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All Journal Jurnal Teknologi Informasi dan Komunikasi Jurnal Pendidikan Sains dan Komputer
Mundirin, Mundirin
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Other

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SISTEM INFORMASI PERPUSTAKAAN (SIPUSTAKA) MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT (RAD) Mundirin, Mundirin; Adistira, Muhammad Dezan
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 15 No. 2 (2024): September
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v15i2.868

Abstract

Library Information System at SMKN 13 Jakarta will be built to overcome various existing problems, such as the lack of information providers, difficulties in accessing books and magazines and lack of ease in searching bookshelf data. So it is necessary to create a Digital Library information system. This library information system was built using the PHP programming language, Sublime Text, Bootstrap, Java Script, CSS and MySQL database. The development method used is the Rapid Application Development (RAD) method. The presence of this library information system is expected to make it easier for library staff, school principals and library members in managing data about the library, and also for members to make reading and borrowing books easier.
Model Prediktif Kelulusan Mahasiswa Berbasis Machine Learning Menggunakan Pipeline Terintegrasi dan Hyperparameter Tuning: A Machine Learning-Based Student Graduation Prediction Model Using an Integrated Pipeline and Hyperparameter Tuning Mundirin, Mundirin; Hedin, Deden; Idawati, Idawati; Latief, Ibrahim; Lili, Mohamad
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Artikel Riset, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.7795

Abstract

Delays in student completion are a critical issue in higher education because they impact academic efficiency, program accreditation, and graduate quality. This study aims to develop a machine-learning-based model for predicting student graduation using an integrated pipeline. This pipeline encompasses data processing, model building, and hyperparameter optimisation. The dataset was obtained from eight semesters of student academic data, totalling 146 credits. This dataset includes both numeric and categorical variables, such as GPA, number of credits passed per semester, study load, and student background characteristics. Preprocessing was performed using ColumnTransformer, which combined StandardScaler for numeric features and OneHotEncoder for categorical features. A classification model was developed using the Random Forest algorithm and optimised with GridSearchCV to identify the optimal hyperparameter settings. Model evaluation was performed using accuracy metrics, confusion matrices, and classification reports. The findings of this study indicate that the model achieves an accuracy of 81%, suggesting a strong ability to classify students as on-time or late graduates. Feature analysis shows that the average Grade Point Average (GPA), the number of Semester Credit Units completed each semester, and consistency in study load are the main factors influencing the timeliness of study completion. The implementation of an integrated channel has proven effective in maintaining preprocessing consistency and reducing the possibility of data leakage. The developed model can be implemented as an early warning system to support data-driven academic decision-making.
Co-Authors Adistira, Muhammad Dezan Hedin, Deden Idawati Idawati Latief, Ibrahim Lili, Mohamad