Article Per Year (5 Year)
p-Index From 2020 - 2025
1.006
P-Index
This Author published in this journals
All Journal JURNAL PengaMAS Jurnal Mnemonic Al-Hijr: Journal of Adulearn World TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang Media of Computer Science
Miftachurohmah, Nisa
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Media of Computer Science

 1 
Expert System For Identification Of Symptoms And Diseases In Lobsters Using The Backward Chaining Method Yiyuni; Miftachurohmah, Nisa; Paliling, Alders; Mardiawati; Sya'ban, Kharis
Media of Computer Science Vol. 2 No. 1 (2025): June 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i1.232

Abstract

Lobster is a high-value fishery commodity widely cultivated, including in Watorumbe Bata Village. However, diseases attacking lobsters are often difficult for farmers to identify early, leading to economic losses due to delayed treatment. This study aims to develop an expert system to identify lobster symptoms and diseases using the backward chaining method. This method enables the system to reason logically and systematically from disease hypotheses to symptom facts. Data collection was conducted through observation, interviews with lobster experts, and literature study. The system development followed the Waterfall model, comprising analysis, design, coding, and testing phases. The implementation results show that the system can diagnose diseases based on symptom inputs and provide information including disease name, cause, solution, and likelihood level. Black-box testing confirmed that all system functions operated properly, while accuracy testing using 20 sample data showed a system accuracy rate of 90%. These results indicate that the expert system using the backward chaining method is effective in assisting farmers to identify lobster diseases more quickly and accurately, thus supporting the sustainability and productivity of lobster farming.
Co-Authors alders paliling Ardiandi, Brukmen Arianti, Widia Bintang, Petra Deviyanti, Komang Dian Ulfa Sari Fadillah Fadillah Fusfitasari, Yenni Guilin, Xie Haikal, Fikram Hasan, Radif Ikmah, Amit Dana Jahara, Jahara Jahring, Jahring Mardiawati Meiliyah, Tri Nurul Muh Nurtanzis Sutoyo Mutmainnah Muchtar Nasruddin Nasruddin Nasuruddin, Nasruddin Rahmat Karim Riska Riska Sabi, Musini Sya'ban, Kharis Syaban, Kharis Wahida Wahida Yiyuni