ANALISIS SENTIMEN ULASAN APLIKASI IBI LIBRARY PADA GOOGLE PLAY STORE MENGGUNAKAN NAIVE BAYES CLASSIFIER

Muhammad Reyan, Franindya Purwaningtyas

Abstract


Riset bertujuan sebagai cara mengidentifikasi persepsi pengguna terhadap aplikasi iBI Library dan mengevaluasi kualitas layanan perpustakaan digital berbasis mobile library dalam memenuhi kebutuhan informasi. Pendekatan Knowledge Discovery in Database (KDD) yang diintegrasikan bersama metode klasifikasi Naive Bayes dalam tiga model meliputi Gaussian, Multinomial dan Bernoulli digunakan pada penelitian.  Pemerolehan data ulasan berasal dari web scraping google play store, kemudian dianalisis melalui proses preprocessing, pelabelan sentimen dengan VADER, transformasi fitur menggunakan TF-IDF, evaluasi model dengan confusion matrix akan dilakukan. Dari yang dilakukan tersebut, ditunjukkan bahwa model MultinomialNB menghasilkan akurasi tertinggi (68,89%), diikuti BernoulliNB (66,67%) dan GaussianNB (51,11%). Sebagian besar ulasan berada dalam kategori netral, yakni sebanyak 76,9% dari total data. Sementara itu, ulasan dengan sentimen positif (12,0%) dan sentimen negatif tercatat sebanyak (11,1%). Visualisasi wordcloud memperkuat analisis dengan menampilkan kosakata dominan pada tiga jenis sentiment, yaitu positif, negatif dan netral. Dengan begitu, kesimpulan kajian ini ialah integrasi NLP dan klasifikasi Naive Bayes merupakan pendekatan efektif dalam mengevaluasi opini pengguna dan meningkatkan pengambilan keputusan dalam pengembangan aplikasi perpustakaan digital.


Full Text:

PDF

References


A. A. Harahap, R. Iqbal, D. Amalia, and S. Y. Kamseno, “EVALUATION OF USER SATISFACTION OF IBI LIBRARY APPLICATION USING PIECES MODEL,” J. El -Pustaka, vol. 05, no. 01, pp. 35–58, 2024, [Online]. Available: https://ejournal.radenintan.ac.id/index.php/elpustaka/article/download/22241/7442?__cf_chl_tk=8xFdn.eJdlDMqQMYVIwXwOFp4QTqLZigBY9aaoaMkQc-1745243761-1.0.1.1-ibqRbj8NfuKDaDWAdjbvvlVzE5Tzwaqsd1Mhr1bzw0w

Aksaramaya, “iBI Library: Aplikasi Digital Library Milik BI,” 2023. https://aksaramaya.com/en/ibi-library-aplikasi-digital-library-milik-bi/ (accessed Apr. 21, 2025).

R. P. Arianty, M. R. A. Prasetya, S. P. Cipta, and B. Nugraha, “Sentiment analysis study of library services using support vector machine methods,” TEKNOSAINS, vol. 12, no. 1, pp. 122–132, 2025, doi: 10.37373/tekno.v12i1.1303.

H. N. Zuhdi and B. Prasetiyo, “Sentiment Analysis On Ipusnas Application Reviews In Google Play Store Using Naive Bayes Classifier Analisis Sentimen pada Ulasan Aplikasi iPusnas di Google Play Store Menggunakan Naive Bayes Classifier,” vol. 5, no. 1, pp. 12–19, 2025, [Online]. Available: https://journal.irpi.or.id/index.php/ijirse/article/view/1846

S. Pasaribu, L. Rohani, and M. Faisal, “Persepsi pengguna aplikasi digital perpustakaan bank indonesia ‘IBI Library’ berbasis android,” Triwikrama J. Ilmu Sos., vol. 3, no. 7, pp. 156–166, 2024, [Online]. Available: https://ejournal.warunayama.org/index.php/triwikrama/article/view/3105

M. Raffi, A. Suharso, and I. Maulana, “Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes Sentiment Analysis of Binar Application Reviews on Google Play Store Using Naïve Bayes Algorithm,” J. Inf. Technol. Comput. Sci., vol. 6, no. 1, pp. 1–7, 2023, [Online]. Available: https://journal.ipm2kpe.or.id/index.php/INTECOM/article/download/6117/3690

A. Septiani and I. Budi, “Klasifikasi Ulasan Pengguna Aplikasi: Studi Kasus Aplikasi Ipusnas Perpustakaan Nasional Republik Indonesia (PNRI),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 4, pp. 1110–1120, 2022, doi: 10.29100/jipi.v7i4.3216.

R. Shad, K. Potter, and A. Gracias, “International Journal of Artificial Natural Language Processing ( NLP ) for Sentiment Analysis : A Comparative Study of Machine Learning Algorithms,” SvedbergOpen, vol. 5, no. 1, pp. 58–69, 2025, doi: 10.51483/IJAIML.5.1.2025.58-69.

