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All Journal Jurnal Teknik ITS JURNAL REKAYASA TEKNOLOGI INFORMASI Voice Of Informatics CSRID (Computer Science Research and Its Development Journal) Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Nusantara Hasana Journal Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Abdi Masyarakat Indonesia Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika dan Teknologi Informasi JSITP Jurnal Indonesia Sosial Teknologi Journal of Artificial Intelligence and Digital Business Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research SOROT: Jurnal Pengabdian Kepada Masyarakat SmartComp Jurnal Akademik Pengabdian Masyarakat Svarga Pena: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika dan Desain Komunikasi Visual
Sarif Surorejo
STMIK YMI Tegal
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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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
Application of artificial neural network method for early detection of dengue fever Surorejo, Sarif; Ningrum, Isna Lidia; Ananda, Pingky Septiana; 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.v7i2.240

Abstract

Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications
Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction Surorejo, Sarif; Ubaidillah, Muhamad Rizal; Syefudin, Syefudin; 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.v7i2.245

Abstract

The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
Co-Authors ., Yustira Aang Alim Murtopo Adhi Santoso, Nugroho Al Fattah, Muhammad Raikhan Albana, Muhammad Syifa Ali Djamhuri Alzam Habibie Ananda, Pingky Septiana Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Arif , Zaenul Arif, Zaenul Aslam, Muhammad Nur Bangkit Indarmawan Nugroho Bayu Aji Santoso Cahyo, Septian Dwi Defi Lugianti Divia Faiqotul Cahyati Dwi Kurniawan, Rifki Erni Unggul Sedya Utami, Erni Unggul Sedya Fadlilah, Chairil Aditya Nur Firmansyah, Muchamad Aries Gunawan Gunawan Gunawan Gunawan Gunawan Hastin Setyorini Isnaeni Hamidah Juniyanto, Rudi Karsidin, Karsidin Khofifah Indah Hasanah Khofifah Indah Hasanah Kurniawan, Rifki Dwi Maulana, M Taufik Fajar Milkhatunisya Milkhatunisya Milkhatunisya, Milkhatunisya Moh. Jamaludin Mohamad Rifki Septiadi Muhamad Lutfi Muhammad Alfan Maulana Muhammad Sulthon Muhammad Syahrul Maulana Muhammad Syahrul Maulana Mutaqin, Ahadan Fauzan Ningrum, Isna Lidia Nugroho Adhi Santoso Nugroho Adhi Santoso Nur Kholifatul Aula Nurokhman, Akhmad Nurul Fadilah, Nurul Pingky Septiana Ananda Pinky Septiana Prayoga, Alan Eka Putra, Alif Sya’Bani Rifki Dwi Kurniawan Rifki Dwi Kurniawan Rito Cipta sigitta Hariyono Rivaldiansyah, Rafik Romadhona, Wahyu Rudi Juniyanto Sagita, Rito Cipta Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nugroho Adh Santoso, Nugroho Adhi Setiawati, Windi Subechi, Fadlan Hafid Syefudin Syefudin Syefudin Syefudin, Syefudin Ubaidillah, Muhamad Rizal umar, moh azizul umar Uswatun Khasanah Wresti Andriani Yustira . Zaenal Arif Zaenul Arif Zain Hidayatullah, Fikri