SENTIMENT ANALYSIS OF PLN MOBILE APPLICATION SERVICES USING NAIVE BAYES, SUPPORT VECTOR MACHINE (SVM) AND DECISION TREE METHODS

Bagus Adi Prabowo; Achmad Hindasyah; Abu Khalid Rivai

doi:10.34288/jri.v7i3.378

Authors

Bagus Adi Prabowo Universitas Pamulang
Achmad Hindasyah Universitas Pamulang
Abu Khalid Rivai Universitas Pamulang

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v7i3.378

Keywords:

Sentiment Analysis, PLN Mobile, Naïve Bayes, SVM, Decision Tree

Abstract

The advancement of information technology has driven public service providers such as PLN to introduce digital innovations, one of which is the PLN Mobile application that enables customers to access various services online. As the number of users increases, numerous reviews have been submitted through the Google Play Store platform, which can be utilized to evaluate service quality. This study aims to conduct sentiment analysis on user reviews of the PLN Mobile application using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Decision Tree. A total of 4,992 review data were collected and processed through text preprocessing stages, including case folding, tokenization, stopword removal, stemming, and vectorization using the TF-IDF method. The data were then split into training and testing sets with a ratio of 80:20 and trained using the three classification algorithms. Model evaluation was conducted using precision, recall, f1-score, and accuracy metrics. The evaluation results indicate that the SVM algorithm delivers the best performance with an accuracy of 94%, followed by Naïve Bayes and Decision Tree, each with an accuracy of 91%. However, all three models demonstrated limited effectiveness in detecting neutral sentiments. Based on these findings, the SVM algorithm is recommended as the most effective model for sentiment classification of PLN Mobile application reviews.

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References

Achmad, A. A., Iin, K., & Iska, Y. (2023). Analisis Klasifikasi Sentimen Berbasis Topik pada Ulasan Layanan Dana dan Sakuku dengan Convolutional Neural Network. INFORMASI (Jurnal Informatika Dan Sistem Informasi), 15(2), 225–236. https://doi.org/10.37424/informasi.v15i2.267

Adi, S. I. R., Bakkara, B., Zega, K. A., Vielita, F. N., & Rakhmawati, N. A. (2024). Analisis Sentimen Masyarakat Terhadap Progress Ikn Menggunakan Model Decision Tree. JIKA (Jurnal Informatika), 8(1), 57. https://doi.org/10.31000/jika.v8i1.9803

Arif Chandra, M. I., & Yusuf, R. (2024). VISUALISASI KATA KUNCI PEMBERITAAN PEMILU 2024 MENGGUNAKAN SCAPY DAN WORDCLOUD. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 5(1), 41–46. https://doi.org/10.46764/teknimedia.v5i1.187

Asri, Y., Suliyanti, W. N., Kuswardani, D., & Fajri, M. (2022). Pelabelan Otomatis Lexicon Vader dan Klasifikasi Naive Bayes dalam menganalisis sentimen data ulasan PLN Mobile. PETIR, 15(2), 264–275. https://doi.org/10.33322/petir.v15i2.1733

Cahyaningtyas, C., Nataliani, Y., & Widiasari, I. R. (2021). Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE. AITI, 18(2), 173–184. https://doi.org/10.24246/aiti.v18i2.173-184

Derisma, D., & Febrian, F. (2020). Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara. Bina Insani Ict Journal, 7(1), 53. https://doi.org/10.51211/biict.v7i1.1343

Desiani, A., Akbar, M., Amran Matematika, A., Matematika dan Ilmu Pengetahuan Alam, F., Sriwijaya Jl Raya Palembang -Prabumlih Km, U., Indah, I., Indralaya, K., & Ogan Ilir Sumatera Selatan, K. (2022). Implementasi Algoritma Naïve Bayes dan Support Vector Machine (SVM) Pada Klasifikasi Penyakit Kardiovaskular. Jurnal Teknik Elektro Dan Komputasi (ELKOM), 4(2), 207–214. http://jurnal.unmuhjember.ac.id/index.php/ELKOM/article/view/7691

