Sentimen Analisa Ulasan Aplikasi Access by KAI pada Google Play Store menggunakan Algoritma K-NN

 (*)Nur Afina Jiana Mail (Universitas Stikubank, Semarang, Indonesia)
 Budi Hartono (Universitas Stikubank, Semarang, Indonesia)

(*) Corresponding Author

Submitted: May 20, 2024; Published: July 26, 2024

Abstract

Access by KAI is an application developed by PT. Kereta Api Indonesia (KAI) that plays an important role in simplifying the train ticket booking process for its users. The application has been downloaded more than 10 million times through the Google Play Store, with a review rating of 2.2 out of a scale of 1 to 5. This relatively low rating indicates a level of dissatisfaction among users. Moreover, the high number of negative reviews compared to positive reviews could also cause bias in the analysis results. Reviews from other users greatly influence the perception of the app's performance and quality among potential users. Therefore, this study aims to conduct a sentiment analysis of the application reviews published on the Google Play Store. The review data analyzed includes 1300 negative reviews and 457 positive reviews considered most relevant to provide a comprehensive overview of user perception. The method used in this analysis is the K-Nearest Neighbor (K-NN) Algorithm. The results of the study show an accuracy rate of 80%, a precision of 73%, a recall of 63%, and an f1-score of 65%. These findings are expected to provide insights for the app developers to improve the quality and performance of the Access by KAI application.

Keywords


Sentiment Analysis; Reviews; K-Nearest Neighbor; Kai Access; Classification

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References

D. Rosa Indah, A. Permata Putra, and M. Afriyan Firdaus, “Analysis of User Acceptance Using UTAUT2 Model in KAI Access Application,” Jurnal Teknologi Informasi dan Pendidikan, vol. 15, no. 2, 2022, doi: 10.24036/tip.v15i2.

M. Izunnahdi, G. Aburrahman, and A. Eko Wardoyo, “Sentimen Analisis Pada Data Ulasan Aplikasi KAI Access Di Google PlayStore Menggunakan Metode Multinomial Naive Bayes Sentiment Analysis on KAI Access Application Review Data on Google PlayStore Using Multinomial Naive Bayes Method,” Jurnal Smart Teknologi, vol. 4, no. 2, pp. 192–198, 2023, [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST

N. B. Sidauruk and N. Riza, “SENTIMEN ANALISIS DATA PENGGUNA TERHADAP KAI ACCESS Systematic Literature Review,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 2, pp. 1297–1303, 2023, doi: 10.36040/jati.v7i2.6764.

G. Radiena and A. Nugroho, “ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN APLIKASI KAI ACCESS MENGGUNAKAN METODE SUPPORT VECTOR MACHINE,” Jurnal Pendidikan Teknologi Informasi (JUKANTI), vol. 6, no. 1, pp. 1–10, 2023, doi: 10.37792/jukanti.v6i1.836.

P. Astuti and N. Nuris, “Penerapan Algoritma KNN Pada Analisis Sentimen Review Aplikasi Peduli Lindungi,” Computer Science (Co-Science), vol. 2, no. 2, pp. 137–142, 2022, doi: 10.31294/coscience.v2i2.1258.

M. Fadhil Rizki, W. Pramusinto, M. Hardjianto, and Subandi, “Implementasi Algoritms K-Nearest Neighbor untuk Analisis Sentimen Aplikasi Jobstreet,” Seminar Nasional Mahasiswa Fakultas Teknologi Informasi, vol. 2, no. 1, pp. 267–276, 2023, Accessed: Jun. 06, 2024. [Online]. Available: https://senafti.budiluhur.ac.id/index.php/senafti/article/view/621

J. A. Josen Limbong, I. Sembiring, and K. Dwi Hartomo, “ANALISIS KLASIFIKASI SENTIMEN ULASAN PADA E-COMMERCE SHOPEE BERBASIS WORD CLOUD DENGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR,” Jurnal Teknologi Informasi dan Imu Komputer (JTIIK), vol. 9, no. 2, pp. 347–356, 2022, doi: 10.25126/jtiik.202294960.

D. Pratmanto, F. Fandi, D. Imaniawan, and C. Author, “Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors,” Computer Science (CO-SCIENCE), vol. 3, no. 2, pp. 110–117, 2023, doi: 10.31294/coscience.v3i2.1917.

