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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Information System and Management
Hammad, Jehad
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Computer Science & IT Engineering Languange, Linguistic, Communication & Media

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Predictive Modeling of Student Dropout Using Academic Data and Machine Learning Techniques Aini, Qurrotul; Rahajeng, Elsy; Tiohandra, Mufadha; Pratama, Hamzah Aji; Hammad, Jehad
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46659

Abstract

This study's objective is to investigate the performance of a predictive model for students at risk of dropout (DO) by considering several internal criteria of an academic program. This research uses academic information from UIN Syarif Hidayatullah Jakarta and applies the C4.5, Naive Bayes Classification (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) to forecast which students might drop out. The data used consists of 714 student records from Department of Information Systems for the academic year 2010–2015 as training and 2018 as testing data. The research method refers to the SEMMA framework (Sample, Explore, Modify, Model, and Assess) to ensure systematic and accurate data processing. Meanwhile, the internal criteria used are the completed courses, the status of the internship report, and the final project proposal. According to the study's findings, the C4.5 and SVM algorithms get the best accuracy rates of 94.44%, while KNN and NBC come in second and third, respectively, with 93%. The results show that the C4.5 and SVM algorithms work well with academic data. This study provides a substantial contribution to the development of a prediction system for students at risk of dropping out, which can be integrated into data-based applications or dashboards. This solution is expected to help higher education institutions identify students who need further academic support. In addition, this research also opens up opportunities for the progress of more accurate forecasting models through the integration of additional variables such as behavioral or psychological data. With this data-driven approach, higher education institutions can enhance their efficiency in monitoring and preventing student dropouts, thereby supporting a vision of quality and sustainable education.
IoT-Based Water Quality Monitoring and Suitability Modeling for Smart Campuses Kurniawan, Fachrul; Aziza, Miladina Rizka; Hasanah, Novrindah Alvi; Putri, Fadia Irsania; Wibawa, Aji Prasetya; Hammad, Jehad; Yuhefizar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7360

Abstract

This study proposes an IoT-based water quality monitoring framework integrated with a continuous suitability modeling approach for smart campus applications. A total of 404 sensor observations were collected, including pH, turbidity, temperature, and Total Dissolved Solids (TDS). A continuous water suitability score ranging from 0 to 1 was constructed based on WHO drinking water standards, and Multiple Linear Regression was employed to model the relationship between water quality parameters and the suitability score. The main contribution of this study lies in the development of a lightweight analytical framework that combines continuous regression modeling with threshold-based classification to support real-time decision-making in resource-constrained environments. The dataset was divided into 90% training and 10% testing data. The results show that the proposed framework achieved a classification accuracy of 88.5% based on threshold mapping of regression outputs, with a misclassification rate of 11.5%. These findings demonstrate the effectiveness of integrating IoT-based monitoring with interpretable and computationally efficient analytical models for sustainable campus water management.
Co-Authors Aji Prasetya Wibawa Aziza, Miladina Rizka Elsy Rahajeng, Elsy Fachrul Kurniawan Novrindah Alvi Hasanah Pratama, Hamzah Aji Putri, Fadia Irsania Qurrotul Aini Tiohandra, Mufadha Yuhefizar Yuhefizar