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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Journal of Information Systems Engineering and Business Intelligence VANOS Journal of Mechanical Engineering Education Bulletin of Electrical Engineering and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JRST (Jurnal Riset Sains dan Teknologi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Swadaya: Indonesian Journal of Community Empowerment Prosiding Seminar Nasional Program Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Pengabdian Pada Masyarakat PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO semanTIK Jurnal Komtika (Komputasi dan Informatika)
Herman Yuliansyah
Universitas Ahmad Dahlan
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Model Klasifikasi Kesesuaian Artikel Pada Jurnal SINTA Berdasarkan Metadata Menggunakan Term Frequency–Inverse Document Frequency dan Naïve Bayes Ainunna’imah; Imam Riadi; Herman Yuliansyah
SemanTIK : Teknik Informasi Vol. 12 No. 1 (2026): Volume 12 Number 1 (January-june 2026)
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v12i1.270

Abstract

Pertumbuhan publikasi ilmiah di Indonesia menimbulkan kebutuhan akan metode yang efisien untuk membantu peneliti mengklasifikasikan dan menentukan jurnal yang sesuai bagi artikel akademik. Berbagai penelitian menunjukkan bahwa metode machine learning, khususnya Naïve Bayes, efektif dalam tugas klasifikasi teks berbahasa Indonesia. Namun, penelitian yang secara khusus memanfaatkan metadata artikel untuk menentukan kesesuaian artikel terhadap jurnal terindeks SINTA masih terbatas, khususnya terkait integrasi TF–IDF dan evaluasi berbasis cross-validation. Penelitian ini bertujuan mengembangkan model klasifikasi kesesuaian artikel pada jurnal SINTA berdasarkan metadata menggunakan Term Frequency–Inverse Document Frequency dan Naïve Bayes. Dataset terdiri atas 1.200 metadata artikel mencakup judul, abstrak, dan kata kunci, yang dikumpulkan melalui crawling manual terhadap jurnal-jurnal bidang teknologi pada portal SINTA. Tahapan penelitian meliputi pengumpulan data, prapemrosesan teks (case folding, translasi, tokenisasi, stopword removal, dan stemming), penggabungan metadata, ekstraksi fitur menggunakan TF–IDF, serta penerapan algoritma Naïve Bayes dengan skema 5-fold cross-validation. Evaluasi berdasarkan confusion matrix menunjukkan bahwa model mencapai accuracy 0,7058, precision 0,6977, recall 0,7133, dan F1-score 0,7065. Hasil ini menegaskan bahwa Naïve Bayes mampu memberikan performa klasifikasi yang cukup baik terhadap metadata artikel, serta berpotensi mendukung pengembangan sistem rekomendasi target submission jurnal The rapid growth of scientific publications in Indonesia has created the need for efficient methods to assist researchers in classifying and determining suitable journals for academic articles. Previous studies have shown that machine learning methods, particularly Naïve Bayes, are effective for various Indonesian text classification tasks. However, research specifically utilizing article metadata to determine the suitability of articles for SINTA-indexed journals remains limited, especially regarding the integration of TF–IDF features and cross-validation–based evaluation. This study aims to develop a classification model for determining article–journal suitability within SINTA using Term Frequency–Inverse Document Frequency and the Naïve Bayes algorithm. The dataset consists of 1,200 article metadata entries, including titles, abstracts, and keywords, collected through manual crawling of technology-related journals listed on the SINTA portal. The research stages include data collection, text preprocessing (case folding, translation, tokenization, stopword removal, and stemming), metadata merging, feature extraction using TF–IDF, and the implementation of Naïve Bayes with a 5-fold cross-validation scheme. Evaluation using confusion matrix metrics shows that the model achieved an accuracy of 0.7058, precision of 0.6977, recall of 0.7133, and an F1-score of 0.7065. These results indicate that Naïve Bayes provides a reasonably strong classification performance on article metadata and has potential application in journal submission recommendation systems
Co-Authors Adelia Saharani Ainunna’imah Anggit Wirasto Fanani Arief Ghozali Fatwa Tentama Herman Herman Iftitah Dwi Ulumiyah Imam Riadi Jamalludin Jamalludin Lina Handayani Lu'lu' Nafiati Lu'lu' Nafiati Lu’Lu’ Nafiati Lu’lu’ Nafiati Maulany Citra Pandini Merlinda Wibowo Muhammad Kunta Biddinika Murinto Murinto Nadia Wati Aprianti Norma Sari Nur Rachmaliany Nurul Huda Prabandari Listyaningrum Rahmasari Adi Putri Imaniati Rajunaidi Rajunaidi Rinday Zildjiani Salji Shidik Shidik Sri Winiarti Sri Winiati Sudarsono, Bambang Sulistyawati Sulistyawati Sunardi, Sunardi Surahma Asri Mulasari Surahma Asti Mulasari Tole Sutikno Tri Wahyuni Sukesi Wegig Pratama Yulianto, Dinan