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All Journal JDM (Jurnal Dinamika Manajemen) Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal MIX : Jurnal Ilmiah Manajemen Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) CENDEKIA : Jurnal Ilmu Pengetahuan BISMA (Bisnis dan Manajemen) Makara Human Behavior Studies in Asia ASEAN Marketing Journal Inkubis: Jurnal Ekonomi dan Bisnis Eduvest - Journal of Universal Studies
Widianto, Sunu
Unknown Affiliation
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Neuroscience Social Sciences

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Examining Characteristics on Twitter Users’ Text and Hashtag Utilization During Tech Winter Layoff Post-COVID-19 Using LDA and K-Means Clustering Approach F., Fery; Widianto, Sunu
Makara Human Behavior Studies in Asia Vol. 27, No. 2
Publisher : UI Scholars Hub

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Abstract

Post-COVID-19 pandemic has significantly impacted the global economy, resulting in a surge of job losses and layoffs across various industries, including the technology sector. The pandemic has led to changes in consumer behavior, supply chain disruptions, and an overall decrease in demand, all of which have contributed to the current economic situation. With the rise of social media platforms, individuals have been using Twitter to express their thoughts and opinions on the impact of the pandemic on the technology industry, including the increase in job losses and layoffs. In this study, we analyze the characteristics of Twitter users and their text and hashtag usage in the context of the pandemic's impact on the technology industry. We employ topic modeling and k-means clustering to a preprocessed dataset of tweets related to tech layoffs to identify common themes or topics in Twitter users' responses to tech winter layoffs in Indonesia. The analysis revealed a high number of negative tweets expressing anger and sadness. The use of predetermined keywords did not provide a comprehensive understanding of the phenomenon as other topics such as politics, religion, news, and advertisements were prevalent.
MyPertamina Transaction Increase Strategy Through Optimizing Application Use at Gas Stations Based on Machine Learning Hutapea, Fresly Leo Chandra; Widianto, Sunu; Samidi, Samidi
Inkubis : Jurnal Ekonomi dan Bisnis Vol. 8 No. 1 (2026): INKUBIS Jurnal Ekonomi Dan Bisnis
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/inkubis.v8i1.150

Abstract

Background: The digital transformation in the energy sector is accelerating cashless payment adoption. MyPertamina, PT Pertamina's mobile payment platform, has been deployed across gas stations nationwide. However, only 5.65% of 1.3 million registered users (September 2024) are active, and only 11.5% qualify as loyal users (≥4 transactions/month). This performance gap, which exists between registered, active, and loyal users, is the focus of this study, as existing research has overlooked the role of gas station operational governance in shaping transaction behavior. Objective: This study aims to (1) identify factors influencing MyPertamina usage at DKI Jakarta gas stations, (2) develop a machine learning-based prediction model to classify transaction behavior (MyPertamina vs. cash), and (3) create a G-STIC framework to increase adoption, usage intensity, and loyalty. Methods: A quantitative case study using the CRISP-DM framework analyzed secondary POS transaction data from 8,000 transactions (5,200 MyPertamina; 2,800 cash) at DKI Jakarta gas stations (2024). Stratified sampling was used, and the models—Decision Tree, Gradient Boosted Trees, and Decision Stump—were evaluated based on accuracy, precision, and recall. Results: Gradient Boosted Trees achieved the highest accuracy (97.75%). Gas Station Type and Class showed the strongest correlations with MyPertamina usage, suggesting further investigation of the Gas Station Code correlation. Conclusion: MyPertamina adoption is influenced by operational governance and service standards. The G-STIC framework provides actionable strategies for increasing digital transaction adoption, contributing to both academic literature and managerial practice in the energy retail sector.
Co-Authors Adz Zikri, Arif Furqon Nugraha Ernest, Bona F., Fery Hadianto, Arief Hutapea, Fresly Leo Chandra Layyinaturrobaniyah Layyinaturrobaniyah Mokhamad Anwar, Mokhamad Nibras, Hanifah Fauziyah Nugraha Adz Zikri, Arif Furqon Okviannas, Nadilla Rita Komaladewi Rizaldi, Muhammad Ibnu Samidi Samidi, Samidi Sinaga, Ruben Sulaeman Rahman Nidar