Classification of Customer Opinions on the Quality of Cooperative Minimarket Services Using the Lexicon Approach
Authors
-
Muhammad Sholeh
Universitas AKPRIND, Yogyakarta, Indonesia
https://orcid.org/0000-0002-6366-7166
-
Uniing Lestari Sholeh
Universitas AKPRIND, Yogyakarta, Indonesia
-
Dina Andayati Sholeh
Universitas AKPRIND, Yogyakarta, Indonesia
DOI:
https://doi.org/10.30812/ijecsa.v5i1.6172Keywords:
Sentiment Analysis, Minimarket, Comments, Lexicon, logistic regressionAbstract
Almost every strategic location today has a store that sells daily necessities. These stores compete with each other by offering prices and services that they hope will satisfy their customers. This competition must be anticipated in the management of minimarkets run by cooperatives. Minimarkets run by cooperatives need to maintain the loyalty of their members and general customers by improving service quality. Customer reviews or suggestions and criticism from members or customers are valuable sources of data for evaluating service performance. These customer reviews are unstructured data that are difficult to process manually. This study aims to classify customer opinions on the service quality of cooperative minimarkets into positive, negative, and neutral sentiments using a Lexicon-Based approach. The research methods used are text data preprocessing, sentiment weighting using a lexicon dictionary, classification into positive, negative, or neutral classes, and system performance testing using a confusion matrix. The data labeling stage is carried out automatically using the Lexicon InSet dictionary to determine the sentiment class (positive or negative). The labeled data was then processed using TF-IDF feature extraction and used to train the logistic regression model. Model performance evaluation was carried out using a Confusion Matrix with a training data and test data ratio of 80:20. The results of this study show that the logistic regression algorithm is capable of classifying cooperative service sentiment with an accuracy rate of 81%, precision of 83%, recall of 81%, and an F1 score of 79%. These results indicate that the method used is quite effective in identifying customer opinions and can be used as a decision support system for cooperative managers in continuously improving service quality based on customer sentiment data analysis.
Downloads
References
[1] A. Sundari and A. Y. Syaikhudin, Manajemen Ritel (Teori dan Strategi dalam Bisnis Ritel). Lamongan: Academia Publication, 2021.
[2] A. Wibowo, Manajemen Bisnis Retail. Semarang: Yayasab Prima Agus Teknik, 2021.
[3] Christina Whidya Utami, Manajemen Ritel: Strategi dan Implementasi Operasional Bisnis Ritel Modern di Indonesia. Jakarta: Penerbit Salemba Empat, 2017.
[4] D. Purnamasari et al., Pengantar Metode Analisis Sentimen. Jakarta: Penerbit Gunadarma, 2023.
[5] Asrumi, D. Suharijadi, A. D. Setiari, and D. P. Wulanda, Analisis Sentimen dan Penggalian Opini. Purbalingga: Eureka Media Aksara, 2023.
[6] Y. Yunitasari, Teori Dan Implementasi Analisis Sentimen Menggunakan Python. Madiub: UNIPMA Press (, 2023.
[7] S. A. S. Mola, R. V. K. I. O. Roma, and T. Widiastuti, Text Mining Analisis Sentimen dengan Lexicon. Bandung: Kaizen Media Publishing, 2025.
[8] F. Rachmawati, U. Azmi, and R. Azwarini, “Comparison of Lexicon-based Methods and Bidirectional Encoder Representations for Transformers Models in Sentiment Analysis of Government Debt Market Movements,” vol. 4, no. 1, pp. 13–28, 2025, doi: doi.org/10.30812/ijecsa.v4i1.4832.
[9] M. Rodríguez-Ib´anez, A. Cas´anez-Ventura, F. Castej´on-Mateos, and P.-M. Cuenca-Jim´enez, “A review on sentiment analysis from social media platforms Margarita Rodríguez-Ib a,” vol. 223, no. August 2022, 2023, doi: 10.1016/j.eswa.2023.119862.
