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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON)
Ken Ditha Tania
Sriwijaya University
Computer Science & IT Control & Systems Engineering Electrical & Electronics 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.
Perbandingan Performa Model Prediksi Volatilitas BTC/IDR Menggunakan LSTM dan ARIMA Fahren Affandi; Imam Akbar; Sella Juniastia Marsya Saputri; Zwesty Quatra; Allsela Meiriza; Ken Ditha Tania; Ahmad Rifai
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9719

Abstract

Karakteristik fluktuatif pasar aset kripto yang ekstrem menuntut ketersediaan model peramalan yang andal sebagai penunjang strategi manajemen risiko investasi. Penelitian ini bertujuan untuk membandingkan pendekatan Long Short-Term Memory (LSTM) sebagai model deep learning sekuensial dan Autoregressive Integrated Moving Average (ARIMA) sebagai model statistik deret waktu dalam memprediksi log-volatility Bitcoin pada pasangan BTC/IDR periode 2018–2025. Dataset historis harian BTC/IDR diperoleh dari platform Binance dengan periode observasi Januari 2018 hingga Desember 2025, kemudian diproses melalui perhitungan log-return, estimasi realized volatility berbasis jendela 7 hari, transformasi logaritmik, serta normalisasi data. Evaluasi model menggunakan metode walk-forward validation dengan metrik Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model LSTM memperoleh MAE sebesar 0,5126, RMSE sebesar 1,0408, dan R² sebesar 0,6803, sedangkan model ARIMA menghasilkan MAE sebesar 0,5430, RMSE sebesar 1,0217, dan R² sebesar 0,7052 pada konfigurasi terbaiknya. Meskipun LSTM memiliki MAE yang lebih rendah, model ARIMA menunjukkan performa yang lebih unggul berdasarkan nilai RMSE yang lebih kecil dan R² yang lebih tinggi, sehingga lebih efektif dalam menjelaskan variasi data serta menangkap fluktuasi ekstrem pada volatilitas Bitcoin. Secara keseluruhan, hasil penelitian menunjukkan bahwa model ARIMA lebih representatif dalam memodelkan dinamika log-volatility Bitcoin dibandingkan model LSTM. Temuan ini menegaskan bahwa pemilihan model prediksi volatilitas perlu mempertimbangkan karakteristik data yang dinamis dan fluktuatif. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan model prediksi volatilitas yang adaptif, khususnya pada pasar cryptocurrency di Indonesia.
Co-Authors Ahmad Rifai Alif Baidhawi Allsela Meiriza, Allsela Fahren Affandi Imam Akbar M. Bintang Naufal Riansyah Mira Afrina Rizka Dhini Kurnia Sella Juniastia Marsya Saputri Zwesty Quatra