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Vol. 3 No. 2 (2026) • Articles
Open Access

Analisis Sentimen Konsumen pada Rumah Makan di Mataram Menggunakan Algoritma K-Nearest Neighbor, Naïve Bayes, dan Support Vector Machine

Ameylan Verina Tabun
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Hairani Hairani
Universitas Bumigora image/svg+xml
Dadang Priyanto
Universitas Bumigora image/svg+xml
Published: September 28, 2026 Pages: 83-91
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Abstract

Consumer reviews on digital platforms can be used to identify customer perceptions of food quality, service, price, and restaurant comfort. This study used 1,006 reviews from 18 restaurants in Mataram City collected through web scraping from TripAdvisor. After data cleaning and selection, 780 reviews were used as the final dataset. The study aimed to analyze consumer review sentiment and compare the performance of K-Nearest Neighbor (K-NN), Naïve Bayes (NB), and Support Vector Machine (SVM) algorithms in classifying positive, negative, and neutral sentiments. A quantitative approach with a comparative experimental method was employed. The research stages included data collection, preprocessing, sentiment labeling based on lexicon and rating, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), classification, and evaluation using a confusion matrix. Tests were conducted using 70:30, 80:20, and 90:10 data splits with accuracy, precision, recall, and F1-score as evaluation metrics. The results showed that SVM achieved the highest accuracy across all testing scenarios. The best performance was obtained with the 80:20 split, reaching 87.1% accuracy, 87.0% precision, 87.0% recall, and 86.0% F1-score.

Keywords:
Sentiment Analysis TripAdvisor Text Mining TF-IDF K-NN Naïve Bayes SVM
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Author Biographies
Ameylan Verina Tabun Universitas Bumigora

Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Bumigora, Kota Mataram, Nusa Tenggara Barat, Indonesia.

Hairani Hairani Universitas Bumigora

Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Bumigora, Kota Mataram, Nusa Tenggara Barat, Indonesia.

Dadang Priyanto Universitas Bumigora

Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Bumigora, Kota Mataram, Nusa Tenggara Barat, Indonesia.

How to Cite
Tabun, A. V., Hairani, H., & Priyanto, D. (2026). Analisis Sentimen Konsumen pada Rumah Makan di Mataram Menggunakan Algoritma K-Nearest Neighbor, Naïve Bayes, dan Support Vector Machine. Jurnal Ilmu Komputer Dan Teknologi Informasi, 3(2), 83-91. https://doi.org/10.63447/jikti.v3i2.2004
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This work is licensed under a Copyright (c) 2026 Ameylan Verina Tabun, Hairani Hairani, Dadang Priyanto .

