ANALISIS SENTIMEN ULASAN APLIKASI ”ACCESS BY KAI” MENGGUNAKAN ALGORITMA MACHINE LEARNING
Abstract
The "Access by KAI" application has received a diverse range of ratings on the Google Play Store. This study aims to analyze user sentiment towards the "Access by KAI" application. The research employs the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology as a framework, with data collection conducted through web scraping user reviews from the Google Play Store from January to June 2024, resulting in 9,124 reviews. The collected data undergoes text processing, including case folding, normalization, tokenization, removal of insignificant words, stemming, and labeling using a lexicon-based approach. The labeling results in 3,905 negative reviews, followed by 3,720 positive reviews, and 851 neutral reviews. The best model for sentiment classification in this study is Logistic Regression, achieving an accuracy of 84%. Sentiment analysis is useful for identifying the main complaints of users regarding the performance of the "Access by KAI" application. This research is expected to provide insights into user opinions about the "Access by KAI" application and to test the performance of machine learning algorithms combined with lexicon-based methods for sentiment classification.
References
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[3] E. Pratama, S.D., & Syaodih, “Analisis Perilaku Konsumen dalam Memanfaatkan Aplikasi KAI Access,” Serv. Manag. Triangle J. Manaj. Jasa, vol. 3, no. 1, pp. 20–27, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jsj 27
[4] A. Nioga, K. C. Brata, and L. Fanani, “Evaluasi Usability Aplikasi Mobile KAI Access Menggunakan Metode System Usability Scale (SUS) Dan Discovery Prototyping (Studi Kasus PT KAI),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 2, pp. 1396–1402, 2019, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4384
[5] W. Nugroho, “Evaluasi Kualitas Digital Payment OVO Berdasarkan Faktor Usability Standar ISO/IEC 9126,” Indones. J. Comput. Sci., vol. 1, no. 1, pp. 14–19, 2022, doi: 10.31294/ijcs.v1i1.1123.
[6] Tommy Suhendra, B. Intan, and A. T. Martadinata, “Analisis Sentimen Pengguna Aplikasi Netflix Pada Ulasan Google Playstore Menggunakan Metode Naïve Bayes Tommy,” ESCAF 3rd, vol. 2, pp. 1011–1022, 2024.
[7] G. Sanjaya and K. M. Lhaksmana, “Lexicon Based ).,” vol. 7, no. 3, pp. 9698–9710, 2020.
[8] F. T. Saputra, S. H. Wijaya, Y. Nurhadryani, and Defina, “Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter,” in 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020, pp. 136–141. doi: 10.1109/ICIMCIS51567.2020.9354269.
[9] Z. Turner, K. Labille, and S. Gauch, “Lexicon-based sentiment analysis for stock movement prediction,” J. Constr. Mater., vol. 2, no. 3, pp. 3–5, 2021, doi: 10.36756/jcm.v2.3.5.
[10] P. Agusia, M. Uli, A. Manurung, V. Calista, and V. C. Mawardi, “Pemanfaatan Word Cloud Pada Analisis Sentimen Dalam Menggali Persepsi Publik,” pp. 25–30, 2024.
[11] D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.
[12] F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” J. SIMETRIS, vol. 10, no. 2, pp. 681–686, 2019.
[13] F. Sulianta, “Basic Data Mining from A to Z - Dasar Membangun Tindakan Bisnis,” no. July, p. 132, 2023, [Online]. Available: https://books.google.co.id/books?id=JcLhEAAAQBAJ&
[14] M. Ridho Handoko, “Sistem Pakar Diagnosa Penyakit Selama Kehamilan Menggunakan Metode Naive Bayes Berbasis Web,” J. Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 50–58, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
[15] A. Multivariate, B. Studi, K. Ovo, and D. A. N. Gopay, “Analisis sentimen pengguna dompet digital menggunakan algoritma multivariate bernoulli (studi kasus: ovo dan gopay),” vol. 7, pp. 345–351, 2024, doi: 10.37600/tekinkom.v7i1.1223.
[16] S. A. Assaidi and F. Amin, “Analisis Sentimen Evaluasi Pembelajaran Tatap Muka 100 Persen pada Pengguna Twitter menggunakan Metode Logistic Regression,” J. Pendidik. Tambusai, vol. 6, no. 2, pp. 13217–13227, 2022.
[17] Q. R. Cahyani et al., “Prediksi Risiko Penyakit Diabetes menggunakan Algoritma Regresi Logistik Diabetes Risk Prediction using Logistic Regression Algorithm Article Info ABSTRAK,” JOMLAI J. Mach. Learn. Artif. Intell., vol. 1, no. 2, pp. 2828–9099, 2022, doi: 10.55123/jomlai.v1i2.598.
[18] F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 9, pp. 4305–4313, 2022.
[19] A. Wandani, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 651–665, 2021.
Published
2024-12-31
How to Cite
NUGROHO, Moh Andi Setyo; SUSILO, Dahlan; RETNONINGSIH, Dwi.
ANALISIS SENTIMEN ULASAN APLIKASI ”ACCESS BY KAI” MENGGUNAKAN ALGORITMA MACHINE LEARNING.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 2, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1854>. Date accessed: 17 jan. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1854.
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