Sentiment-Based Knowledge Discovery pada Aplikasi iPusnas Menggunakan Metode Machine Learning dan Deep Learning
DOI:
https://doi.org/10.30871/jaic.v9i5.10258Keywords:
Sentiment Analysis, Deep Learning, iPusnas, Knowledge Discovery, Machine LearningAbstract
iPusnas is a digital library application developed by the National Library of the Republic of Indonesia since 2016, with over 1.5 million users. Despite its potential to improve literacy, the application has only received a rating of 2.0. This study conducted sentiment analysis on 7.596 reviews obatained through web scraping using the Google Play Scraper Library. The data then underwent preprocessing steps including case folding, data cleaning, tokenization, stopword removal, and stemming. Reviews were automatically labeled based on the rating score, where scores of 1-3 were categorized as negative, with 5.174 entries, and scores 4-5 as positive, with 2.422 entries. The dataset was split in an 80:20 ratio, with 80% for training, and 20% for testing. The machine learning models tested were SVM, Random Forest, CNN, LSTM, and RNN. The evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. CNN and LSTM achieved the highest accuracy (82%), Random Forest and CNN achieved the highest precision (81%), RNN the highest recall (79%) and LSTM the highest F1-score (79%). McNemar test showed a significant difference between Random Forest and CNN, Random Forest and LSTM, and between RNN and LSTM, while CNN and LSTM, as well as CNN and RNN, showed no significant difference.
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References
[1] D. Ilmu, P. Uin, and R. F. Palembang, “Penerapan Strategi Promosi Perpustakaan Wahfiuddin Rahmad Harahap,” 2021.
[2] R. Indonesia, Dewan Perwakilan Rakyat Republik Indonesia dan Presiden Republik Indonesia. Indonesia, 2007, p. 129.
[3] S. Anjani and Y. Winoto, “Pemetaan Publikasi Ilmiah Tentang Perpustakaan Digital Tahun 2011-2021 Melalui Aplikasi VOSViewer,” Jurnal Ilmu Perpustakaan (JIPER), vol. Vol.4, No.2, 2022.
[4] S. Taryani and L. Wijayanti, “Pengukuran Kualitas Layanan Aplikasi Ipusnas Terhadap Kepuasan Pengguna Dengan Menggunakan Metode Webqual 4.0,” VISI PUSTAKA, vol. Vol. 25 No. 1, 2023.
[5] A. Septiani and I. Budi, “Klasifikasi Ulasan Pengguna Aplikasi: Studi Kasus Aplikasi Ipusnas Perpustakaan Nasional Republik Indonesia (PNRI),” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. Vol. 07, 2022.
[6] F. Sena Lestari, M. Maariful Huda, T. Prabowo, and I. Komputer, “Sentiment Analysis of iPusnas Application Reviews on Google Play Using Support Vector Machine,” The Changing Role of Knowledge and Living Sustainability in ASEAN Community, 2022.
[7] K. L. Tan, C. P. Lee, and K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Applied Sciences (Switzerland), vol. 13, no. 7, Apr. 2023, doi: 10.3390/app13074550.
[8] Q. Ain, E. Utami, and A. Nasiri, “Analisis Sentimen: Prediksi Rating Terhadap Reviews Wisatawan Tanjung Puting Pada Tripadvisor Menggunakan Support Vector Machine,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1586–1595, Aug. 2024, doi: 10.29100/jipi.v9i3.5430.
[9] C. Llatas, B. Soust-Verdaguer, L. C. Torres, and D. Cagigas, “Application of Knowledge Discovery in Databases (KDD) to environmental, economic, and social indicators used in BIM workflow to support sustainable design,” Journal of Building Engineering, vol. 91, Aug. 2024, doi: 10.1016/j.jobe.2024.109546.
[10] V. Novalia, K. Ditha Tania, A. Meiriza, and A. Wedhasmara, “Knowledge Discovery of Application Review Using Word Embedding’s Comparison with CNN-LSTM Model on Sentiment Analysis,” in ICECOS 2024 - 4th International Conference on Electrical Engineering and Computer Science, Proceeding, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 234–238. doi: 10.1109/ICECOS63900.2024.10791113.
[11] N. A. Sofiah, K. D. Tania, A. Meiriza, and A. Wedhasmara, “A Comparative Assessment SARIMA and LSTM Models for the Gurugram Air Quality Index’s Knowledge Discovery,” in ICECOS 2024 - 4th International Conference on Electrical Engineering and Computer Science, Proceeding, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 26–31. doi: 10.1109/ICECOS63900.2024.10791243.
