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All Journal Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika)
Maretta, Aulia Pinkan
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Languange, Linguistic, Communication & Media

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PERBANDINGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR TERHADAP ANALISIS SENTIMEN ULASAN PROGRAM MAKAN SIANG GRATIS DI INDONESIA Fathoni, Fathoni; Maretta, Aulia Pinkan; Kusuma, Aisha Nuraini; Sasmita, Ruth Mei; Rizkyllah, Anabel Fiorenza; Ibrahim, Ali
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14084

Abstract

Program Makan Siang Gratis di Indonesia sebagai kebijakan kontroversial memicu respons beragam di media sosial, sehingga analisis sentimen diperlukan untuk memahami persepsi publik secara komprehensif. Permasalahan utama terletak pada keterbatasan metode klasifikasi dalam menangani data teks informal, yang berpotensi membuat akurasi identifikasi menurun. Penelitian ini bertujuan membandingkan kinerja algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN) dalam mengklasifikasikan sentimen ulasan masyarakat terhadap program tersebut. Sebanyak 2.080 komentar dari X (Twitter) dan YouTube dikumpulkan melalui web scraping, kemudian diproses dengan tahapan cleaning (penghapusan mention, URL), penghapusan stopwords, tokenisasi, dan transformasi fitur menggunakan TF-IDF. Dataset dibagi dengan rasio 60:40 untuk training dan testing, lalu dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan Naïve Bayes mencapai akurasi tertinggi (87,75%), lebih unggul dari K-NN (86,67%). Kedua algoritma mencatat precision sempurna (100%), namun memiliki kelemahan dalam recall (NB: 18,4%; K-NN: 11,2%) dan F1-score (NB: 31%; K-NN: 20,1%), yang mengindikasikan kesulitan dalam mengidentifikasi sentimen positif. Penelitian ini membuktikan keunggulan Naïve Bayes dalam analisis sentimen kebijakan publik berbasis teks informal
PERBANDINGAN METODE NAÏVE BAYES, DECISION TREE, DAN KNN DALAM ANALISIS SENTIMEN APLIKASI GOJEK DI PLAYSTORE Maretta, Aulia Pinkan; Anadia, Qothrunnada Wafi; Sasmita, Ruth Mei; Epriyanti, Nadia; Rizkyllah, Anabel Fiorenza; Mariska, Inneke Via; Tania, Ken Ditha; Meiriza, Allsela
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 2 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Mei 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zjf8x279

Abstract

Sentiment analysis on user evaluation of Gojek application services on Play Store is important to understand user opinions on the services provided. This study compares three machine learning methods, namely Naïve Bayes, Decision Tree, and K-Nearest Neighbors (KNN) when categorizing user sentiment on Google Play Store as positive, negative, or neutral. The data processed comes from the Gojek user review dataset obtained from Kaggle. The analysis process involves data preprocessing (cleaning, stopword removal, tokenization, and split data), data transformation, and implementation of classification algorithms. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results of the study prove that Naïve Bayes has the best performance with an accuracy of 89%, followed by KNN (86%) and Decision Tree (84%). This study provides good insight for application developers in choosing the best method to understand user opinions and improve service quality.
Co-Authors Ali Ibrahim Allsela Meiriza, Allsela Anadia, Qothrunnada Wafi Epriyanti, Nadia Fathoni Fathoni Ken Ditha Tania Kusuma, Aisha Nuraini Mariska, Inneke Via Rizkyllah, Anabel Fiorenza Sasmita, Ruth Mei