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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 454 Documents
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APRIORI ALGORITHM AND TIME SERIES FOR PURCHASING PATTERNS AND SALES FORECASTING: MATAHARI CIPUTRA JAKARTA Taufiq Muhaziz; Hendri Ardiansyah
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.604

Abstract

Retail transaction data can be used to explain purchasing patterns and forecast future sales, but the two analyses are often applied separately. This study examines purchasing patterns and product sales forecasts for the Eid shopping period at Matahari Department Store Ciputra Jakarta. The dataset contained 389,588 product-level records from January-April 2025 and January-April 2026, representing 194,854 transactions. The Apriori algorithm was applied with a minimum support of 1% and a minimum confidence of 10%. Daily sales of the 50 best-selling products were forecast using ARIMA(2,1,2), SARIMA(2,1,2)(1,1,1,7), and Prophet with weekly seasonality. Apriori produced 59 frequent itemsets and 24 association rules. The strongest selected rule was Socks to Sneakers with a lift of 11.39, while Hoodie to Jeans reached 43.63% confidence. ARIMA produced the lowest average errors, with MAE 42.46, RMSE 76.86, and MAPE 102.14%, compared with SARIMA and Prophet. The relatively high MAPE indicates that the forecast should be treated as a comparative estimate rather than an exact value. The 90-day forecast was combined with association rules to generate stock priorities, bundling, and product-display recommendations. Manual recalculation of selected Apriori rules and ARIMA evaluation metrics matched the system output, while all 16 black-box scenarios were successfully executed. Keywords: Apriori; association rules; Eid sales; purchasing patterns; time series forecasting
IMPLEMENTATION OF DISASTER MITIGATION CHATBOT USING GRAPH RETRIEVAL-AUGMENTED GENERATION (GraphRAG) FOR BPBD KABUPATEN BOGOR Anastia Nisrina; Foni Agus Setiawan; Gibtha Fitri Laxmi
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.650

Abstract

Kabupaten Bogor is among the highest disaster-risk regencies in West Java, having recorded 1,826 disaster events throughout 2025, while official mitigation guidelines remain confined to static PDF documents that are difficult to consult during emergencies. To address this gap, this study implements a disaster mitigation chatbot built on Graph Retrieval-Augmented Generation (GraphRAG), pairing Neo4j Desktop as a local graph database with Gemma3:4b (served through Ollama) as the language model, orchestrated via LangChain and exposed through a Flask web interface. The knowledge graph was built from 11 source documents provided by BPBD Kabupaten Bogor—Disaster Preparedness Pocket Books together with supplementary disaster-information documents—spanning 8 disaster types, yielding 483 nodes and 754 relationships. Each entity node carries three properties (name, description, and disaster phase) to enable more precise retrieval. System performance was assessed with the RAGAS v0.4.3 framework over a 99-question dataset over three trials, yielding average Faithfulness, Answer Relevancy, and Context Precision scores of 0.8764, 0.6773, and 0.8216, respectively. These results indicate promising contextual grounding and retrieval performance, while the lower Answer Relevancy score highlights the need for further improvement in aligning generated answers with user questions. The findings demonstrate the technical feasibility of a locally hosted GraphRAG prototype for retrieving disaster-mitigation information from BPBD Kabupaten Bogor documents. However, the evaluation does not establish operational readiness for emergency use, as comparison with conventional RAG, expert assessment, and validation with intended users remain necessary.
XGBOOST-BASED CLASSIFICATION OF POTENTIALLY FRAUDULENT SHOPEE SELLERS USING SELLER METADATA AND SHAP Azka Fahsya; Wargijono Utomo; Harry Dwiyana Kartika
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.651

Abstract

The rapid growth of e-commerce increases the exposure to fraudulent sellers, while reactive reporting mechanisms generally operate only after consumers suffer losses. This study develops a proactive fraud-risk screening model to assist in identifying potential fraud-risk indications among Shopee sellers from store-profile metadata and to explain the model's decisions. Primary data were collected from Indonesian Shopee seller profiles using a manual-hybrid approach with Playwright. The initial dataset contained 460 profiles and was reduced to 457 clean records after duplicate removal. The final dataset comprised 355 Safe stores and 102 Fraud stores (labeled heuristically using external indicators) with 11 predictor features. XGBoost was trained with class weighting through a scale_pos_weight value of 3.48 and an 80:20 stratified split. Evaluation on 92 test records produced 95.65% Accuracy, 84.00% Precision, 100.00% Recall, and 91.30% F1-Score. SHAP analysis identified historical_sold (48.42%), store age (18.73%), and rating_star (14.76%) as the three most influential features. The trained model was subsequently integrated into a Chrome Extension prototype to provide predictions and SHAP-based rationales. The results indicate that a seller-centric metadata approach can support early fraud-risk detection; however, the 84.00% precision confirms that the output should be interpreted as a risk screening indication rather than an absolute determination of fraud, assisting consumers in making safer decisions.
CLEARPAY: INNOVATIVE DESIGN OF AN IMPORT INVOICE PAYMENT SYSTEM USING DESIGN THINKING Siti Ernawati; Dara Pramita Rosliana
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.654

Abstract

The invoice payment process in import activities faces various obstacles, such as limited information, unorganized workflows, difficulty monitoring payment status, and the risk of administrative errors. The purpose of this research is to create a design for the invoice payment system in the import process by applying the Design Thinking method. This method consists of five stages, namely Empathise, Define, Ideate, Prototype, and Test. At the Empathise stage, information is collected through observation and interviews to understand user needs. Define is used to determine the main problems faced by users. Ideate is used to create various creative design solutions. The Prototype phase resulted in a high-fidelity prototype called ClearPay that provides features for invoice management, approval processing, payment processing, status monitoring, and payment confirmation, while Testing to assess the design based on user feedback. Evaluation using the system Usability Scale (SUS) obtained an average score of 82.42, which included Acceptable, Grade A, and the adjective rating Excellent categories. The results show that the ClearPay prototype has a good level of usability and user acceptance and can be the basis for system development at the implementation stage.

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