[12] S. Mazya Permataning Tyas, R. Sarno, and B. Setya Rintyarna, “Analisis Perbandingan Metode Klasifikasi Sentimen Berita Saham: Pendekatan Machine Learning, Deep Learning, Transfer Learning, dan Graf,” Jurnal Penelitian Ipteks, vol. 9, no. 1, pp. 58–64, 2024, doi: https://doi.org/10.32528/penelitianipteks.v9i1.1479.
[13] S. A. Sutresno, “Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 4, Mar. 2023, doi: 10.47065/bits.v4i4.3149.
[14] F. I. Rafif, M. D. Purbolaksono, and W. Astuti, “Sentiment Analysis using Random Forest and Word2Vec for Indonesian Language Movie Reviews,” Jurnal Media Informatika Budidarma, vol. 7, no. 3, p. 1109, Jul. 2023, doi: 10.30865/mib.v7i3.6299.
[15] N. M. K. Sedana, I. N. S. W. Wijaya, and I. K. R. Arthana, “Analisis Sentimen Berbahasa Inggris Dengan Metode Lstm Studi Kasus Berita Online Pariwisata Bali,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1325–1334, Dec. 2024, doi: 10.25126/jtiik.2024118792.
[16] Y. Yuliska, D. Hidayatul Qudsi, J. Hakim Lubis, K. Umam Syaliman, and N. Fadilah Najwa, “Analisis Sentimen Pada Data Saran Mahasiswa Terhadap Kinerja Departemen Di Perguruan Tinggi Menggunakan Convolutional Neural Network,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. Vol. 8, 2021, doi: 10.25126/jtiik.202184842.
[17] P. Mukherjee, Y. Badr, S. Doppalapudi, S. M. Srinivasan, R. S. Sangwan, and R. Sharma, “Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 370–379. doi: 10.1016/j.procs.2021.05.038.
[18] A. U. El Majid and R. Nuari, “Perbandingan Kinerja Metrik Bert Dan Model Machine Learning Klasik (Svm, Naive Bayes) Untuk Analisis Sentimen,” Jurnal Infotek Polbeg, vol. 10, no. 2, 202AD.
[19] B. Z. Ramadhan, I. Riza, and I. Maulana, “Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Dengan Menggunakan Algoritma Naïve Bayes,” 2022. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[20] Herwinsyah and A. Witanti, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Jurnal Sistem Informasi dan Informatika (Simika) P-ISSN, vol. 5, pp. 2622–6901, 2022.
[21] S. Wu, Y. Liu, Z. Zou, and T. H. Weng, “S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis,” Conn Sci, vol. 34, no. 1, pp. 44–62, 2022, doi: 10.1080/09540091.2021.1940101.
[22] Styawati, N. Hendrastuty, A. Raahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” Jurnal Informatika: Jurnal pengembangan IT (JPIT), vol. 6, no. 3, 2021, doi: https://doi.org/10.30591/jpit.v6i3.2870.
[23] H. T. Ismet, T. Mustaqim, and D. Purwitasari, “Aspect Based Sentiment Analysis of Product Review Using Memory Network,” Scientific Journal of Informatics, vol. 9, no. 1, pp. 73–83, May 2022, doi: 10.15294/sji.v9i1.34094.
[24] O. I. Gifari, M. Adha, I. Rifky Hendrawan, F. Freddy, and S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” JIFOTECH (Journal Of Information Technology, vol. 2, no. 1, 2022.
[25] H. T. Duong and T. A. Nguyen-Thi, “A review: preprocessing techniques and data augmentation for sentiment analysis,” Comput Soc Netw, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40649-020-00080-x.
[26] A. Bijaksana, P. Negara, H. Muhardi, and I. M. Putri, “Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes Dan Seleksi Fitur Information Gain Sentiment Analysis On Airlines Using Naïve Bayes Method And Feature Selection Information Gain,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 7, no. 3, pp. 599–606, 2020, doi: 10.25126/jtiik.202071947.
[27] D. A. Kristiyanti and S. Hardani, “Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM),” Jurnal RESTI, vol. 7, no. 3, pp. 722–732, Jun. 2023, doi: 10.29207/resti.v7i3.4737.
[28] M. A. Raihan and E. B. Setiawan, “Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter,” Jurnal RESTI, vol. 6, no. 4, pp. 591–598, 2022, doi: 10.29207/resti.v6i4.4187.
[29] A. Erfina and M. R. N. R. Alamsyah, “Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language,” Data and Metadata, vol. 2, Jan. 2023, doi: 10.56294/dm202345.
[30] N. D. Kurniawan, P. R. Ferdian, and N. Hidayati, “Analisis Sentimen Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Pada Ulasan Aplikasi Ajaib,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 11, no. 1, pp. 87–97, May 2025, doi: 10.25077/teknosi.v11i1.2025.87-97.
