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Dendi Sunardi
Universitas Pamulang
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Evaluasi Mutual Information dalam Klasifikasi Dokumen Ilmiah Menggunakan Random Forest dan CBOW Mufidah Karimah; Dendi Sunardi; Ikhwan Fauzi
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/aqfp3679

Abstract

Scientific document classification in large-scale digital repositories requires an efficient process because manual categorization is time-consuming and may introduce inconsistencies. This study evaluates the performance of a Random Forest Classifier (RF) using Continuous Bag-of-Words (CBOW) for feature representation and Mutual Information (MI) for feature selection in scientific document classification. The dataset contains 5,560 titles and abstracts of nuclear-related scientific documents distributed across 10 categories. After data cleaning, 5,374 documents were used for experiments with an 80:20 training-test split. Experiments were conducted using CBOW vector dimensions of 100, 200, 300, 400, and 500 under two scenarios: RF+CBOW and RF+CBOW+MI. Performance was evaluated using precision, recall, F1-score, and accuracy. The RF+CBOW scenario achieved the highest accuracy of 73% at a vector dimension of 100, while the scenario with MI reached a maximum accuracy of 71% across several dimensions. Adding MI increased recall to 73% at dimension 100 but did not improve overall performance and reduced all reported macro metrics to 69% at dimension 300. These findings indicate that feature selection does not necessarily improve classification performance; dataset characteristics and representation dimensionality also influence model effectiveness.
Co-Authors Ayu Ernawati Ayu Lestari Bambang Suharjo Chairul Anwar Christien Rozali Dhea Amalia Putri Fitria Fakri Ikhwan Fauzi irsyad fauzan rabbani Leni Susanti M. Faizal M. Rizky Febriansyah Mufidah Karimah Muhammad Ichlasul Rais Nopi Dwi Lestari Rama Dina Ratna Sari Dewi Ruwirohi, Jan Everhard