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All Journal Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information Systems and Informatics
Rizka Dhini Kurnia
Sriwijaya University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Determinants of Impulsive Buying During Shopee Flash Sales: Ajzen’s Theory of Planned Behavior Approach Alif Baidhawi; Mira Afrina; Ken Ditha Tania; Rizka Dhini Kurnia
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1452

Abstract

This research investigates the psychological elements that affect consumers’ impulsive buying behavior during Shopee flash sale events using the TPB. This inquiry employs a quantitative causal approach using survey data from 154 Shopee users engaged in flash sale purchases. Data were analyzed using a variance-based structural equation modeling approach with SmartPLS. The findings indicate that AT, SN, and PB jointly demonstrate significant effects on impulsive buying intention (β = 0.401; β = 0.395; β = 0.161), jointly explaining 59.9% of its variance. In addition, impulsive buying intention demonstrates a strong influence on actual impulsive buying behavior (β = 0.656, p < 0.001), accounting for 43.1% of the behavioral variance. Among the antecedents, attitude represents the most dominant predictor of intention, followed by subjective norms. A key advancement of this research stems from the integration of the TPB framework within flash sale contexts, positioning impulsive buying intention as a central psychological mechanism under conditions of time pressure. from a practical standpoint, the findings suggest that Shopee sellers and digital marketers should emphasize benefit-oriented messaging, urgency cues, and social validation features such as reviews, real time purchase indicators, and influencer endorsements to strengthen consumers’ impulsive buying intention during flash sale campaigns.
Predicting Impulsive Buying in Tokopedia Flash Sales: A UTAUT2 Approach M. Bintang Naufal Riansyah; Mira Afrina; Ken Ditha Tania; Rizka Dhini Kurnia
SISTEMASI Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.6137

Abstract

Flash sale events have become a dominant marketing strategy to trigger rapid purchasing decisions. However, despite the massive growth of e-commerce in Indonesia, it remains unclear whether consumer participation in these events is primarily driven by the thrill of the "hunt" (hedonic) or the rational calculation of discounts (price value), particularly in developing digital markets like Palembang City. This study investigates the determinants of impulsive buying behavior during Flash Sale events on the Tokopedia platform. Drawing upon a modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, this study investigates how Hedonic Motivation and Price Value affect Behavioral Intention, and in turn, its effect on Impulsive Buying. A quantitative methodology was applied, leveraging survey responses from 144 participants in Palembang City who had engaged in Tokopedia Flash Sales. Analysis was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software. Findings reveal that both Hedonic Motivation and Price Value positively and significantly impact Behavioral Intention, with Price Value identified as the most influential predictor. Furthermore, a robust positive relationship was found between Behavioral Intention and Impulsive Buying, confirming that the intention to participate in Flash Sales significantly drives unplanned purchasing behavior. These findings suggest that while hedonic enjoyment is important, the perceived economic benefit remains the primary catalyst for consumers. Practically, platforms can optimize flash sale design by emphasizing perceived savings and enjoyable experience to effectively drive conversion.
ANALISIS SENTIMEN ULASAN APLIKASI MOBILE JKN MENGGUNAKAN NAÏVE BAYES, KNN, DAN SVM BERBASIS SMOTE: SENTIMENT ANALYSIS OF JKN MOBILE APPLICATION REVIEWS USING NAÏVE BAYES, KNN, AND SVM BASED ON SMOTE Octa Dwiansyah; Rizka Dhini Kurnia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6930

Abstract

Sentiment analysis of user reviews on the Mobile JKN application plays an important role in identifying user satisfaction levels and obstacles encountered when accessing BPJS Kesehatan’s digital services. This study aims to evaluate the performance of three classification algorithms Naïve Bayes, KNN, and SVM optimized using the SMOTE approach to address data imbalance. A total of 19,186 user reviews were used, which underwent preprocessing, TF-IDF weighting, and an 80:20 data split, resulting in 15,348 training data and 3,838 testing data. Each model was evaluated before and after SMOTE application to assess its impact on model performance. The results show that SMOTE improved model sensitivity toward minority classes without significantly reducing accuracy. The Naïve Bayes model achieved 93.56 % accuracy before SMOTE and 92.05 % after, KNN improved from 71.20 % to 87.96 %, while SVM maintained the highest performance with 93 % accuracy and an F1-score of 0.91–0.95. The highest AUC value was obtained by SVM (0.98), followed by Naïve Bayes (0.97) and KNN (0.93). Sentiment classification results revealed that most user reviews were positive, reflecting high satisfaction with the Mobile JKN service, while negative reviews were primarily related to technical issues. Overall, the SMOTE-based SVM model proved to be the most effective algorithm for sentiment analysis and can serve as a foundation for evaluating and improving the quality of BPJS Kesehatan’s digital services.
Co-Authors Alif Baidhawi Ken Ditha Tania M. Bintang Naufal Riansyah Mira Afrina Octa Dwiansyah