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All Journal Sistemasi: Jurnal Sistem Informasi ILKOM Jurnal Ilmiah
Herman, Suherman
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Computer Science & IT Electrical & Electronics Engineering

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PENGEMBANGAN SISTEM MEMBACA AL-QUR’AN DENGAN METODE MULTIMEDIA DEVELOPMENT LIFE CYCLE Herman, Suherman; Samsuni, Sunny; Fathurohman, Fathurohman
ILKOM Jurnal Ilmiah Vol 11, No 2 (2019)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (931.572 KB) | DOI: 10.33096/ilkom.v11i2.406.95-101

Abstract

Al-Qur'an is the Holy Book which is the main and first source in Islamic teachings. It is important for us as Muslims to learn about how to read the Qur'an properly. This research  uses two method approach namely Multimedia System Development method and learning method used is to use Tartil method. By using this two-method approach it is hoped that it will be more interesting for students to read and be able to accelerate how to read the Qur'an properly. The results of this research are in the form of an android-based application that can help to learn and read the Al-Qur?an properly. This application is tested using the Black Box method that shows all functions are running properly 100% according to what is expected.. The results show 85% progressed after using the application and only 15% did not progress.
Prediction of Unpaid Student Fees at Muhammadiyah Ahmad Dahlan University Cirebon using the Random Forest Algorithm Herman, Suherman; Kristomo, Domy
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5411

Abstract

This study aims to develop a predictive model for student fee payment arrears at Universitas Muhammadiyah Ahmad Dahlan Cirebon using the Random Forest algorithm. The dataset was obtained from the Academic Information System and consisted of 490 student records from four cohorts (2018–2021), which were divided into 80% training data and 20% testing data. The data processing stages included data cleaning, transformation, and feature selection using Recursive Feature Elimination (RFE). The model was optimized using GridSearchCV to obtain the best configuration. The evaluation results indicate strong performance, with an AUC of 0.980, accuracy of 88.8%, precision of 90.4%, recall of 88.8%, and an F1-score of 0.875. Feature importance analysis identified the amount of arrears variable as the most dominant factor influencing prediction outcomes. Strategic recommendations for university implementation include: (1) deploying a data-driven early warning system to identify at-risk students, (2) offering payment relief or installment programs for students with high arrears, and (3) conducting regular financial monitoring through a dashboard to support timely decision-making. Therefore, this study not only produces an effective predictive model but also provides practical solutions for improving university financial management.
Co-Authors Domy Kristomo, Domy Fathurohman Fathurohman, Fathurohman Samsuni, Sunny