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All Journal Techno.Com: Jurnal Teknologi Informasi ILKOM Jurnal Ilmiah Jurnal Manajemen Informatika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Business Intelligence (IJUBI) Jurnal Mnemonic Journal of Information Technology and Its Utilization Journal of Applied Data Sciences Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ilmiah Edutic : Pendidikan dan Informatika AnoaTIK: Jurnal Teknologi Informasi dan Komputer Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Journal of Information Systems and Technology (JISTech) Mekongga: Jurnal Pengabdian Masyarakat Media of Computer Science JROCK (Journal Research on Computing Knowledge) Jurnal Sistem Informasi dan Teknologi Informasi Journal of Information Technology and its Utilization
alders paliling
Universitas Sembilanbelas November Kolaka
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : Media of Computer Science

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Expert System for Determining Diseases and Pests in Seaweed Using Forward Chaining (Case Study : Watorumbe Village, Mawasangka Tengah) Asriani, Ika; Muchtar, Mutmainnah; Ismail, Rima Ruktiari; Paliling, Alders; Sya'ban, Kharis; Karim, Rahmat
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.175

Abstract

Seaweed is a marine organism that plays a crucial role in both ecosystem and economy. However, it often faces attacks from diseases and pests that can jeopardize the productivity and sustainability of the seaweed industry. Hence, the development of an expert system to diagnose seaweed diseases and pests becomes imperative. This research aims to develop an Expert System for Determining Diseases and Pests in Seaweed using the Forward Chaining method, with a case study conducted in the Watorumbe Village, Mawasangka Tengah Sub-district, Southeast Sulawesi. The Forward Chaining method is employed to identify symptoms appearing in seaweed and determine potential diseases or pests. Testing is carried out with 30 data samples compared against expert diagnoses, resulting in an accuracy rate of 90%. Therefore, this system has the potential to assist seaweed farmers in diagnosing diseases and pests more quickly and accurately, thereby enhancing the productivity and sustainability of seaweed cultivation efforts.
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 Agies Al Kasyaf, Muhammad Akbar, Muh. Hajar Andi ilham, Andi Anjar Pradipta Anton Yudhana Asriani, Ika Asrul Syam Basrawi Bulaka, Bardan Eko Harianto, Eko Ery Muchyar Hasiri Fahmi Fardian, Fardian Hadi Sasongko, Hadi Halil, Nur Ihsan Ismail, Rima Ruktiari Iyan Nurdiyan Haris Johar Nur Iin Kusuma , Damar Yoga La Ode Nursalam, La Ode LM. Fajar Israwan Mardianto Mardianto Mardianto Mardianto Mardianto Mardiawati Mardiawati, Mardiawati Marlina Mustafa, Marlina Miftachurohmah, Nisa Muh Nurtanzis Sutoyo Muh. Nurtanzis Sutoyo Mutmainnah Muchtar Nisa Miftachurohmah Nurtanzis Sutoyo, Muh. Rahmat Karim Reskiawan, Bimas - Sabi, Musini Saputra, I Gede Purwana Edi Sunyanti, Sunyanti Sutoyo, Muhammad Nurtanzis Sya'ban, Kharis Syaban, Kharis Tundreng, Syarifuddin Yiyuni