[10] A. Faesal, A. Muslim, and A. H. Ruger, “Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,” vol. 19, no. 2, pp. 207–213, 2020, doi: https://doi.org/10.30812/matrik.v19i2.640 Sentimen.
[11] T. Puspa, R. Sanjaya, A. Fauzi, A. Fitri, and N. Masruriyah, “Analisis sentimen ulasan pada e-commerce shopee menggunakan algoritma naive bayes dan support vector machine Analysis of review sentiment on shopee e-commerce using the naive bayes algorithm and support vector machine,” vol. 4, pp. 16–26, 2023, doi: 10.37373/infotech.v4i1.422.
[12] T. Kemal et al., “Penerapan Naive Bayes Untuk Analisis Sentimen Pada Ulasan Pelanggan Di LazadaStud kasus Toko Mawar Collection,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 2, pp. 997–1003, 2025, doi: dx.doi.org/10.23960/jitet.v13i2.6391 PENERAPAN.
[13] A. P. Adhi, K. Umuri, G. Triyono, F. T. Informasi, M. I. Komputer, and U. B. Luhur, “Sentiment Analysis And Entity Detection On News Headlines To Support Investment Decisions,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 6, pp. 1801–1810, 2024, doi: doi.org/10.52436/1.jutif.2024.5.6.3434.
[14] M. Syawaluddin and S. Susandri, “Sentiment Classification of Customer Feedback in Indonesian : A Case Study of Global Bangunan Using SVM and Naive Bayes,” in Prosiding Semnas 2025 Sekolah Tinggi Teknologi Dumai, 2025, pp. 416–431.
[15] D. A. Andriati, R. Laple, S. Putra, and G. D. Saputra, “Analisis Sentimen Komentar Pengguna Aplikasi Klik Indomaret Di Google Playstrore Menggunakan Metode Support Vector Machine ( SVM ),” Jatilima : Jurnal Multimedia Dan Teknologi Informasi, vol. 07, no. 03, pp. 663–673, 2025, doi: doi.org/ 10.54209/jatilima.v7i03.1653.
[16] A. Karim and S. F. C, “Analisis Sentimen pada Komentar Sosial Media Instagram Layanan Kesehatan BPJS Menggunakan Naïve Bayes Classifier,” in Prosiding Seminar Nasional Konstelasi Ilmiah Mahasiswa UNISSULA 7 (KIMU 7), 2022, pp. 61–70.
[17] M. Hasan, S. Kurniawan, and H. L. Nisa, “Analisis Sentimen Terhadap Komentar Aplikasi Allstats BPS Dengan Klasifikasi Naïve Bayes,” Emerging Statistics and Data Science Journal, vol. 2, no. 3, pp. 315–329, 2024, doi: 10.20885/esds.vol1.iss.2.art22.
[18] F. Faturohman, M. Julaeha, and I. Kartini, “Analisis Bauran Pemasaran Pada Toko Sembako ( Studi Kasus Pada Toko Sembako di Desa Sukasirna ),” INNOVATIVE: Journal Of Social Science Research, vol. 5, pp. 1104–1114, 2025, doi: https://doi.org/10.31004/innovative.v5i3.19121.
[19] S. Nafisyah and R. Sulistiyowati, “Analisis Sentimen Ulasan Produk Toko Online Esrocte Untuk Peningkatan Pelayanan Menggunakan Algoritma Naïve,” Blantika: Multidisciplinary Jornal, vol. 2, no. 8, pp. 846–852, 2024, doi: https://doi.org/10.57096/blantika.v2i8.189.
[20] K. Neighbor and N. K. Lingga, “Analisis Sentimen Nasabah Pada Kotak Komentar Terhadap Pelayanan Koperasi Simpan Pinjam Bangun Mandiri Mengunakan Metode,” JIKTEKS: Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 02, no. 02, pp. 47–59, 2024, doi: doi.org/10.70404/jikteks.v2i02.74.
