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All Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal Teknologi Dan Sistem Informasi Bisnis JTIK (Jurnal Teknik Informatika Kaputama) TIN: TERAPAN INFORMATIKA NUSANTARA Majalah Ilmiah UPI YPTK Prosiding Konferensi Nasional PKM-CSR Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Informatika el hisbah: Journal of Islamic Economic Law
Faradika, Faradika
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Aplikasi Sistem Informasi Geografis Pemetaan Persebaran Alumni Universitas Dharma Andalas Berbasis Web Maidiansyah, Eko; Sularno, Sularno; Faradika, Faradika
Jurnal Sains dan Teknologi (JSIT) Vol. 1 No. 1 (2021): Jurnal Sains dan Teknologi (JSIT)
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (352.441 KB) | DOI: 10.47233/jsit.v1i1.14

Abstract

Penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi Alumni Berbasis GIS yang dapat digunakan untuk pendataan Alumni, wadah komunikasi antar Alumni, wadah untuk mencari pekerjaan, serta pemetaan persebaran kerja Alumni yang dapat dijadikan sebagai sarana yang dapat membantu dalam proses akreditasi program studi Universitas Dharma Andalas. Pembuatan perangkat lunak dalam penelitian ini, Penulis menggunakan bahasa pemrograman PHP dan MySQL sebagai basis data. Hasil akhir dari penelitian ini adalah dihasilkan program aplikasi berbasis GIS yang diharap dapat membantu pihak program studi dan Alumni memperoleh informasi terkait Alumni di Univesitas Dharma Andalas.
Pengembangan Sistem Rekomendasi Program Studi Multikelas Menggunakan Algoritma Random Forest Astri, Renita; Kamal, Ahmad; Zulfahmi, Zulfahmi; Faradika, Faradika
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8369

Abstract

Choosing a major is a crucial decision for prospective students entering higher education. An inappropriate choice may lead to low learning motivation, poor academic performance, and career mismatches. This study aims to develop a multiclass majors recommendation system based on machine learning using the Random Forest algorithm. The dataset consists of 855 student and alumni records from 10 majors at Dharma Andalas University (UNIDHA), including academic attributes (subject grades, GPA, entrance test results) and non-academic attributes (gender, high school major, interest, and alumni career field). The model was trained using an 80:20 train-test ratio and evaluated using accuracy, precision, recall, F1-score, and macro-average AUC. The results show that the Random Forest outperforms Decision Tree, K-Nearest Neighbor, and Naive Bayes, achieving an accuracy of 0.920 and AUC of 0.972. These findings demonstrate that ensemble-based algorithms are highly effective for multiclass recommendation problems and can serve as a foundation for academic and career guidance systems in universities.
Co-Authors Ahmad Kamal ahmad kamal, ahmad Analiansyah Anggraini, Putri Bawarta, Westi Chandra, Cantika Damayanti, Elok Enny Arita Ingra Sovita Maidiansyah, Eko Mulya, Dio Prima Randi Yudha Pratama Renita Astri Rina Desiana Sari, Intan Utna Septia Wulandari Sularno Sularno, Sularno Zulfahmi Fahmi Zulfahmi Zulfahmi