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All Journal Journal of Innovation Information Technology and Application (JINITA) International Journal of Artificial Intelligence and Robotics (IJAIR)

Bonifacius Vicky Indriyono

Universitas Dian Nuswantoro

Author-ID : 5666708

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document

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Articles

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

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

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%.

Co-Authors Erna Alimudin Evita Citra Yustiqomah Iffah Irdinawati Imelda Dimentieva Moch. Sjamsul Hidajat Natalinda Pamungkas Nur Budi Nugraha Pita Mellati Pratama, Zudha Tri Esti Rahayuningtyas Wildan Mahmud

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