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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Information Systems and Informatics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistem Komputer dan Informatika (JSON) International Journal Software Engineering and Computer Science (IJSECS)
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.
ANALISIS SPASIO-TEMPORAL DETEKSI ANOMALI SUHU PERMUKAAN BUMI ISOLATION FOREST: STUDI KASUS INDONESIA Nicolaus Owen Marvell; Muhammad Iqbalul Khoiri; Chrisjuanito Clancy; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4626

Abstract

Land surface temperature (LST) is an important indicator of climate change because increasing surface temperatures can trigger environmental degradation, drought, and extreme weather events. In Indonesia, long-term monitoring of extreme temperature anomalies remains limited, as most studies rely on conventional statistical methods that are less effective in detecting complex and non-linear anomaly patterns. Therefore, this study analyzes the spatio-temporal dynamics of LST and identifies extreme temperature anomalies across Indonesia during 1940–2024 using a machine learning approach. Monthly LST data were examined through exploratory data analysis (EDA), including temporal trend analysis and 10-year moving averages, to characterize long-term temperature variability, while the Isolation Forest algorithm was implemented as an unsupervised anomaly detection method using n_estimators = 100 and contamination = 0.05. The results identified 51 temperature anomalies, representing approximately 5% of the 1,020 monthly observations analyzed. Most anomalies occurred during periods associated with major climate disturbances and corresponded closely with documented El Niño events, particularly in 1997–1998 and 2015. Trend analysis revealed a persistent increase in Indonesia’s surface temperature, indicating an ongoing warming pattern consistent with climate change, while anomaly score distributions showed a clear separation between normal and extreme observations, confirming the effectiveness of the Isolation Forest algorithm. These findings demonstrate that integrating spatio-temporal analysis with machine learning provides a robust framework for detecting extreme temperature events and monitoring climate variability, thereby supporting climate risk assessment and strengthening BMKG’s early warning systems for climate change adaptation and mitigation.
KOMPARASI MODEL REGRESI DALAM MEMPREDIKSI GAJI PEKERJAAN ARTIFICIAL INTELLIGENCE M Rafly Ramdhani; M Luthfi Aldi Pratama; Muhammad Emirshah Yusuf; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4627

Abstract

The increasing demand for Artificial Intelligence-related jobs has intensified global labor market dynamics, characterized by high salary variability and growing industry uncertainty. These conditions pose significant challenges for organizations and professionals in determining accurate, objective, and data-driven salary estimations. This study aims to develop and compare the performance of several regression models for predicting Artificial Intelligence job salaries, namely Linear Regression, Gradient Boosting, and Support Vector Regression. A large-scale global job postings dataset is employed, incorporating conventional job attributes such as location, experience level, and job type. In addition, this study integrates industry risk variables, including layoff risk and automation risk, to capture more realistic labor market dynamics. The research methodology consists of data preprocessing, model development using a machine learning pipeline to ensure consistent processing between training and testing data, and performance evaluation. The dataset is split into training and testing sets using an 80:20 ratio, and model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The experimental results indicate that Gradient Boosting achieves the best performance with the lowest prediction errors and the highest explanatory power, followed by Linear Regression. In contrast, Support Vector Regression exhibits relatively poor performance on high-dimensional feature representations. These findings confirm that ensemble-based approaches are more effective in modeling the heterogeneous and non-linear salary structures of Artificial Intelligence jobs and provide valuable insights for data-driven labor market analysis.
User-Centered Design of Marker-Based Augmented Reality Learning Media to Improve Informatics Learning Effectiveness Chairunnisa Umi Kalsum; Ali Ibrahim; Ken Ditha Tania
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1704

Abstract

Computer science education in schools still relies heavily on traditional teaching methods, resulting in low student motivation, particularly in topics related to computer hardware components. This study develops an interactive Augmented Reality (AR) learning medium with Marker-Based Tracking using a User-Centered Design (UCD) approach to improve Informatics Learning effectiveness among 73 junior high school students at SMP Negeri 28 OKU. The UCD method was implemented through four stages: establishing requirements, designing, prototyping, and evaluating. Learning effectiveness was measured using N-Gain, a paired sample t-test, and Cohen's d based on pre-test and post-test scores. User experience was evaluated using the User Experience Questionnaire (UEQ), covering six dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The N-Gain score reached 0.937, classified as "High." The paired t-test yielded a statistically significant difference between pre-test and post-test scores (p < 0.001), and Cohen's d was 1.757, indicating a "Very Large" effect size. UEQ evaluation results across all six dimensions exceeded the benchmark threshold for the "Excellent" category. These findings indicate improved learning outcomes in the evaluated class and positive user experience with the developed AR medium.
Sentiment Analysis of Food Poisoning Incidents in Indonesia's Free Nutritious Meal Program Using TF-IDF and SVM Muhammad Lucky Hermanto; Ari Wedhasmara; Ken Ditha Tania; Allsela Meiriza
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.7809

Abstract

Food poisoning incidents associated with the Free Nutritious Meal Program (MBG) have triggered significant public reactions on the social media platform X/Twitter. This study identified and classified public sentiment regarding food poisoning occurrences in the MBG program by applying a Support Vector Machine (SVM) classification framework. Data were crawled from X/Twitter via Tweet Harvest using keywords related to MBG food poisoning incidents between January 2025 and June 2026. A total of 6,929 posts were collected, and 6,902 Indonesian-language records were retained after duplicate removal. The analytical workflow comprised data selection, automated sentiment labeling with IndoBERTweet, text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) feature weighting, and classification using the SVM algorithm. Model performance was evaluated through a confusion matrix using accuracy, precision, recall, and F1-score metrics. Based on an evaluation of 1,989 testing records, the SVM model achieved an overall accuracy of 76.42%, exhibiting its strongest performance in the negative sentiment class. However, positive sentiment recorded a low recall of 26.03%, reflecting the difficulty of detecting the minority class. Sentiment distribution analysis revealed that negative sentiment dominated public discourse at 63.78%, followed by neutral sentiment at 29.05% and positive sentiment at 7.17%. These findings indicate that public perceptions of food poisoning incidents in the MBG program were overwhelmingly negative, underscoring critical concerns regarding food safety and program oversight. Beyond contributing to machine learning-based social media analytics, this study provides actionable insights for policymakers to evaluate service delivery and reinforce food safety standards in the MBG program.
Co-Authors Ahmad Rifai Ali Ibrahim Alif Baidhawi Allsela Meiriza, Allsela Ari Wedhasmara Chairunnisa Umi Kalsum Chrisjuanito Clancy Fahren Affandi Imam Akbar M Luthfi Aldi Pratama M Rafly Ramdhani M. Bintang Naufal Riansyah Mira Afrina Muhammad Emirshah Yusuf Muhammad Iqbalul Khoiri Muhammad Lucky Hermanto Nicolaus Owen Marvell Rizka Dhini Kurnia Sella Juniastia Marsya Saputri Zwesty Quatra