Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Informatics and Electronics Engineering
Muhammad Lovano Adjie Senjaya
Politeknik Negeri Bengkalis
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sistem Pakar Deteksi Penyakit Tanaman Cabai Menggunakan Metode Forward Chaining Muhammad Lovano Adjie Senjaya; Depandi Enda
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1605

Abstract

Chili plants are a vital horticultural commodity in Indonesia, yet their productivity is frequently hindered by disease outbreaks that are difficult for novice farmers to identify. Delays in diagnosing symptoms often lead to improper treatment and significant crop losses. This study aims to develop an expert system for detecting chili plant diseases that provides pesticide recommendations based on observed symptoms. The system is built using the Forward Chaining method for reasoning from symptomatic facts to disease conclusions. The software development follows the Rapid Application Development (RAD) methodology, implemented with the PHP programming language and Laravel framework. Research data were gathered through literature reviews and interviews with plant experts. Black box testing results indicate that all system functions—including symptom data management, disease records, and rule sets—operate as designed. System validation, conducted by comparing results with diagnoses from three experts using the Fleiss' Kappa test, yielded a value of 0.464, placing the agreement level in the "Moderate" category. Consequently, this expert system is a viable tool for farmers to perform early identification of chili plant diseases and accurately determine the appropriate pesticides independently.
Co-Authors Depandi Enda