Article Per Year (5 Year)
p-Index From 2021 - 2026
2.012
P-Index
This Author published in this journals
All Journal International Journal of Pedagogy and Teacher Education Jurnal Ilmiah Ecosystem INTEK: Jurnal Penelitian Journal of Information Systems and Informatics Jurnal Teknologi Elekterika EJOIN : Jurnal Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems Journal of Informatics and Computer Engineering Research
Utomo, Muhammad Nur Yasir
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : journal of informatics and computer engineering research

 1 
CATTLE DISEASE DIAGNOSIS SYSTEM USING RANDOM FOREST CLASSIFICATION METHOD Syam, Muh. Fadhil; Utomo, Muhammad Nur Yasir; Tungadi, Eddy
Journal of Informatics and Computer Engineering Research Vol. 2 No. 1 (2025)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v2i1.5563

Abstract

Cattle farming plays an important role in Indonesia's economy, but its productivity can decline due to livestock health issues. To address this, this study develops a cattle disease diagnosis system based on machine learning using the Random Forest classification method. The system helps farmers identify diseases independently based on input symptoms. The model is built using the Random Forest algorithm, trained on 1745 primary data obtained from the Barru Regency Department of Agriculture. The data undergoes a comprehensive pre-processing stage, including cleaning to remove inconsistencies, One-Hot Encoding for categorical feature transformation, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE) to ensure fair representation of all disease categories. Model evaluation using a Confusion Matrix demonstrates a high accuracy of 91%, indicating strong predictive performance. Based on the model, a mobile application based on Android is developed to assist farmers in the early detection of cattle diseases.
PENGEMBANGAN SISTEM ZONASI PADA APLIKASI SAPEDA MADRASAH ALIYAH KABUPATEN PANGKEP MENGGUNAKAN METODE HAVERSINE FORMULA Muh. Fajri Raharjo; Utomo, Muhammad Nur Yasir; Ramadhani, Ainun Zulkiah
Journal of Informatics and Computer Engineering Research Vol. 2 No. 1 (2025)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v2i1.5566

Abstract

The SAPEDA application in Pangkep Regency currently lacks a feature that allows prospective students to determine the nearest school to their residence. This has led to an inefficient school selection process and unequal distribution of students. The aim of this research is to develop a zoning system for the SAPEDA application at Madrasah Aliyah in Pangkep Regency. The study employs the Haversine Formula in the zoning feature to calculate the distance between students' homes and nearby schools, offering an effective approach to improve the SAPEDA system. This will help prospective students choose the nearest school, reduce transportation costs, and promote equitable access to education. The research procedure includes a literature review, interviews, system design, system development, and testing. Results from a questionnaire based on the TAM (Technology Acceptance Model) show that the system is well-received and positively evaluated by respondents, with an average score of 90.62%, categorized as Excellent. This indicates, that the system has successfully provided benefits, ease of use, and has met users' expectations across various evaluated aspects, resulting in a high level of satisfaction
WEB-BASED PUBLIC RELATIONS CHATBOT USING LARGE LANGUAGE MODELS AND THE RETRIEVAL-AUGMENTED GENERATION Nuzul1, Andi Muhammad; Utomo, Muhammad Nur Yasir; Indrabulan, Tantri
Journal of Informatics and Computer Engineering Research Vol. 2 No. 2 (2025)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v2i2.6014

Abstract

The Public Relations and Protocol Working Group (Pokja Humas) of Politeknik Negeri Ujung Pandang (PNUP) faces challenges in providing interactive and responsive information services. The official website functions only as a one-way medium, and the high volume of repeated questions causes delays in response time. This study developed a public relations chatbot based on Large Language Model (LLM) using the Retrieval-Augmented Generation (RAG) method to improve information services. The chatbot data were obtained through web scraping of the PNUP official website and internal PDF documents, which were processed through preprocessing, text splitting, and embedding using Hugging Face and stored in a FAISS vectorstore. The system was built using FastAPI as the backend and web-based interfaces for admin and user interactions. The results show that User Acceptance Test (UAT) involving 35 respondents achieved 91.93% acceptance (very good). The Retrieval-Augmented Generation Assessment (RAGAS) evaluation achieved average scores of 0.89 for Faithfulness, 0.91 for Answer Relevancy, 0.89 for Context Precision, and 0.89 for Context Recall, indicating that the chatbot produced relevant and contextually accurate answers.
Co-Authors Abdul Rahman Al Imran, Andi Yusdar Anggun Winursito BASTIAN, ALVIAN Fansab, Nur Ayu Farahgta Indrabulan, Tantri Irfan Syamsuddin Khartika, Widya Memet Sudaryanto Mochammad Imron Awalludin Muh. Fajri Raharjo Nuzul1, Andi Muhammad Ramadhani, Ainun Zulkiah Rini Nur, Rini Silamba, Yurico Ignasius May Syam, Muh. Fadhil TUNGADI, EDDY Violita, Fajrin Yusri, Iin Karmila