A. Nugraha, Y. H. Chrisnanto, and R. Yuniarti, “Prediksi Sentimen Pada Sosial Media Twitter Mengenai Produk Smartphone Menggunakan Algoritma K-NN Classification,” Seminar Nasional Sains & Teknologi Informasi (SENSASI) , pp. 251–258, 2019, [Online]. Available: http://prosiding.seminar-id.com/index.php/sensasi/issue/archivePage|251

Z. Ulfah Siregar, R. Ruli, A. Siregar, and R. Arianto, “KLASIFIKASI SENTIMENT ANALYSIS PADA KOMENTAR PESERTA DIKLAT MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” JURNAL KILAT, vol. 8, no. 1, 2019, doi: 10.33322/kilat.v8i1.421.

R. Rasenda, H. Lubis, and R. Ridwan, “Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 4, no. 2, p. 369, Apr. 2020, doi: 10.30865/mib.v4i2.2051.

F. Putra Herlambang and D. Avianto, “JURNAL MEDIA INFORMATIKA BUDIDARMA Analisis Sentimen Opini Pengguna Twitter Terhadap Tragedi Kanjuruhan Malang dengan Metode Support Vector Machine,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, no. 4, pp. 1727–1739, 2023, doi: 10.30865/mib.v7i4.6332.

S. Dyah Fritama, Y. Raymond Ramadhan, and M. Andayani Komara, “Analisis Sentimen Review Produk Acne Spot Treatment di Female Daily Menggunakan Algoritma K-Nearest Neighbor,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 1, pp. 134–143, 2023, doi: 10.30865/klik.v4i1.1070.

A. Dwiki, A. Putra, and S. Juanita, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Bibit Dan Bareksa Dengan Algoritma KNN,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 2, pp. 636–646, Jun. 2021, doi: 10.35957/jatisi.v8i2.962.

A. N. Assidyk, E. B. Setiawan, and I. Kurniawan, “Analisis Perbandingan Pembobotan TF-IDF dan TF-RF pada Trending Topic di Twitter dengan Menggunakan Klasifikasi K-Nearest Neighbor,” e-Proceeding of Engineering, vol. 7, no. 2, pp. 7773–7781, Aug. 2020.

A. Baita, Y. Pristyanto, and N. Cahyono, “ANALISIS SENTIMEN MENGENAI VAKSIN SINOVAC MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN K-NEAREST NEIGHBOR (KNN),” Information System Journal (INFOS), vol. 4, no. 2, pp. 42–46, Nov. 2021, doi: 10.24076/infosjournal.2021v4i2.687.

E. Wahyu Sholeha, S. Yunita, R. Hammad, V. Cahya Hardita, T. Rekayasa Komputer Jaringan, and P. Tanah Laut, “Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor (Sentiment Analysis of Online Travel Agent Using Naïve Bayes and K-Nearest Neighbor),” Jurnal Teknologi Informasi dan Multimedia, vol. 3, no. 4, pp. 203–208, 2022, doi: 10.35746/jtim.v3i4.178.

S. Wahyunita, Y. Azhar, and N. Hayatin, “Analisa Sentimen Tweet Berbahasa Indonesia dengan Menggunakan Metode Pembobotan Hybrid TF-IDF pada Topik Transportasi Online,” REPOSITOR, vol. 2, no. 2, pp. 185–192, Jan. 2024, doi: 10.22219/repositor.v2i2.30471.

D. Siswanto, Zamzami, L. Nijal, and S. Rajab, “ANALISA SENTIMEN PUBLIK MENGENAI PEREKONOMIAN INDONESIA PADA MASA PANDEMI COVID-19 DI TWITTER MENGGUNAKAN METODE KLASIFIKASI K-NN DAN SVM,” Jurnal Pusat Akses Kajian Teknologi Artificial Intelligence, vol. 2, no. 1, pp. 1–9, 2022.

M. F. El Firdaus, N. Nurfaizah, and S. Sarmini, “Analisis Sentimen Tokopedia Pada Ulasan di Google Playstore Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 5, p. 1329, Oct. 2022, doi: 10.30865/jurikom.v9i5.4774.

E. Indrayuni, A. Nurhadi, and D. A. Kristiyanti, “Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc,” Faktor Exacta, vol. 14, no. 2, pp. 64–71, Aug. 2021, doi: 10.30998/faktorexacta.v14i2.9697.

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