Email this article 
(Universitas Islam Indonesia, Indonesia)(2) * Imam Djati Widodo
(Universitas Islam Indonesia, Indonesia)(3) Nisrina Faiza Mufid
(Universitas Islam Indonesia, Indonesia)(4) Qurtubi Qurtubi
(Universitas Islam Indonesia, Indonesia)*corresponding author
AbstractPT ABC is one of the companies that provide online ticket-purchasing facilities amidst the rise of the digitalization era. So, companies need to see how application users complain as a form of evaluation and improvement. The rating results given by application users show a score of 3.3 from 172,000 reviews. The review results that will be examined are user reviews from January 2022 to April 1, 2023, which is more or less the last year of user comments. This research aims to form a review group using K-Means Clustering, the Elbow method, TF-IDF weighting, and analysis of review improvement strategies. The Elbow method is used to determine the optimal number of clusters so as not just to use assumptions. The success of the Elbow method in processing categorical data can be supported by assigning weights based on word frequency sequences using TF-IDF. The research analysis results show the formation of 4 clusters, with two tending to have negative sentiment, one neutral sentiment, and one positive sentiment. Mapping is carried out on each cluster to find out the characteristics of each cluster and possible causes of reviews, as well as providing solutions and strategies as a form of improvement. The problem of negative reviews appearing in each review group is different. It can be corrected with the proposed strategies, such as improving the appearance of features at the registration, ordering, and payment stages, adding payment methods, and carrying out regular system maintenance.
KeywordsCategorical Data; Elbow Method; Sentiment Analysis
|
DOIhttps://doi.org/10.29099/ijair.v7i2.1062 |
Article metrics10.29099/ijair.v7i2.1062 Abstract views : 474 | PDF views : 152 |
Cite |
Full Text Download
|
References
W. Wijanarto and S. P. Brilianti, “Peningkatan Performa Analisis Sentimen dengan Resampling dan Hyperparameter pada Ulasan Aplikasi BNI Mobile,” Jurnal Eksplora Informatika, vol. 9, no. 2, pp. 140–153, Mar. 2020, doi: 10.30864/eksplora.v9i2.333.
H. -, A. Y. Kuntoro, and T. Asra, “Klasifikasi Keluhan Pengguna KAI Access untuk Pemesanan Tiket dengan Algoritma SVM dan Naive Bayes,” JIKA (Jurnal Informatika), vol. 6, no. 2, pp. 161–169, Jun. 2022, Accessed: Dec. 13, 2023. [Online]. Available: https://jurnal.umt.ac.id/index.php/jika/article/view/6187
H. Mustakim and S. Priyanta, “Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 2, p. 113, Apr. 2022, doi: 10.22146/ijccs.68903.
K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 Tweets by Deep Learning Classifiers—A Study to Show How Popularity is Affecting Accuracy in Aocial Media,” Applied Soft Computing Journal, vol. 97, Dec. 2020, doi: 10.1016/j.asoc.2020.106754.
G. Punj and D. W. Stewart, “Cluster Analysis in Marketing Research: Review and Suggestions for Application.”
D. A. Miles, “A Taxonomy of Research Gaps: Identifying and Defining the Seven Research Gaps,” 2017.
P. Bholowalia and A. Kumar, “EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN,” Int J Comput Appl, vol. 105, no. 9, pp. 975–8887, 2014.
C. T. Yu, K. Lam, and G. Salton, “Term Weighting in Information Retrieval Using the Term Precision Model,” 1982.
V. Gurusamy, S. Kannan, and A. Professor, “Preprocessing Techniques for Text Mining,” 2014. [Online]. Available: https://www.researchgate.net/publication/273127322
D. Munková, M. Munk, and M. Vozár, “Data Pre-Processing Evaluation for Text Mining: Transaction/Sequence Model,” in Procedia Computer Science, Elsevier B.V., 2013, pp. 1198–1207. doi: 10.1016/j.procs.2013.05.286.
N. Z. Dina, R. Triwastuti, and M. Silfiani, “TF-IDF Decision Matrix to Measure Customers’ Satisfaction of Ride Hailing Mobile Application Services: Multi-Criteria Decision-Making Approach,” International Journal of Interactive Mobile Technologies, vol. 15, no. 17, pp. 104–118, 2021, doi: 10.3991/ijim.v15i17.22509.
T. Soni Madhulatha, “An Overview on Clustering Methods,” vol. 2, no. 4, pp. 719–725, 2012, Accessed: Dec. 13, 2023. [Online]. Available: www.iosrjen.org
C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm,” EURASIP J Wirel Commun Netw, vol. 2021, no. 1, Dec. 2021, doi: 10.1186/s13638-021-01910-w.
R. Dubes and A. K. Jain, “Validity Studies in Clustering Methodologies,” Pattern Recognit, vol. 11, no. 4, pp. 235–254, 1979.
Z. Huang, “Clustering Large Data Sets with Mixed Numeric and Categorical Values,” in In The First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1997. [Online]. Available: https://api.semanticscholar.org/CorpusID:3007488
M. Del Pero, “The Importance of Strategic Planning,” Reinforced Plastics, vol. 57, no. 2, pp. 16–18, 2013, doi: 10.1016/S0034-3617(13)70054-7.
N. Singh and N. Sinha, “How Perceived Trust Mediates Merchant’s Intention to use a Mobile Wallet Technology,” Journal of Retailing and Consumer Services, vol. 52, Jan. 2020, doi: 10.1016/j.jretconser.2019.101894.
W. T. Wang, W. M. Ou, and W. Y. Chen, “The Impact of Inertia and User Satisfaction on the Continuance Intentions to Use Mobile Communication Applications: A Mobile Service Quality Perspective,” Int J Inf Manage, vol. 44, pp. 178–193, Feb. 2019, doi: 10.1016/j.ijinfomgt.2018.10.011.
A. K. Kar, “What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the ‘Digital Service Usage Satisfaction Model,’” Information Systems Frontiers, vol. 23, no. 5, pp. 1341–1361, Sep. 2021, doi: 10.1007/s10796-020-10045-0.
O. Tounekti, A. Ruiz-Martinez, and A. F. Skarmeta Gomez, “Users Supporting Multiple (Mobile) Electronic Payment Systems in Online Purchases: An Empirical Study of Their Payment Transaction Preferences,” IEEE Access, vol. 8, pp. 735–766, 2020, doi: 10.1109/ACCESS.2019.2961785.
A. Subiyantoro, R. Dwi Astuti, H. Agung Nugroho, A. Manajemen Administrasi Yogyakarta, and U. Janabadra Yogyakarta, “Pengaruh Strategi Pemasaran, Pelayanan dan Promositerhadap Keputusan Pembelian Tiket Kereta Api,” 2022. [Online]. Available: http://Jurnal.amayogyakarta.ac.id/index.php/albama
T. F. Abdelzaher and N. Bhatti, “Web Content Adaptation to Improve Server Overload Behavior,” 1999.
F. Alqahtani and R. Orji, “Insights From User Reviews to Improve Mental Health Apps,” Health Informatics J, vol. 26, no. 3, pp. 2042–2066, Sep. 2020, doi: 10.1177/1460458219896492.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.