Article Per Year (5 Year)
p-Index From 2020 - 2025
1.77
P-Index
This Author published in this journals
All Journal Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT Jurnal Janitra Informatika dan Sistem Informasi Brilliance: Research of Artificial Intelligence Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Jurnal Teknik Informatika dan Desain Komunikasi Visual
Kurniawan, Rifki Dwi
Unknown Affiliation
Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Intelligent Decision Support System (IDSS)

 1 
Application of computer vision techniques to detect diseases and pests of chili plants Nurokhman, Akhmad; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.201

Abstract

This research aims to develop a disease and pest detection system in chili plants using computer vision techniques. In this study, deep learning methods, especially Convolutional Neural Networks (CNN), were applied to identify and classify various types of diseases and pests that often attack chili plants. The data used included images of chili leaves infected with various diseases and pests, which were then trained in CNN models to recognize certain patterns that indicate the presence of infection. The results showed that the developed system was able to detect and classify diseases and pests in chili plants with a very high degree of accuracy. The novelty of this research lies in the use of computer vision techniques combined with sophisticated deep learning algorithms to automatically detect diseases and pests, which were previously done manually by farmers or agricultural experts. These findings make an important contribution to improving efficiency and effectiveness in chili crop health management, offering innovative solutions to support agricultural sustainability through the use of advanced technology.
Application of fuzzy expert system method for early detection of dengue fever Prayoga, Alan Eka; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.217

Abstract

The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.
Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency Surorejo, Sarif; Mutaqin, Ahadan Fauzan; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.231

Abstract

This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
Co-Authors Aang Alim Murtopo Adhi Santoso, Nugroho Affandi, Khaediar Bagus Arif, Zaenul Bangkit Indarmawan Nugroho Dewi, Errika Mutiara Gunawan Gunawan Karsidin, Karsidin Lestari, Nindy Putri Marzuqi, Maezun Nafis Mutaqin, Ahadan Fauzan Nugroho, Bangkit Indramawan Nurokhman, Akhmad Prayoga, Alan Eka Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nugroho Adhi Sarif Surorejo Supratman, Ardhi Syefudin, Syefudin