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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Komtekinfo
Okmarizal, Bisma
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Metode AHP Dan Maut untuk Rekomendasi Produk Tupperware Terlaris Okmarizal, Bisma; Defit, Sarjon; Sumijan
Jurnal KomtekInfo Vol. 10 No. 3 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i3.430

Abstract

Untuk melakukan pemesanan barang tupperware pada unit simabur indah, sering terjadi ketidaksesuaian antara permintaan barang dengan barang yang di pesan sehingga terjadinya penumpukan barang dan kekosongan stok barang. Untuk meningkatkan efektifitas jual beli maka dibutuhkanlah sebuah sistem informasi untuk rekomendasi barang terlaris sebagai pendukung keputusan. Metode yang digunakan adalah AHP dan MAUT, pembuat keputusan adalah 5 star manager dan data yang digunakan adalah data katalog bulan maret 2023 sebanyak 20 data dan kriteria yang digunakan adalah 6 kriteria. Implementasi metode AHP dan MAUT menghasilkan A9, A20, A8, A13, A7 dengan nilai 0,916077, 0,842113, 0,831385, 0,768166, 0,768166 sebagai alternatif yang disarankan untuk pembelian di bulan berikutnya. Dengan membandingkan antara keputusan perusahaan didapatkanlah satu hasil rekomendasi yang berbeda sehingga akurasi didapatkan sebanyak 80%.
Prediksi Dropout Mahasiswa: Early-Warning Berbasis Enrollment dengan Machine Learning putra, Febri andika; Mirajdandi, Syahisro; Nandra; Okmarizal, Bisma; Mulyanda, Sandy
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10714

Abstract

Dropout among university students remains a major challenge in higher education because it affects study continuity, institutional performance, and the efficiency of academic service planning. This study develops a machine learning–based Early Warning System (EWS) that leverages data available at enrollment and is updated after the first semester. Using the public dataset “Predict Students’ Dropout and Academic Success” (n = 4,424), the original three-class outcome (Dropout, Enrolled, Graduate) is simplified into a binary target, with dropout treated as the positive class. Two feature scenarios are evaluated: (1) enrollment-only for pre-entry screening and (2) enrollment plus first-semester indicators to update risk scores. Three models are compared: class-balanced Logistic Regression, class-balanced Random Forest, and Gradient Boosting. Model performance is assessed using accuracy, precision/recall/F1score for the dropout class, balanced accuracy, and ROC-AUC. Under the enrollment-only setting, Logistic Regression achieves the best early-warning performance (recall = 0.697; F1 score = 0.651). After incorporating first-semester features, performance improves (recall = 0.792; F1score = 0.779). Beyond model comparison, this study adds an operational perspective through confusion-matrix simulation and probability-threshold analysis to balance missed at-risk cases and follow-up workload.
Co-Authors Mirajdandi, Syahisro Mulyanda, Sandy Nandra Putra, Febri Andika Sarjon Defit Sumijan