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Dede Kurniadi
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Journal of Intelligent Systems Technology and Informatics
Published by Aliansi Peneliti Informatika
ISSN : -     EISSN : 3109757X     DOI : https://doi.org/10.64878/jistics
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
The Journal of Intelligent Systems Technology and Informatics (JISTICS) is an international peer-reviewed open-access journal that publishes high-quality research in the fields of Artificial Intelligence, Intelligent Systems, Information Technology, Computer Science, and Informatics. JISTICS aims to foster global scientific exchange by providing a platform for researchers, practitioners, and academics to disseminate original findings, critical reviews, and innovative applications. The journal is published three times a year (March, July, November) and may also publish special issues on emerging topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 24 Documents
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Analysis of Earthquake Notification Complaint Topics in Info BMKG Reviews Using BERTopic Diniyaturobiah, Hanipah; Muzaky, Rifky Khoerul
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.123

Abstract

Reliable earthquake notification services in public information applications play a critical role in supporting public awareness and preparedness in seismically active regions. This study examines user complaints about earthquake notification features in the Info BMKG mobile application by analyzing publicly available Google Play Store user reviews. A total of 1,500 reviews were collected and examined, with complaint reviews operationally defined as those with star ratings of 3 or lower. Prior to analysis, the dataset underwent text preprocessing and a balancing procedure to ensure adequate representation of complaint-related content. Topic modeling was conducted using BERTopic, a transformer-based approach that enables context-aware clustering of short, informal text, followed by descriptive temporal analysis to examine variations in complaint occurrence over time. The analytical workflow included text normalization, embedding generation, topic extraction, and temporal mapping of complaint patterns. The results reveal several recurring complaint themes, including delayed or missing notifications, clarity of information, application performance issues, and user responses to system updates. Temporal variations indicate periods of increased complaint activity that align with heightened application usage, reflecting shifts in user engagement rather than direct evidence of system failure. Topic validity was assessed through qualitative inspection of representative reviews to ensure semantic consistency and interpretability. Overall, this study provides a structured, descriptive overview of user concerns regarding earthquake notification services and demonstrates the applicability of topic-level and temporal analysis as an evaluative approach for mobile disaster information applications, without making causal performance claims.
Transaction Segmentation of Supermarket Sales Data for Retail Decision Support Using K-Means Clustering Khoiriyyah, Fakhrun Mahda; Suhendar, Hery; Maulana, Yusep
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.145

Abstract

The increasing availability of transactional data in the retail sector provides opportunities to support data-driven managerial decision-making. This study aims to segment supermarket sales transactions using the K-Means clustering method to identify meaningful transaction patterns that support retail decision-making. A publicly available supermarket transaction dataset was analyzed using selected numerical attributes representing purchase quantity, transaction value, and customer rating. To ensure reliable and interpretable clustering results, data standardization was applied, and the optimal number of clusters was determined using a combined validation strategy comprising the Elbow Method and the Silhouette Score. The results indicate that three distinct transaction segments were identified, characterized by similar purchase quantities but differing transaction values and customer satisfaction levels. Principal Component Analysis visualization confirms that the resulting clusters are well separated and interpretable. The findings demonstrate that integrating systematic cluster validation with interpretable cluster analysis provides practical insights for retail managers in designing targeted marketing strategies, improving customer satisfaction, and supporting inventory and promotional decision-making.
Rethinking Efficiency: A Comparative Study of Lightweight CNN Architectures for Image Classification Fauzan, Mochamad Rizal; Naufal Nadhif Rabbani Iskandar; Rafi Zahran Fauzi
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.167

Abstract

Lightweight convolutional neural networks (CNNs) are increasingly required for image classification in resource-constrained environments; however, their comparative behavior under unified training conditions remains insufficiently explored, particularly when accuracy, parameter efficiency, inference latency, and augmentation sensitivity are evaluated simultaneously. This study presents a systematic benchmark of five lightweight CNN architectures, namely MobileNetV2, EfficientNet-B0, ShuffleNetV2, SqueezeNet, and ResNet18, on the CIFAR-100 dataset using a consistent experimental pipeline. All models were trained for 40 epochs with an input resolution of 128 × 128, AdamW optimization, cosine annealing, mixed-precision training, and identical preprocessing settings. Two augmentation strategies, namely basic and advanced augmentation, were evaluated to examine their influence on model generalization. The results show that EfficientNet-B0 achieved the best classification performance with 82.75% Top-1 accuracy and 96.46% Top-5 accuracy, while SqueezeNet achieved the fastest inference latency of 1.52 ms and the smallest parameter size, indicating its suitability for highly constrained deployment scenarios. Across all evaluated models, the average Top-1 and Top-5 accuracies reached 76.6% and 94.16%, respectively. In addition, the effect of advanced augmentation was found to be architecture-dependent rather than uniformly beneficial. On average, it resulted in a Top-1 accuracy change of −0.66 percentage points, with only ResNet18 showing a modest improvement. The main contribution of this study is to provide a unified, practically oriented benchmark that highlights how architectural design, rather than parameter count alone, determines the balance between accuracy and computational efficiency. These findings provide clearer guidance for selecting lightweight CNN models for real-world image classification tasks under varying deployment constraints.
Empirical Benchmarking of Hybrid Retrieval in Educational Conversational AI: Accuracy‑Latency Trade‑offs and Robustness Abdul Saboor Hamedi; Iqbal Hussain Alamyar; A.A. Waskita
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.245

Abstract

This study investigates the comparative performance of lexical, semantic, and hybrid retrieval strategies in educational conversational AI, with a focus on accuracy-latency trade‑offs and robustness across diverse query types. A controlled experimental framework was implemented using PostgreSQL’s ts_rank for lexical retrieval, pgvector embeddings for semantic retrieval, and two fusion strategies: Linear Weighted Fusion and Reciprocal Rank Fusion. The evaluation corpus consisted of approximately 50,000 text chunks extracted from 2025 arXiv AI/ML papers, and a benchmark of 100 queries spanning conceptual, factual, procedural, comparative, and miscellaneous categories was executed. Effectiveness was measured using NDCG@10, Precision@5, and MRR, while efficiency was quantified via end‑to‑end latency. Relevance judgments were generated through an AI‑as‑a‑Judge pipeline to ensure scalability and reproducibility. Results showed that semantic and hybrid methods achieved a high accuracy mean NDCG@10 ≈ 0.91 but incurred latency costs between 227-505 ms. Lexical retrieval was fastest, 88 ms, but substantially less accurate, 0.346. Hybrid‑Linear fusion emerged as the most robust strategy, winning 66% of queries in the Winner‑Take‑All analysis, while semantic search excelled in conceptual queries and lexical search in acronym‑based factual lookups. Reciprocal Rank Fusion achieved comparable mean accuracy but failed to dominate in any category. The findings highlight a clear quality–speed dichotomy and establish Hybrid‑Linear fusion as the most dependable retrieval method for educational chatbots. For latency‑sensitive applications, semantic search offers the best balance of responsiveness and accuracy. The study provides actionable design guidelines and identifies future directions, including corpus generalization, human evaluation calibration, intelligent query routing, and latency optimization.

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