional indicators, using multi-institutional data, and exploring deep learning or advanced resampling methods to enhance generalizability and robustness
ANALISIS RISIKO PERDAGANGAN BAWANG MERAH DENGAN VOLATILITAS HISTORIS DAN VALUE AT RISK YAN ADITYA PRADANA; LENNY PUSPITA DEWI; PUTRI BALQIS AL KUBRO; SATRIYO PRIYO HANDOKO; MUHAMMAD ULUL ALBAB; FRANSISKUS FIDO EKA KURNIA NUGRAHA
E-Jurnal Matematika Vol. 15 No. 3 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i03.p511

Abstract

Shallots are one of Indonesia's most important horticultural commodities, however their prices are highly volatile, creating significant financial risks for farmers and traders. This study aims to quantify the price risk of shallot trading in East Java Province and Nganjuk Regency using historical volatility, Value at Risk (VaR), and Conditional Value at Risk (CVaR) based on rolling windows of 10, 20, &40 days. The log-return series were found to be non-normally distributed (Shapiro–Wilk, p<0.05) but stationary (ADF, p<0.05), supporting the use of the historical simulation approach. The results show that Nganjuk exhibits substantially higher price volatility than East Java, particularly over the 10-day rolling window. Consistent with this finding, the 95% VaR indicates that Nganjuk has a higher potential short-term loss than East Java (-0.0133 compared with-0.0065). Furthermore, CVaR provides a more conservative estimate of downside risk, indicating that the expected loss beyond the VaR threshold in Nganjuk is considerably larger than that estimated by VaR alone. These findings demonstrate that combining historical volatility, VaR, &CVaR provides a more comprehensive assessment of extreme price risk than volatility analysis alone, thereby extending the application of quantitative risk measurement to agricultural commodity trading and supporting risk management and policy decisions.
Co-Authors Adinda Talia Salsabilah Ahdi, Mohammad Wafiyul Al-Kubro, Putri Balqis Anam, M. Khairul Ariska, Mega Aseti Prafianti, Rayinda Dian Novi Ambarwati Elly Anjarsari Febriyanti, Rahma FRANSISKUS FIDO EKA KURNIA NUGRAHA Ilmayasinta, Nur Ja’far Shodiq Laksono Trisnantoro Lenny Puspita Dewi M. Ghofar Rohman Marissa Dewi Fatimah Novitasari Novitasari Oktaviana, Dwi Livia Prafianti, Rayinda Aseti Prafianti, Rayinda Aseti Prihantini, Campina Illa Rahma Febriyanti Rahma Febriyanti SATRIYO PRIYO HANDOKO Setyana, Sukma Febri Shahreen Kasim Silvia, Ifroha Anita Sriwijayati, Anissa Tatag Yuli Eko Siswono Tri Dyah Prastiti Ulil Albab Yan Aditya Pradana Zubaile Abdullah