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All Journal Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika
Ichsan Ibrahim
STMIK Indonesia Mandiri Bandung
Computer Science & IT Electrical & Electronics Engineering

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Design and Development of a Hybrid NLP and Rule-based QA Assistant for Indonesian User Stories Cheria Sevani Apiani; Ichsan Ibrahim
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6383

Abstract

This study aims to address challenges in Agile-based software testing, where manually creating test cases from user stories is often time-consuming and produces inconsistent quality. Although Natural Language Processing (NLP) techniques and rule-based systems have been proposed, each approach has limitations in handling linguistic ambiguity and variations in sentence structure, particularly in the Indonesian language context. This research proposes a hybrid Quality Assurance (QA) assistant that integrates an IndoBERT-based Named Entity Recognition (NER) model with a deterministic rule-based system. The NER model is used to extract functional elements, including actors, actions, objects, conditions, and expected outcomes, while the rule-based system maps these elements into structured test case templates. Qualitative evaluation conducted by QA practitioners showed that the hybrid approach achieved an average score of 4.67 on a 5-point Likert scale, outperforming both the NLP-only approach (3.87) and the rule-only approach (4.60). The proposed system was proven to improve testing efficiency by more than 99% while generating test cases that are more complete, readable, and traceable. These findings confirm that integrating the flexibility of NLP with the consistency of rule-based systems is highly effective for automating Quality Assurance processes in the Indonesian local context.
Development of a Mobile Web-Based Food and Beverage Ordering Application in aYouth Cafe With QR Code Technology Adilla Faradila A.Sagaf; Ichsan Ibrahim
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/way9vn18

Abstract

The implementation of QR Code technology at Youth Cafe aims to increase ordering efficiency and provide a more comfortable experience for customers. Each table is equipped with a unique QR code that can be scanned to access the menu and place orders via the mobile web-based application. This application was designed using Native PHP with MySQL as the database, to ensure stability and optimal performance. The success of the system is measured by increasing order time efficiency by up to 50%, reducing manual errors by 90%, and the system's ability to handle up to 500 orders per day without interruption.  Customer surveys show satisfaction levels increased by 35% after application implementation. These results show that the use of QR Code technology has succeeded in improving operations, efficiency and service quality at Youth Cafe, as well as making a significant contribution to innovation in the food and beverage industry.
Implementation of Machine Learning in Business Intelligence for Customer Segmentation and Loyalty at PT. Inti Group Galuh Pandu Siwi Ambarsari; Ichsan Ibrahim
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5xwns554

Abstract

This study addresses the need for integrated data analytics and machine learning in PT Inti Group’s BI dashboard by implementing an unsupervised K‑Means clustering method on historical training data (January 2021–May 2025) extracted directly from a PostgreSQL database and analyzed using Python. The analysis process includes data preprocessing and feature engineering to create key variables: number of participants, training‑type frequency, recency (days since the last training), and engagement duration. Cluster determination was evaluated using the Elbow method (4 clusters), Silhouette score (2 clusters), and Davies–Bouldin index (9 clusters). Based on business interpretation and the balance between cluster compactness and separation, four clusters were selected: Loyal & High‑Value Customers, Inactive, Growing/Potential, and New/Sporadic. Customers who attended training more than ten times were classified as loyal. The segmentation results are visualized in a Power BI dashboard integrated directly with the data source, supporting rapid data‑driven managerial decisions. This study demonstrates that integrating unsupervised learning with BI effectively enhances understanding of customer characteristics and serves as a basis for designing more targeted marketing strategies. A limitation of this study is that the data cover only up to May 2025.
Co-Authors Adilla Faradila A.Sagaf Cheria Sevani Apiani Galuh Pandu Siwi Ambarsari