Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Brilliance: Research of Artificial Intelligence
Tri Bondan Kriswinarso
Universitas Cokroaminoto Palopo, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Internet of Things (IoT)-Based Water Quality Monitoring System Design for Tilapia Fish Farming Ponds Putu Erawati; Dianradika Prasti; 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.v5i2.6927

Abstract

This study aimed to develop an Internet of Things (IoT)-based water quality monitoring system for tilapia cultivation ponds at Varel Collection. Farmers previously monitored pond water manually, which was often neglected due to their agricultural activities, leading to poor water conditions and negative impacts on fish growth. To overcome this issue, a prototype system was designed to enable real-time monitoring of water parameters using IoT technology integrated with the Blynk platform. The research applied a Research and Development (R&D) approach with a Prototype method. The system was built using NodeMCU ESP32 as the main microcontroller, integrated with several sensors including a pH sensor, DS18B20 temperature sensor, and a turbidity sensor to measure water quality parameters. Data from these sensors are processed and transmitted to the Blynk application, allowing farmers to remotely monitor pond conditions via the internet. Supporting tools included Fritzing for design, Arduino IDE for programming, and hardware components such as adapters, jumper cables, and enclosures. Evaluation by three experts indicated that the system achieved an average feasibility index of 95.93%, categorized as “very good.” These findings show that the IoT-based monitoring system is functional, feasible for aquaculture use, and has strong potential for further development in real-time water quality management.
Co-Authors Dianradika Prasti Fajar Novriansyah Yasir Putu Erawati Willyadam Saad As’saidy