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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Malcom: Indonesian Journal of Machine Learning and Computer Science
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: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

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.
SECI and K-Means Integration for Public Sector Logistics Budget Efficiency Leiden Fauzi Yoka Surya; R. Nyi Pipih Kurniasari; Muthia Ramadhani; Khansa Putri Amanda; Ken Ditha Tania; Dedy Kurniawan; Ahmad Rifai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2695

Abstract

Suboptimal management of office supplies causes significant budget waste in the public sector. This study addresses this issue by integrating the K-Means algorithm and the Socialization, Externalization, Combination, dan Internalization (SECI) knowledge management model to optimize logistics budget efficiency at the Palembang DPRD Secretariat. K-Means was utilized to partition the 2025 supply expenditure data into three priority clusters based on budget absorption and demand frequency. To ensure analytical outputs influence managerial decisions, K-Means was positioned as the primary explicit-to-explicit transformation engine within the SECI combination phase. The integration successfully transformed raw transaction data into a data-driven Standard Operating Procedure (SOP). Quantitative analysis reveals that a small subset of items in Cluster C3 accounts for a disproportionately high share of total budget absorption. Consequently, supervision can now strictly target these high-budget anomalies such as the Rp52.2 million spent on specific folio paper significantly reducing potential leakage and improving allocation efficiency. The main scientific contribution of this study is a novel framework that bridges mathematical data extraction and managerial policy formulation. This integrated approach is proven to measurably enhance regional budget efficiency.
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 Dedy Kurniawan Fahren Affandi Imam Akbar Khansa Putri Amanda Leiden Fauzi Yoka Surya M. Bintang Naufal Riansyah Mira Afrina Muthia Ramadhani R. Nyi Pipih Kurniasari Rizka Dhini Kurnia Sella Juniastia Marsya Saputri Zwesty Quatra