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All Journal Brilliance: Research of Artificial Intelligence
Fajar Novriansyah Yasir
Universitas Cokroaminoto Palopo, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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A Mobile-Based Expert System for Glaucoma Diagnosis Using the Naive Bayes Algorithm Amelia Bahar; Fajar Novriansyah Yasir; Sukmawati Sukmawati
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6633

Abstract

This study aims to develop a mobile-based expert system application for early diagnosis of glaucoma using the Naive Bayes algorithm. The application is designed to help users recognize early symptoms of glaucoma, provide preliminary information, and increase public awareness to reduce the risk of vision loss or blindness. The application was developed using the Dart programming language, the Flutter framework, and Firebase as the database platform. The research method employed is Research and Development (R&D), utilizing the 4D development model, which consists of four stages: Define, Design, Develop, and Dissemination. To evaluate the functionality and effectiveness of the application, both black-box testing and expert validation were conducted. The Naive Bayes algorithm implemented in the application demonstrated a high accuracy rate of 97.50%, indicating strong reliability in recognizing symptom patterns and producing appropriate diagnostic predictions based on user input. Furthermore, the System Usability Scale (SUS) was used to assess the application's usability, yielding a high average score of 97.5%, reflecting excellent ease of use and user satisfaction. In addition, content validation by subject matter experts resulted in an average feasibility score of 98.07%, indicating that the application is highly suitable for public use in supporting early screening and diagnosis of glaucoma.
Expert System for Diagnosing Phone Damage with Repair Shop Recommendations Using CF Method Willyadam Saad As’saidy; Fajar Novriansyah Yasir; Tri Bondan Kriswinarso
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6648

Abstract

Based on observations conducted by the author through interviews at one of the repair shops in Palopo City, the owner—Zul, in 2024—stated that repair shops tend to direct mobile phone users to other shops when the type of device damage cannot be handled by their own repair shop. This reflects a lack of a centralized diagnostic system that can accurately identify damage and recommend the appropriate service provider. In response to this issue, this study aims to develop an Android-based expert system application for diagnosing mobile phone damage, equipped with a responsive and location-aware repair shop recommendation feature using the Certainty Factor (CF) method. The application was built using the 4D development model (Define, Design, Develop, Disseminate) and utilizes the results of observations, literature reviews, and interviews with local technicians to form its knowledge base and rule sets. The diagnosis process is carried out by calculating the confidence value between selected symptoms and corresponding damage types using a combined CF formula, such as CFcombine = CF1 + CF2 × (1 – CF1). This allows the system to measure the degree of certainty with which a particular diagnosis can be made. User testing involving target users showed a high level of feasibility and satisfaction, with a System Usability Scale (SUS) score of 82.5, falling into the “Acceptable” and “Good” categories. The application has proven effective in identifying various types of device damage and providing accurate, real-time repair shop recommendations based on both user location and the type of damage detected. This offers a relevant and practical digital solution for the community in Palopo City.
Implementation of Data Mining for Analyzing Consumer Purchasing Patterns at TeTa Ino Cafe Theresia Elvita Tjia; Fajar Novriansyah Yasir; Shindy Ekawati
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6767

Abstract

TeTa Ino is a micro, small, and medium enterprise by university students focusing on the production and marketing of innovative products based on butterfly pea tea. The significant decrease in sales volume from approximately 50–80 units per promotional event to only 20–40 units indicates a potential issue in the current marketing strategy. Therefore, this study aims to identify consumer purchasing patterns that can serve as the foundation for developing more targeted marketing strategies to enhance the competitiveness of TeTa Ino. This research employs the Cross Industry Standard Process for Data Mining (CRISP-DM) approach by applying the K-Means Clustering algorithm to four months of transaction data, including variables such as number of transactions, total transaction value, and discounts offered. The analysis resulted in four distinct consumer clusters: passive consumers, loyal consumers, non-loyal consumers, and consumers with moderate purchasing frequency. Each cluster is recommended to be approached with tailored marketing strategies, such as loyalty programs, product benefit education, and bundling promotions. The clustering evaluation achieved a Silhouette Score of 0.9008 and a Calinski Harabasz Score of 7630.34, indicating good segmentation quality and clear separation among clusters. This study concludes that applying the K-Means Clustering algorithm is effective in mapping consumer purchasing behavior as a basis for data-driven marketing strategy formulation. Future research is recommended to incorporate time-related variables and explore other clustering methods to further strengthen the analysis.
Co-Authors Amelia Bahar Shindy Ekawati Sukmawati Sukmawati Theresia Elvita Tjia Tri Bondan Kriswinarso Willyadam Saad As’saidy