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References
Total: 16 References
  1. Damaryana, L. (2025). Analisis sentimen komentar YouTube terhadap program Makan Bergizi Gratis menggunakan algoritma Support Vector Machine dan K-Nearest Neighbor [Doctoral dissertation, Universitas Nasional].
  2. Firdaus, A. A., Saputro, J. S., Anwar, M., Adriyanto, F., Maghfiroh, H., Ma'arif, A., ... Hidayat, R. (2024). Application of sentiment analysis as an innovative approach to policy making: A review. Journal of Robotics and Control (JRC), 5(6), 1784–1798. https://doi.org/10.18196/jrc.v5i6.22573
  3. Ichsansyah, R. F. P., & Subektiningsih, S. (2026). Perbandingan algoritma Naïve Bayes dan Random Forest dalam klasifikasi sentimen ulasan pengguna Kredivo di Play Store. Jurnal Teknologi Informasi Dan Ilmu Komputer, 13(2), 297–308.
  4. Muliawan, J., & Dazki, E. (2023). Sentiment analysis of Indonesia’s capital city relocation using three algorithms: Naïve Bayes, KNN, and Random Forest. Jurnal Teknik Informatika (JUTIF), 4(5), 1227–1236. https://doi.org/10.52436/1.jutif.2023.4.5.1436
  5. Nabila, J., Putra, I. A. D. B., & Wijayanto, H. (2025). Sentiment analysis on Starbucks reviews: Implementation of K-Nearest Neighbors and Support Vector Machine. Infact: International Journal of Computers, 9(02), 79–86. https://doi.org/10.61179/infact.v9i02.761
  1. Prasetyo, S. D., Hilabi, S. S., & Nurapriani, F. (2023). Analisis sentimen relokasi ibukota Nusantara menggunakan algoritma Naïve Bayes dan KNN. Jurnal KomtekInfo, 10(1), 1–7. https://doi.org/10.35134/komtekinfo.v10i1.330
  2. Pratama, S. R., & Fajri, I. N. (2025). Comparison of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in predicting customer satisfaction. Journal of Computer Science and Informatics Engineering, 4(3), 135–146. https://doi.org/10.55537/cosie.v4i3.1160
  3. Purnawibawa, I. W. A., Purnama, I. N., & Wijaya, I. N. Y. A. (2022). Komparasi algoritme K-Nearest Neighbors dan Support Vector Machines dalam prediksi layanan produk ICONNET. Progresif: Jurnal Ilmiah Komputer, 18(2), 271–282. https://doi.org/10.35889/progresif.v18i2.894
  4. Rahat, A. M., Kahir, A., & Masum, A. K. M. (2020). Comparison of Naive Bayes and SVM algorithm based on sentiment analysis using review dataset. In Proceedings of the 2019 8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019 (pp. 266–270). https://doi.org/10.1109/SMART46866.2019.9117512
  5. Rahayu, A. N. S., Hermanto, T. I., & Nugroho, I. M. (2022). Sentiment analysis using K-Nearest Neighbor based on particle swarm optimization according to sunscreen’s reviews. Jurnal Teknik Informatika (JUTIF), 3(6), 1639–1646. https://doi.org/10.20884/1.jutif.2022.3.6.425
  6. Rukmana, D., Putri, A. N., Karim, A., & ZA, M. (2025). Sentiment classification of customer reviews in the fast-food industry using the Naïve Bayes algorithm. bit-Tech, 8(1), 930–940. https://doi.org/10.32877/bt.v8i1.2766
  7. Salsabillah, D. F., Ratnawati, D. E., & Setiawan, N. Y. (2024). Analisis sentimen ulasan rumah makan menggunakan perbandingan algoritma Support Vector Machine dengan Naive Bayes (Studi kasus: Ayam Goreng Nelongso Cabang Singosari, Malang). Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(1), 107–116. https://doi.org/10.25126/jtiik.20241117584
  8. Samudra, B., Lestanti, S., & Romadhona, R. D. (2025). Sentiment analysis of Mie Gacoan reviews in Blitar City on the Grab application using the Support Vector Machine method. JOSAR (Journal of Students Academic Research, 10(2), 129–139. https://doi.org/10.35457/jwf5eb71
  9. Sari, A. W., Hermanto, T. I., & Defriani, M. (2023). Sentiment analysis of tourist reviews using K-Nearest Neighbors algorithm and Support Vector Machine. Sinkron: Jurnal dan Penelitian Teknik Informatika, 7(3), 1366–1378. https://doi.org/10.33395/sinkron.v8i3.12447
  10. Sifa Amalia, B., Umaidah, Y., & Mayasari, R. (2021). Analisis sentimen review pelanggan restoran menggunakan algoritma Support Vector Machine dan K-Nearest Neighbor. SITEKIN: Jurnal Sains, Teknologi dan Industri, 19(1), 28–34.
  11. Singgalen, Y. A. (2023). Analisis sentimen konsumen terhadap food, services, and value di restoran dan rumah makan populer Kota Makassar berdasarkan rekomendasi TripAdvisor menggunakan metode CRISP-DM dan SERVQUAL. Building of Informatics, Technology and Science (BITS), 4(4). https://doi.org/10.47065/bits.v4i4.3231