[31] F. Nufairi, N. Pratiwi, and F. Herlando, “Analisis Sentimen Pada Ulasan Aplikasi Threads Di Google Play Store Menggunakan Algoritma Support Vector Machine,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 1, pp. 339–348, Feb. 2024, doi: 10.29100/jipi.v9i1.4929.
[32] J. J. Tono and P. Parjito, “Persepsi Publik Terhadap Kepemimpinan Firli Bahuri Di Kpk: Pendekatan Sentimen Twitter Dengan Naïve Bayes Dan SVM,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 2, pp. 1272–1285, Mar. 2025, doi: 10.29100/jipi.v10i2.6181.
[33] D. A. Musleh et al., “Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation,” Big Data and Cognitive Computing, vol. 7, no. 3, Sep. 2023, doi: 10.3390/bdcc7030127.
[34] T. Chen, X. Yin, L. Peng, J. Rong, J. Yang, and G. Cong, “Monitoring and recognizing enterprise public opinion from high-risk users based on user portrait and random forest algorithm,” Axioms, vol. 10, no. 2, Jun. 2021, doi: 10.3390/axioms10020106.
[35] J. Asian, M. D. Rosita, and T. Mantoro, “Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods,” Jurnal Online Informatika, vol. 7, no. 1, pp. 132–141, Jun. 2022, doi: 10.15575/join.v7i1.900.
[36] D. Purnamasari, A. B. Aji, S. Madenda, I. M. Wiryana, and S. Harmanto, “Sentiment Analysis Methods For Customer Review Of Indonesia E-Commerce,” International Journal of Innovative Computing, Information and Control, vol. 20, no. 1, pp. 47–60, Feb. 2024, doi: 10.24507/ijicic.20.01.47.
[37] M. Kamyab, G. Liu, and M. Adjeisah, “Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis,” Applied Sciences (Switzerland), vol. 11, no. 23, Dec. 2021, doi: 10.3390/app112311255.
[38] L. Xiaoyan, R. C. Raga, and S. Xuemei, “GloVe-CNN-BiLSTM Model for Sentiment Analysis on Text Reviews,” J Sens, vol. 2022, 2022, doi: 10.1155/2022/7212366.
[39] A. Z. R. Adam and E. B. Setiawan, “Social Media Sentiment Analysis Using Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU),” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 1, pp. 119–131, Feb. 2023, doi: 10.26555/jiteki.v9i1.25813.
[40] R. Bharal and O. V Vamsi Krishna, “Social Media Sentiment Analysis Using CNN-BiLSTM,” International Journal of Science and Research (IJSR), vol. 10, no. 9, pp. 656–661, Sep. 2021, doi: 10.21275/sr21913110537.
[41] B. A. Pramono, A. Firman Daru, and M. B. Ulum, “Twitter Sentiment Analysis Using Natural Language Processing (NLP) Method and Long Short Term Memory (LSTM) Algorithm in the 2024 Indonesian Presidential Election,” 2024. [Online]. Available: http://ejournal.uksw.edu/ijiteb
[42] K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, “RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network,” IEEE Access, vol. 10, pp. 21517–21525, 2022, doi: 10.1109/ACCESS.2022.3152828.
[43] K. Mrhar, L. Benhiba, S. Bourekkache, and M. Abik, “A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs,” International Journal of Emerging Technologies in Learning, vol. 16, no. 23, pp. 216–232, 2021, doi: 10.3991/ijet.v16i23.24457.
[44] S. Imron, E. I. Setiawan, J. Santoso, and M. H. Purnomo, “Aspect Based Sentiment Analysis Marketplace Product Reviews Using BERT, LSTM, and CNN,” Jurnal RESTI, vol. 7, no. 3, pp. 586–591, Jun. 2023, doi: 10.29207/resti.v7i3.4751.
[45] M. H. Abdalla et al., “Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm,” IEEE Access, vol. 11, pp. 134087–134099, 2023, doi: 10.1109/ACCESS.2023.3334980.
[46] P. K. Sahu and T. Fatma, “Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies with Optuna Optimization,” IEEE Access, vol. 13, pp. 35645–35661, 2025, doi: 10.1109/ACCESS.2025.3539746.
[47] L. G. Atlas et al., “A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-01104-0.
[48] I. G. S. Mas Diyasa, N. M. I. Marini Mandenni, M. I. Fachrurrozi, S. I. Pradika, K. R. Nur Manab, and N. R. Sasmita, “Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob,” IOP Conf Ser Mater Sci Eng, vol. 1125, no. 1, p. 012034, May 2021, doi: 10.1088/1757-899x/1125/1/012034.
[49] B. Su and J. Peng, “Sentiment Analysis of Comment Texts on Online Courses Based on Hierarchical Attention Mechanism,” Applied Sciences (Switzerland), vol. 13, no. 7, Apr. 2023, doi: 10.3390/app13074204.
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