F. A. Tohir, B. Irawan, and A. Bahtiar, “Analisis Sentimen Aplikasi ChatGPT Mobile Menggunakan Agoritma Naïve Bayes,” ICIT (Innovative Creat. Inf. Technol., vol. 10, no. 2, pp. 179–192, 2024, [Online]. Available: https://ejournal.raharja.ac.id/index.php/icit/article/view/3016

S. Khoerunnisa, D. F. Shiddiq, and D. Nurhayati, “Application of the Naive Bayes Algorithm with TF-IDF and Cross Validation Techniques for Sentiment Analysis Towards Starlink Penerapan Algoritma Naive Bayes dengan Teknik TF-IDF dan Cross Validation untuk Analisis Sentimen Terhadap Starlink,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 5, no. April, pp. 566–577, 2025, [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/download/1852/933/10058

R. Mursyid and A. D. Indriyanti, “Perbandingan Akurasi Metode Analisis Sentimen Untuk Evaluasi Opini Pengguna Pada Platform Media Sosial (Studi Kasus: Twitter),” J. Informatics Comput. Sci., vol. 06, pp. 371–383, 2024, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jinacs/article/view/61322%0Ahttps://ejournal.unesa.ac.id

M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-Based Syst., vol. 226, p. 107134, 2021, doi: 10.1016/j.knosys.2021.107134.

L. J. Anreaja, N. N. Harefa, J. G. P. Negara, V. N. H. Pribyantara, and A. B. Prasetyo, “Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia,” JISA(Jurnal Inform. dan Sains), vol. 5, no. 1, pp. 62–68, 2022, doi: 10.31326/jisa.v5i1.1267.

V. Kumar and B. Bhimrao, “Exploring the Use of Sentiment Analysis in Library User Studies: Approaches and Challenges,” Chang. Landsc. LIS Educ. Res., no. April, 2023, [Online]. Available: https://www.researchgate.net/publication/370058798

A. T. Rizkya, R. Rianto, and A. I. Gufroni, “Implementation of the Naive Bayes Classifier for Sentiment Analysis of Shopee E-Commerce Application Review Data on the Google Play Store,” Int. J. Appl. Inf. Syst. Informatics, vol. 1, no. 1, pp. 31–37, 2023, doi: 10.37058/jaisi.v1i1.8993.

B. Alsanousi, A. S. Albesher, H. Do, and S. Ludi, “Investigating the User Experience and Evaluating Usability Issues in AI-Enabled Learning Mobile Apps: An Analysis of User Reviews,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 6, pp. 18–29, 2023, doi: 10.14569/IJACSA.2023.0140602.

F. Aftab et al., “A Comprehensive Survey on Sentiment Analysis Techniques,” Int. J. Technol., vol. 14, no. 6, pp. 1288–1298, 2023, doi: 10.14716/ijtech.v14i6.6632.

R. F. P. Pratama and W. Maharani, “Comparative Analysis of Naive Bayes and SVM for Improved Emotion Classification on Social Media,” Edumatic J. Pendidik. Inform., vol. 9, no. 1, pp. 11–20, 2025, doi: 10.29408/edumatic.v9i1.29087.

E. A. Elfaiz, R. Akhsani, S. Prayoga, M. Cinthya, and M. S. Akbar, “Analisis Sentimen Performansi Operator Telekomunikasi di Indonesia Menggunakan Metode Text Mining,” J. Sains Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 14–22, 2025, doi: 10.54259/satesi.v5i1.4024.

M. Yusran, S. Siswanto, and A. Islamiyati, “Comparison of Multinomial Naive Bayes and Bernoulli Naive Bayes on Sentiment Analysis of Kurikulum Merdeka with Query Expansion Ranking,” Sistemasi, vol. 13, no. 1, p. 96, 2024, doi: 10.32520/stmsi.v13i1.3187.




DOI: https://doi.org/10.46576/djtechno.v6i2.6968

Article Metrics

Abstract view : 2 times
PDF – 1 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Muhammad Reyan, Franindya Purwaningtyas

DJTECHNO: Jurnal Teknologi Informasi Indexed By


MEMBER OF


Dedicated to :

Djtechno: Jurnal Teknologi Informasi published by :

PROGRAM STUDI TEKNOLOGI INFORMASI UNIVERSITAS DHARMAWANGSA

Alamat : Jl. K. L. Yos Sudarso No. 224 Medan
Kontak : Tel. 061 6635682 - 6613783  Fax. 061 6615190
Surat Elektronik : s1.ti@dharmawangsa.ac.id

Djtechno: Jurnal Teknologi Informasi

Ciptaan disebarluaskan di bawah Creative Commons Attribution-ShareAlike 4.0 International License