Efraim, D. A. (2023). Analisis Sentimen Pada Sosial Media Instagram Menggunakan Algoritma Naive Bayes ( Studi Kasus : Timnas Futsal Indonesia ). April 2012, 498–509. https://conference.upnvj.ac.id/index.php/senamika/article/view/2574

Faisal, H., Febriandirza, A., & Hasan, F. N. (2024). Analisis Sentimen Terkait Ulasan Pada Aplikasi PLN Mobile Menggunakan Metode Support Vector Machine. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 5(1), 303–312. https://pkm.tunasbangsa.ac.id/index.php/kesatria/article/view/339

Faried Vangeran Saragih, G., Gata, W., Ilmu Komputer, M., Ilmu Komputer, F., & Nusa Mandiri Jakarta, S. (2017). Terakreditasi SINTA Peringkat 2 Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection Algoritma Naive Bayes dan Support Vector Machine. Masa Berlaku Mulai, 1(3), 504–512. https://jurnal.iaii.or.id/index.php/RESTI/article/view/1942

Hilal, K. R., Setiawan, N. Y., & Ratnawati, D. E. (2024). Fakultas Ilmu Komputer ANALISIS SENTIMEN BERBASIS ASPEK UNTUK PENGGUNA PLN MOBILE PADA GOOGLE PLAYSTORE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM). 8 No 7(1), 2548–2964. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/13927/

Khusna, T. N., & Sugiantoro, B. (2023). Komparasi Algoritma Naïve Bayes Classifier, K-Nearest Neighbor, dan Decision Tree untuk Menganalisis Sentimen Masyarakat terhadap Kesehatan Mental pada Media Sosial Twitter. JUMANJI (Jurnal Masyarakat Informatika Unjani), 7(1), 18. https://doi.org/10.26874/jumanji.v7i1.279

Maulana, M. I., Budianita, E., Fikry, M., & Yanto, F. (2023). Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier. Jurnal Sistem Komputer Dan Informatika (JSON), 4(3), 485. https://doi.org/10.30865/json.v4i3.5854

Meidick Dias Devasela, & Tata Sutabri. (2024). Analisis Tingkat Kepuasan Layanan Aplikasi PLN Mobile Menggunakan Framework ITIL V3 Pada PT PLN Indonesia. Jurnal RESTIKOM : Riset Teknik Informatika Dan Komputer, 6(1), 113–121. https://doi.org/10.52005/restikom.v6i1.292

Mukti, A., Hadiyanti, A. D., Nurlaela, A., & Panjaitan, J. (2023). Sistem Analisa Sentiment Bakal Calon Presiden 2024 Menggunakan Metode NLP Berbasis Web. Jurikom, 6(1), p-ISSN. https://www.poltekstpaul.ac.id/jurnal/index.php/jsoscied/article/view/621

Oktavia, D., Ramadahan, Y. R., & Minarto. (2023). Analisis Sentimen Terhadap Penerapan Sistem E-Tilang Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM). KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(1), 407–417. https://doi.org/10.30865/klik.v4i1.1040

Raksaka Indra Alhaqq, I Made Kurniawan Putra, & Yova Ruldeviyani. (2022). Analisis Sentimen terhadap Penggunaan Aplikasi MySAPK BKN di Google Play Store. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 11(2), 105–113. https://doi.org/10.22146/jnteti.v11i2.3528

Syafrizal, S., Afdal, M., & Novita, R. (2023). Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(1), 10–19. https://doi.org/10.57152/malcom.v4i1.983

Tanggraeni, A. I., & Sitokdana, M. N. N. (2022). Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 785–795. https://doi.org/10.35957/jatisi.v9i2.1835

Zulfahmi, I. (2024). Analisis Sentimen Aplikasi PLN Mobile Menggunakan Metode Decission Tree. 3(1). https://doi.org/10.55606/juprit.v3i1.3096