[21] S. Winardi, S. Megawan, N. P. Wong, R. Kurniawan, and F. A. Putra, “Utilizing TF-IDF Content-based Filtering for Job Recommendation Systems,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 8, no. 5, pp. 2838–2845, 2025, doi: https://doi.org/10.32672/jnkti.v8i5.9686.
[22] S. Winardi, S. Megawan, N. P. Wong, R. Kurniawan, and F. A. Putra, “Utilizing TF-IDF Content-based Filtering for Job Recommendation Systems,” vol. 8, no. 5, pp. 2838–2845, 2025.
[23] urya K. S, Irma Idris, Y. A. Mustofa, and I. A. Salih, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine ( SVM ),” Jambura Journal of Electrical and Electronics Engineering, vol. 5, no. 1, pp. 32–35, 2023, doi: https://doi.org/10.37905/jjeee.v5i1.16830.
[24] A. R. Putra and D. E. Ratnawati, “Analisis Sentimen Berbasis Aspek Pada Aplikasi Mobile Menggunakan Naïve Bayes Berdasarkan Ulasan Pengguna Playstore ( Studi Kasus : Jconnect Mobile ) Aspect-Based Sentiment Analysis On Mobile Applications Using Naïve Bayes Based On Playstore User Reviews,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 12, no. 2, pp. 293–300, 2025, doi: 10.25126/jtiik.2025127556.
[25] I. Sanu, J. Wong, and H. Irsyad, “Implementasi TF-IDF , Cosine Similarity , dan Logistic Regression Pada Rekomendasi Buku Berdasarkan Mood Pembaca Dengan Data Oversampling,” Journal Of Information System, Computer Science And Information Technology, vol. 6, no. 1, pp. 142–154, 2025, doi: https://doi.org/10.46576/device.v6i1.6499.
[26] H. Putra and Rumini, “Comparative Study of Logistic Regression , Random Forest , and XGBoost for Bank Loan Approval Classification,” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 5, pp. 2822–2835, 2025, doi: https://doi.org/10.30871/jaic.v9i5.10862.
[27] F. Madani and A. H. Lubis, “CatBoost Algorithm Implementation for Classifying Women’s Fashion Products,” JITE (Journal of Informatics and Telecommunication Engineering) Available, vol. 9, no. July, pp. 249–260, 2025, doi: 10.31289/jite.v9i1.15604.
[28] N. Agustina, D. H. Citra, W. Purnama, C. Nisa, and A. R. Kurnia, “The Implementation of Naïve Bayes Algorithm for Sentiment Analysis of Shopee Reviews on Google Play Store Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, no. April, pp. 47–54, 2022, doi: https://doi.org/10.57152/malcom.v2i1.195.
[29] F. Chahyadi, A. Uperiati, R. Absari, I. Pratiwi, and N. Hamid, “Evaluating Lexicon Weighting and Machine Learning Models for Sentiment Classification of Indonesian Mangrove Ecotourism Reviews,” Jurnal Teknik Informatika (JUTIF), vol. 6, no. 6, pp. 5679–5698, 2025, doi: doi.org/10.52436/1.jutif.2025.6.6.5563 Evaluating.
[30] A. Okta, K. Adi, A. Sanjaya, and A. B. Setiawan, “Penerapan Inset Lexicon untuk Analisis Sentimen Penonton Video JKT48 di YouTube,” in Seminar Nasional Inovasi Teknologi), 2025, pp. 1276–1283.
[31] S. Anggina, N. Y. Setiawan, and F. A. Bachtiar, “Analisis Ulasan Pelanggan Menggunakan Multinomial Naïve Bayes Classifier dengan Lexicon-Based dan TF-IDF Pada Formaggio Coffee and Resto,” Accounting Information Systems and Information Technology Business Enterprise, vol. 7, pp. 76–90, 2022, doi: https://doi.org/10.34010/aisthebest.v7i1.7072.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Sholeh, Uniing Lestari Sholeh, Dina Andayati Sholeh
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
