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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) International Research on Big-data and Computer Technology (IRobot)
Ritham Tuntun
STMIK Multicom
Computer Science & IT Control & Systems Engineering

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Perancangan Sistem Informasi Manajemen dan Keuangan Masjid Al-Mukhlisin Berbasis Web dan Mobile Mihuandayani Mihuandayani; Arif Setiawan; Ritham Tuntun; Virginia Tamuntuan
International Research on Big-Data and Computer Technology: I-Robot Vol 10, No 1 (2026): April
Publisher : UNIVERSITAS DHARMA WACANA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/ir.v10i1.903

Abstract

Pengelolaan administrasi, keuangan, dan kegiatan di Masjid Al-Mukhlisin Kelurahan Molinow masih dilakukan secara manual sehingga berpotensi menyebabkan kehilangan data, keterlambatan penyusunan laporan keuangan, serta kurang optimalnya penyampaian informasi kepada pengurus dan jamaah. Penelitian ini bertujuan merancang sistem informasi manajemen dan keuangan masjid berbasis web dan mobile yang dapat menjadi acuan dalam pengembangan sistem pada tahap selanjutnya. Metode yang digunakan adalah Prototype dengan tahapan pengumpulan kebutuhan melalui observasi, wawancara, studi pustaka, perancangan sistem, dan evaluasi rancangan bersama pengguna. Hasil penelitian berupa rancangan sistem yang meliputi pemodelan proses bisnis, perancangan basis data, antarmuka pengguna, serta rancangan fitur pengelolaan keuangan, kegiatan, inventaris, donasi digital, dan penjadwalan petugas salat Jumat. Rancangan yang dihasilkan diharapkan dapat menjadi pedoman implementasi sistem sehingga mendukung pengelolaan masjid yang lebih efektif, terstruktur, dan transparan. 
Learning Analytics for STEM Course-Completion: Logistic Regression, ANN, and DNN Mihuandayani Mihuandayani; Michel Farrel Tomatala; Ritham Tuntun; Caroline Belinda Tangka
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 4 (2026): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.125791

Abstract

Identifying students at risk of not completing STEM courses is essential for timely academic intervention, yet institutional learning data remain underutilized. This study develops a comparative learning analytics framework for classifying STEM course completion using Logistic Regression, a Shallow Artificial Neural Network (ANN), and a Deep Neural Network (DNN). The dataset contained 1,800 records from 300 students in the 2022 and 2023 cohorts, covering six STEM courses and 14 predictors. Data were stratified into training, validation, and testing subsets of 70%, 15%, and 15%, with random oversampling applied only to the training set. Performance was evaluated using accuracy, precision, recall, F1-score, AUC, and confusion matrices, with non-completion as the positive class. Logistic Regression achieved 97.78% accuracy, 94.57% precision, 98.86% recall, 96.67% F1-score, and 99.93% AUC. Shallow ANN achieved 97.41% accuracy and 96.09% F1-score, while DNN achieved 97.78% accuracy, 95.56% precision, and 96.63% F1-score. Final-exam, midterm, assignment, and quiz scores were the most influential predictors. These findings indicate that Logistic Regression provides the strongest overall balance between predictive performance and interpretability for STEM learning analytics.
Co-Authors Arif Setiawan Caroline Belinda Tangka Michel Farrel Tomatala Mihuandayani Mihuandayani Virginia Tamuntuan