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All Journal PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Pengembangan Riset dan Observasi Teknik Informatika
Ni Kadek Winda Patrianingsih
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Evaluasi Naïve Bayes dan K-Nearest Neighbor dalam Klasifikasi Sentimen Ulasan Produk Skincare MSGLOW di Tokopedia Arya Sugianta, I Kadek; Ni Kadek Winda Patrianingsih
Pengembangan Riset dan Observasi Teknik Informatika Vol 12 No 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/protekinfo.v12i1.10302

Abstract

Industri skincare di Indonesia mengalami pertumbuhan pesat, terutama dalam segmen perawatan wajah pria. Platform e-commerce seperti Tokopedia menjadi sarana utama bagi konsumen untuk memberikan ulasan produk, sehingga analisis sentimen terhadap ulasan pelanggan menjadi penting dalam memahami persepsi pasar. Penelitian ini mengevaluasi kinerja algoritma Naïve Bayes dan K-Nearest Neighbor (KNN) dalam klasifikasi sentimen ulasan produk skincare di e-commerce. Data dikumpulkan melalui web scraping dari ulasan pelanggan terhadap produk MSGLOW Men Energizer Facial Wash di Tokopedia, menghasilkan 475 data ulasan. Proses pengolahan teks dilakukan dengan metode text mining, termasuk case folding, tokenizing, stop words removal, dan stemming. Analisis dilakukan menggunakan perangkat lunak RapidMiner, dengan klasifikasi sentimen menjadi positif, negatif, dan netral. Evaluasi model dilakukan menggunakan confusion matrix untuk mengukur akurasi. Hasil penelitian menunjukkan bahwa Naïve Bayes memiliki akurasi sebesar 92,16%, lebih unggul dibandingkan KNN yang hanya mencapai 68,82%. Temuan ini menegaskan bahwa pemilihan algoritma yang tepat berperan penting dalam meningkatkan akurasi analisis sentimen di e-commerce. Selain itu, penelitian ini memberikan wawasan bagi industri skincare dalam memahami opini pelanggan dan mengoptimalkan strategi pemasaran berbasis data.
Sentiment Classification of Cremation Vs Ngaben on Social Media X: A Lexicon-Based Comparison of SVM and KNN Ni Kadek Winda Patrianingsih; Arditaloka, I Wayan Angga
Jurnal PROCESSOR Vol 21 No 2 (2026): Jurnal Ilmiah Processor, Edisi Oktober 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2026.21.2.3002

Abstract

The transition of some Balinese Hindus away from the conventional ngaben cremation ceremony toward modern crematorium services has become a widely discussed public topic on social media platform X, largely centered on concerns about cost, time, and the potential erosion of customary values. This study labels netizen sentiment on this issue using a lexicon-based approach and builds and evaluates an automatic classification model based on Support Vector Machine (SVM), benchmarked against K Nearest Neighbor (KNN), as a preliminary step toward designing a public opinion monitoring information system. A total of 841 mixed language tweets were gathered from platform X and narrowed to 533 Indonesian language records after preprocessing (case folding and removal of URLs, mentions, hashtags, punctuation, and numbers).  lexicon-based labelling, using a sentiment dictionary purpose built for the Balinese cultural context, produced a distribution of 50.66% neutral, 44.28% positive, and 5.07% negative sentiment. The data were then TF IDF weighted and split 80:20 into stratified training and testing sets. Testing showed the linear kernel SVM reaching 82.24% accuracy, far ahead of KNN (K=5) at only 52.34% on the same data. Per class evaluation showed SVM performing well on the neutral and positive classes but still limited on the negative class (33% recall) because of class imbalance. These results suggest SVM is more reliable than KNN for high dimensional, class imbalanced text data, and can serve as a foundation for an automatic classification module within a public opinion monitoring system supporting cultural preservation policy in Bali.
Co-Authors Arditaloka, I Wayan Angga Arya Sugianta, I Kadek