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All Journal Journal of Information Systems and Informatics Jurnal Algoritma
Erina Setyawati
Universitas Amikom Purwokerto
Computer Science & IT

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Analisis Kesuksesan Aplikasi Food Delivery Menggunakan Model DeLone & McLean: Studi Empiris Pengguna ShopeeFood Erina Setyawati; Berlilana; Dhanar Intan Surya Saputra
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3425

Abstract

The digital transformation in the food delivery industry is driving service providers to offer high-quality information systems while ensuring user security and privacy so that satisfaction levels can be maintained. This study was conducted to examine the influence of Information System Quality and Security & Privacy on User Satisfaction among ShopeeFood app users by adopting the DeLone and McLean information system success model and using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method. The study employed a quantitative approach by collecting data through a questionnaire distributed to 100 ShopeeFood users. The analysis results show that Information System Quality and Security & Privacy have a positive and significant influence on User Satisfaction. An R-squared value of 0.642 indicates that these two variables account for 64.2% of the variation in user satisfaction. These findings confirm that optimal information system quality, supported by adequate security and privacy protections, plays a crucial role in enhancing ShopeeFood user satisfaction. From a theoretical perspective, this study reinforces the relevance of the DeLone and McLean model in the context of digital food delivery services, while from a practical standpoint, the research findings can serve as a reference for service providers to improve system quality, service security, and the overall user experience.
Rule-Based Aspect Extraction and IndoBERT-Based Sentiment Classification of Ruparupa Mobile Application Reviews Erina Setyawati; Berlilana; Dhanar Intan Surya Saputra
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.v8i4.1816

Abstract

This study evaluates a pipeline that separates rule-based aspect extraction from IndoBERT-based binary sentiment classification for Indonesian Ruparupa mobile application reviews. Google Play reviews were collected on 31 July 2026, anonymized, deduplicated before aspect expansion, and cleaned by lowercasing, removing URLs, emails, and special characters, and normalizing whitespace; no slang normalization, stop-word removal, or stemming was applied. Ratings 1-2 and 4-5 provided weak negative and positive labels, while three-star reviews were excluded. A 331-entry aspect dictionary mapped 1,495 unique reviews into 2,873 aspect-review pairs across six aspects. Across five repeated leakage-free group hold-out splits, IndoBERT achieved mean accuracy 0.9179 ± 0.0214, macro F1 0.9178 ± 0.0214, and ROC-AUC 0.9719 ± 0.0103; a calibrated TF-IDF + linear SVM baseline achieved 0.8765 ± 0.0124, 0.8759 ± 0.0127, and 0.9452 ± 0.0102, respectively. A McNemar test on run 1 showed a significant paired difference (p = 0.00013). Performance measures agreement with rating-derived weak labels rather than human-validated aspect sentiment. Because results from system-assigned aspects lacked independent human validation, aspect frequencies are descriptive rule-system outputs. Within this dataset, IndoBERT performed consistently across the five splits; supervised aspect extraction and human aspect-level annotation remain priorities.
Co-Authors Berlilana Berlilana Dhanar Intan Surya Saputra