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All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) EQIEN - JURNAL EKONOMI DAN BISNIS INTERACTION: Jurnal Pendidikan Bahasa Jurnal Infortech Computer Science (CO-SCIENCE) Jurnal Info Sains : Informatika dan Sains SPEED - Sentra Penelitian Engineering dan Edukasi Informatics and Computer Engineering Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Artificial Intelligence and Digital Business Journal of Accounting Information System Journal of Informatics, Electrical and Electronics Engineering INTERACTION: Jurnal Pendidikan Bahasa Indonesian Community Service Journal of Computer Science (IndoComs)
Robi Aziz Zuama
Universitas Bina Sarana Informatika
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Analysis of Student Academic Performance Using Random Forest and Support Vector Machines Galih Mifta Agung; Robi Aziz Zuama; Eko Setia Budi
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10123

Abstract

Assessing student academic performance objectively remains a challenge at SMP Negeri 16 Bogor due to diverse internal and external factors in student records. This study aims to compare the classification performance of the Random Forest and Support Vector Machine (SVM) algorithms using a dataset of 403 students containing demographic, socioeconomic, and school-related attributes. Although the attributes are not traditional academic indicators (e.g., assignment or exam scores), they are used to explore whether non-academic features can contribute to predictive models. Following data preprocessing—handling missing values, encoding categorical variables, and managing class imbalance—both algorithms were evaluated using accuracy, precision, recall, and confusion matrix analysis. Results show that SVM outperforms Random Forest with 78.00% accuracy, 89.98% precision, and 70.24% recall. These findings indicate that SVM is more robust for imbalanced classification tasks and can provide useful insights even when academic-performance labels are predicted from non-academic attributes.
Analisis Perbandingan Algoritma Artificial Neural Network, Logistic Regression, dan Random Forest Menggunakan Fitur SqueezeNet untuk Klasifikasi Kematangan Buah Jeruk Nipis Robi Aziz Zuama; Arina Selawati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12732

Abstract

Kematangan buah jeruk nipis merupakan faktor penting yang memengaruhi kualitas dan nilai jual produk. Penentuan tingkat kematangan secara manual masih bergantung pada pengamatan visual sehingga berpotensi menimbulkan subjektivitas dan ketidakkonsistenan. Penelitian ini bertujuan untuk menganalisis kinerja algoritma Artificial Neural Network (ANN), Logistic Regression (LR), dan Random Forest (RF) dalam mengklasifikasikan tingkat kematangan buah jeruk nipis menggunakan fitur hasil ekstraksi SqueezeNet. Dataset yang digunakan terdiri dari 104 citra buah jeruk nipis yang terbagi secara seimbang menjadi dua kelas, yaitu 52 citra buah matang dan 52 citra buah mentah. Seluruh citra diproses melalui tahap cropping dan resize menjadi 227 × 227 piksel, kemudian dilakukan ekstraksi fitur menggunakan model SqueezeNet pada aplikasi Orange Data Mining. Evaluasi model dilakukan menggunakan metode 10-Fold Cross Validation dengan metrik Accuracy, Precision, Recall, F1-Score, Area Under Curve (AUC), dan Confusion Matrix. Hasil penelitian menunjukkan bahwa Artificial Neural Network memberikan performa terbaik dengan nilai Accuracy sebesar 95,2%, Precision sebesar 95,3%, Recall sebesar 95,2%, F1-Score sebesar 95,2%, dan AUC sebesar 0,997. Logistic Regression memperoleh akurasi sebesar 94,2%, sedangkan Random Forest sebesar 87,5%. Hasil tersebut menunjukkan bahwa kombinasi SqueezeNet sebagai metode ekstraksi fitur dan Artificial Neural Network sebagai algoritma klasifikasi mampu memberikan performa terbaik dalam mengidentifikasi tingkat kematangan buah jeruk nipis.
Co-Authors Abdul Latif Matihudin Achmad Baroqah Pohan Achmad Rifai Aditia Darmawan Ahmad Fauzi Amin Nur Rais Arina Selawati Arkan, Faiz Astuti, Rachmawati Darma Biktra Rudiarto Deni Gunawan Deni Gunawan Eka Herdit Juningsih Eko Setia Budi Fajar Akbar Galih Mifta Agung Gunawan, Deni Hamdun Sulaiman Mareanus Lase Mohammad Syamsul Azis Muhamad Abdul Ghani Nurul Ichsan Rachmawati Darma Astuti Ramdhan Saepul Rohman, Ramdhan Saepul Razaq, Sultan Nur Hadi Setia Budi, Eko Sobari, Irwan Agus Sofian Wira Hadi Syaifur Rahmatullah Syaifur Rahmatullah Syaifur Rahmatullah Veithzal Rivai Zainal Yuliani, Yuri Yuri Rahayu