Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Masyarakat Informatika Journal of Innovation Information Technology and Application (JINITA) International Journal of Artificial Intelligence and Robotics (IJAIR)
Bonifacius Vicky Indriyono
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Implementasi K-Means Clustering Pada Sistem Pakar Penentuan Jenis Sayuran Nur Budi Nugraha; Erna Alimudin; Bonifacius Vicky Indriyono
Journal of Innovation Information Technology and Application (JINITA) Vol 4 No 2 (2022): JINITA, December 2022
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (869.542 KB) | DOI: 10.35970/jinita.v4i2.1627

Abstract

Indramayu is one of the regencies in West Java Province that produces food crop production in the agricultural and plantation sectors. Many people work in the agricultural sector, one of which is by growing vegetables on their land. Planting vegetables on land in the Indramayu area often experiences problems, for example the land used is not in accordance with the type of vegetables grown. Some farmers have difficulty in evaluating land due to their lack of understanding of the land to be planted so that farmers rely on the system of planting habits that they usually do. The wrong land selection can result in energy and financial losses used for maintenance. Vegetables will develop imperfectly, even vegetables can die because of the inappropriateness of the land used. This study aims to assist farmers in determining what types of vegetable crops can be planted on their land using the k-means clustering method. There are 7 data criteria used for processing k-means so that later it can produce output a recommendation of vegetable types that can be planted by farmers according to the criteria they enter into the system. The results of this study produce an expert system that can provide information about vegetables selected according to the criteria selected by each user and with this system, ordinary people can find out how the selection of types of vegetables is practically on the land to be planted with vegetables.
Expert System for Detecting Diseases of Potatoes of Granola Varieties Using Certainty Factor Method Bonifacius Vicky Indriyono; Moch. Sjamsul Hidajat; Tri Esti Rahayuningtyas; Zudha Pratama; Iffah Irdinawati; Evita Citra Yustiqomah
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.132 KB) | DOI: 10.25139/ijair.v4i2.5312

Abstract

The low productivity of potatoes is caused by many factors, including the very low quality of the seeds used, poor storage, climate, capital, limited farmer knowledge, and attacks by plant-disturbing organisms, especially diseases. Not only that, many farmers are still unfamiliar with the various diseases that can attack potato plants, or their knowledge about potato plant diseases is incomplete. This study aims to design and develop an expert system web-based application technology using the Certainty Factor (CF) method to detect potato disease symptoms. The CF method defines a measure of the capacity of a fact or provision to express the level of an expert's belief in a matter experienced by the concept of belief or trust and distrust or uncertainty contained in the certainty factor. The results showed that the CF method could function optimally in detecting potato plant diseases which can help farmers based on the symptoms that appear with an accuracy value of 94%.
Expert System for Detection of Diseases in Layers Using Forward Chaining and Certainty Factor Methods Kevin Febrianto; Erika Devi Udayanti; Bonifacius Vicky Indriyono; Wildan Mahmud; Iqlima Zahari
Jurnal Masyarakat Informatika Vol 14, No 2 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.2.52266

Abstract

Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.
Co-Authors Erika Devi Udayanti Erna Alimudin Evita Citra Yustiqomah Iffah Irdinawati Iqlima Zahari Kevin Febrianto Moch. Sjamsul Hidajat Nur Budi Nugraha Pratama, Zudha Tri Esti Rahayuningtyas Wildan Mahmud