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All Journal Jurnal Pendidikan Informatika dan Sains Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika ECOPLAN : JOURNAL OF ECONOMICS AND DEVELOPMENT STUDIES Jurnal Inovasi Penelitian Journal of Computer Networks, Architecture and High Performance Computing Akrab Juara : Jurnal Ilmu-ilmu Sosial JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) ProBisnis : Jurnal Manajemen ABDINE Jurnal Pengabdian Masyarakat
Hasta Herlan Asymar
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Medicine & Pharmacology Physics Social Sciences Other

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ANALISIS PERBANDINGAN ALGORITMA RANDOM FOREST DAN NAÏVE BAYES DALAM KLASIFIKASI PENYAKIT AIDS Saprudin, Muhamad Tedi; Sudrajat, Budi; Asymar, Hasta Herlan
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 6, No 1 (2026): JEIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol6no1.1002

Abstract

This study aims to compare the performance of the Random Forest and Naïve Bayes algorithms in classifying AIDS based on patient data. The dataset was obtained from the Kaggle platform and consists of 2,139 data points with 23 attributes, including medical information such as age, weight, haemoglobin level, gender, treatment type, and CD4 and CD8 cell counts. The research method comprises several stages: data collection, pre-processing, model building, and evaluation. The model was evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC. The results showed that the Random Forest algorithm outperformed Naïve Bayes. Random Forest achieved an accuracy of 88.16% and an AUC of 91.25%, while Naïve Bayes only achieved an accuracy of 81.46% and an AUC of 81.40%. These findings indicate that Random Forest is better able to handle complex, non-linear medical data and provides more stable, reliable classification results. Therefore, Random Forest is recommended as a more effective algorithm for classifying AIDS cases based on the dataset used in this study.Penelitian ini bertujuan untuk membandingkan kinerja algoritma Random Forest dan Naïve Bayes dalam melakukan klasifikasi penyakit AIDS berdasarkan data pasien. Dataset yang digunakan diperoleh dari platform Kaggle dan terdiri atas 2.139 data dengan 23 atribut, yang mencakup informasi medis seperti usia, berat badan, kadar hemoglobin, jenis kelamin, jenis pengobatan, serta jumlah sel CD4 dan CD8. Metode penelitian meliputi beberapa tahapan, yaitu pengumpulan data, pra-pemrosesan, pembentukan model, dan evaluasi. Model dievaluasi menggunakan metrik akurasi, presisi, recall, F1-score, confusion matrix, dan AUC- ROC. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa lebih baik dibandingkan Naïve Bayes. Random Forest mencapai akurasi sebesar 88,16% dan nilai AUC sebesar 91,25%, sedangkan Naïve Bayes hanya memperoleh akurasi 81,46% dengan AUC 81,40%. Temuan ini menunjukkan bahwa Random Forest lebih mampu menangani data medis yang kompleks dan non-linear, serta memberikan hasil klasifikasi yang lebih stabil dan andal. Oleh karena itu, Random Forest direkomendasikan sebagai algoritma yang lebih efektif untuk klasifikasi kasus AIDS menggunakan dataset pada penelitian ini.
Co-Authors Ade Mahmud Ajeng Puji Lestari Budi Sudrajat Dimas Muslim Setiawan Dirgahayu Erri Eka Putri Handayani Esron Rikardo Nainggolan Esron Rikardo Nainggolan Fahlepi Roma Doni Herlina Ferliyanti Hidayah, Said Ita Nurmayanti Lase, Mareanus Losa Larasati Mareanus Lase Muhammad Darrusalam Muhammad Rifal Pratama Putra Muhammad, Agit Nafi Ramadhani Nainggolan, Esron Rikardo Nur Ali Farabi Putra, Muhammad Rifal Pratama Rachmat Hidayat Rachmat Hidayat Resti Yulistria Said Hidayah Saprudin, Muhamad Tedi Setiawan, Dimas Muslim Tatu Zakiyatun Nufus Uthami, I Gusti Ayu Agung Dewi Kurnia Zumantara, Zidan Januri