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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Monica Cinthya
Universitas Negeri Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Public Opinion on MyTelkomsel Using DeLone and McLean Model on X Bagas Setya Wicaksono; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78043

Abstract

The MyTelkomsel application is a digital service used by Telkomsel customers to access telecommunications information and services. The high number of users is accompanied by the emergence of various user opinions and complaints expressed through social media. This study aims to analyze user satisfaction with the MyTelkomsel application based on public opinions on the X (Twitter) platform using the DeLone and McLean Information Systems Success Model. The research data consist of 1,500 Indonesian-language tweets collected through a crawling process. The data then underwent a text preprocessing stage to improve analysis quality. Sentiment analysis was conducted using the RoBERTa model to classify user opinions into positive, neutral, and negative sentiments. Subsequently, each tweet was labeled into six dimensions of the DeLone and McLean model, namely System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits. Sentiment scores were used as quantitative values for each dimension. The relationships among variables were analyzed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method. The results indicate that System Quality and Information Quality significantly influence User Satisfaction, while Service Quality shows a lower level of influence. This study is expected to provide academic contributions to the application of the DeLone and McLean model based on social media data and offer practical insights for the development of the MyTelkomsel application in improving service quality and user experience. Keywords : MyTelkomsel, Sentiment Analysis, Social Media, DeLone and McLean, User Satisfaction, SEM-PLS
Sentiment Analysis And UTAUT2 Classification On Maxim Application User Reviews Using IndoBERT And Zero-Shot Hilal Hindi Saputra; Cendra Devayana Putra; I Kadek Dwi Nuryana; Monica Cinthya
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78304

Abstract

The rapid growth of ride-hailing services has intensified competition, making user feedback on digital platforms a critical asset for service improvement. This study addresses the challenge of managing and extracting actionable insights from large volumes of unstructured user reviews on the Google Play Store for the Maxim application. To overcome this, a comprehensive text-mining framework is proposed, integrating sentiment analysis and technology acceptance modeling. A dataset of 2.000 Indonesian-language user reviews from July to September 2025 was retrieved via web scraping. Data preprocessing was executed using case folding, filtering, and normalization. Subsequently, sentiment classification was performed using the IndoBERT model, while the mapping of user text to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework was automated using a Zero-Shot Classification approach. Finally, Structural Equation Modeling–Partial Least Squares (SEM-PLS) via SmartPLS 4.0 was utilized to test the structural hypotheses. The analytical findings reveal that negative sentiments slightly dominate the dataset (48.05%), heavily driven by system stability and sudden fare adjustments. Furthermore, the structural model proves that behavioral intention, effort expectancy, facilitating conditions, habit, performance expectancy, price value, and social influence exert positive and significant effects on adoption, whereas hedonic motivation exhibits no significant influence.
Analisis Sentimen Performansi Operator Telekomunikasi di Indonesia Menggunakan Metode Text Mining Ersha Aisyah Elfaiz; Riza Akhsani Setyo Prayoga; Monica Cinthya; Muhammad Sonhaji Akbar; Rizky Basatha
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.4024

Abstract

The telecommunications sector in Indonesia has experienced rapid development in recent years, characterized by the increasing number of telecommunications operators offering various services and products. Therefore, there is competitive rivalry among operators. The right strategy is needed to survive and compete effectively. One of the efforts that can be made by telecommunication companies is to evaluate operational performance. This research aims to analyze the sentiment of X users towards telecommunication operational performance, at Telkomsel and Tri operators using text mining methods, namely Naïve Bayes, Support Vector Machine (SVM) and Decision Tree Learning (DTL). The research data is obtained by crawling from the X application, then the data is processed to remove unnecessary words or affixes. Then data modeling and validation is carried out using split validation and cross validation techniques. In the split validation technique, the data is divided into 70% training data and 30% testing data, while in the cross-validation technique the fold parameter is set to determine which fold has the highest accuracy. The results of the study show that the SVM method has the highest accuracy, where in split validation the accuracy is 84.29% for Telkomsel data and 75.70% for Tri data. Similarly, in cross validation, the accuracy is 82.15% on fold 4, 7 for Telkomsel data and 61.41% on fold 9 for Tri data. In addition, it is known that Telkomsel data has 18.64% positive sentiment and 81.36% negative sentiment. While Tri data has 61.11% positive sentiment and 38.89% negative sentiment.
Deteksi Hate Speech Unsur Sara Pada Komentar Media Sosial Menggunakan Pendekatan Two-Stage Classification Dengan Algoritma Indobert Dan Support Vector Machine Ovy Marsya Zieera; Monica Cinthya
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 4 (2026): IDENTIK - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i4.1698

Abstract

The rapid development of social media in Indonesia has increased public interaction on platforms such as YouTube, Instagram, and TikTok. However, this has also driven the proliferation of hate speech, particularly content containing elements of (SARA). This study proposes a Two-Stage Classification approach to address this challenge. In the first stage, the IndoBERT model (indobenchmark/indobert-base-p1) is fine-tuned to classify comments into Hate Speech and Non-Hate Speech. In the second stage, Support Vector Machine (SVM) with TF-IDF feature extraction and a custom SARA lexicon is used to further classify hate speech comments into SARA-based hate speech (HS_SARA) and general hate speech (HS_Umum). The dataset consists of 36,000 comments scraped from YouTube, Instagram, and TikTok on viral SARA-related topics. Data labeling was conducted using LLM Ensemble Voting involving three AI models followed by validation by three human annotators. The results show that IndoBERT in Stage 1 achieved an accuracy of 82.56% on the test set. In Stage 2, the SVM model achieved an accuracy of 95.07%, precision of 95.31%, recall of 95.07%, and F1-score of 95.07%, with cross-validation confirming stability at a mean accuracy of 96.74% (std = 0.19%). These findings demonstrate that the Two-Stage Classification approach effectively improves the specificity of hate speech detection by separating tasks in a sequential manner.
Co-Authors Bagas Setya Wicaksono Cendra Devayana Putra Ersha Aisyah Elfaiz Hilal Hindi Saputra I Kadek Dwi Nuryana Muhammad Sonhaji Akbar Ovy Marsya Zieera Riza Akhsani Setyo Prayoga Rizky Basatha