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All Journal JURIKOM (Jurnal Riset Komputer) bit-Tech KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Abdi Jurnal Publikasi Journal of Artificial Intelligence and Digital Business Jurnal Ilmu Komputer (JIK) OKTAL : Jurnal Ilmu Komputer dan Sains BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat
Ria Ester
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Social Sciences Other

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Perancangan dan Implementasi Aplikasi Penjadwalan Mengajar Guru Berbasis Web Menggunakan Metode R&D dan Agile Ria Ester; Yuliana
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 5 No 08 (2026): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Teacher scheduling is an important part of educational activity management. Scheduling that is still performed manually using Excel templates creates several problems, including a relatively long preparation time, potential scheduling conflicts, difficulty identifying teacher availability, and delays in adjusting schedules when changes or substitute teachers are required. This study aims to design and implement a web-based teacher scheduling application to improve the efficiency and effectiveness of schedule management. The research uses the Research and Development (R&D) method with an Agile Study approach. Data were collected through observation, interviews, and literature study. The system was developed using PHP, HTML, CSS, JavaScript, Bootstrap, and MySQL. The implementation produced a system capable of managing teacher data, class data, subjects, teaching schedules, teacher availability, substitute teachers, and schedule reports in PDF and Excel formats. Black Box Testing on the reported scenarios resulted in passed status, while White Box Testing was used to evaluate the program logic flow. User evaluation involving 20 respondents and 15 questions resulted in 271 Yes responses out of 300 responses, or 90.33%, placing the system in the Highly Feasible category. The results indicate that the application supports more structured schedule management and facilitates schedule adjustments and substitute teacher assignments.
Optimasi Hyperparameter XGBoost Berbasis Bayesian Optimization dan SHAP untuk Prediksi Dropout Mahasiswa Perguruan Tinggi Herwis Gultom; Ahmad Fauzi; Ria Ester
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 4 (2026): Agustus 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i4.10055

Abstract

Putus studi (dropout) mahasiswa merupakan masalah pendidikan tinggi yang merugikan secara finansial dan sosial, terutama karena proporsi kelas dropout yang tidak seimbang serta minimnya model prediksi yang transparan dan teruji stabil. Penelitian ini menerapkan algoritma Extreme Gradient Boosting (XGBoost) yang hyperparameter-nya dioptimasi melalui Bayesian Optimization berbasis Gaussian Process sebanyak 50 iterasi, dilengkapi ambang keputusan berbasis skor F2, yaitu metrik yang memberi bobot lebih besar pada recall dibandingkan precision, untuk mengatasi ketidakseimbangan kelas, serta interpretasi Shapley Additive exPlanations (SHAP) guna menjelaskan kontribusi tiap fitur terhadap prediksi model. Penelitian ini bertujuan mengembangkan kerangka kerja prediksi dropout yang akurat, transparan, dan tervalidasi secara statistik pada dataset 4.424 mahasiswa dengan 34 atribut akademik, demografis, dan sosioekonomi. Hasil sementara menunjukkan bahwa model XGBoost teroptimasi mencapai luas area di bawah kurva Receiver Operating Characteristic (ROC-AUC) sebesar 0,930 dan luas area di bawah kurva presisi-recall (PR-AUC) sebesar 0,904 pada data uji, dengan recall 90,49% pada ambang F2 optimal sebesar 0,2251. Validasi tambahan melalui nested cross-validation menghasilkan ROC-AUC rata-rata 0,924, yang mengonfirmasi stabilitas performa model. Interpretasi SHAP mengungkapkan bahwa jumlah mata kuliah yang lulus pada semester kedua merupakan prediktor paling dominan terhadap risiko dropout. Temuan ini menegaskan bahwa integrasi optimasi hyperparameter dan explainable AI mampu menghasilkan model prediksi dropout yang andal dan akuntabel bagi kebijakan intervensi akademik dini.
Co-Authors Agung Siswopranoto Ahmad Fauzi Aulia Ikhsan Dede Handayani Derysa Maulana Putra Setya Eka Sri Rahayu Galuh Saputri Herwis Gultom Iis Aisyah Intan Kumalasari Lisda Fitriana Masitoh Nasrul Hidayah Okta Irawati Raditia Vindua Roeslan Djutalov Salsa Sayida Bilqis4 Yuliana Yusniati Herni