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Multimode cocoa1/3/2024 Juanda No.95, Cempaka Putih, Ciputat Timur.Į-mail: Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. of Informatics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.ģrd Floor, Dept. 27–37, 2017.Ĭopyright (c) 2021 Iin Intan Uljanah, Shofwatul Uyun Atajeromavwo, “A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis,” Appl. Fauziati, “A comparative study on fuzzy Mamdani-Sugeno-Tsukamoto for the childhood tuberculosis diagnosis,” AIP Conf. Ibrahim, “Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management,” Int. Girsang, “Mobile decision support system to determine Toddler’s nutrition using fuzzy Sugeno,” Int. Felipe, “Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization,” Comput. Ramkumar, “A modified ANP and fuzzy inference system based approach for risk assessment of in-house and third party e-procurement systems,” Strateg. Liang, “GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China,” Comput. Jankowski, “Spatially-explicit integrated uncertainty and sensitivity analysis of criteria weights in multicriteria land suitability evaluation,” Environ. Djaenudin, “Perkembangan penelitian sumber daya lahan dan kontribusinya untuk mengatasi kebutuhan lahan pertanian di indonesia,” J. Wahyudi, “Analisis Kebijakan Pengembangan Industri Hilir Kakao (Suatu Pendekatan Sistem Dinamis) Policy Analysis of Cocoa Downstream Industry Development (a System Dynamic Approach),” Inform. Rubiyo and Siswanto, “Peningkatan Produksi dan Pengembangan Kakao (Theobroma cacao L.) di Indonesia,” J. Hidayanto, “Peningkatan produksi dan mutu kakao melalui kegiatan Gernas di Kalimantan Timur,” in Pros Sem Nas MAsy Biodiv Indon, 2015, vol. Anga, “The world cocoa economy: current status, challenges and prospects,” Multi-year Expert Meet. Based on the testing result, it can be concluded that the multi-layer inference Fuzzy Tsukamoto for determining the land suitability class of cocoa plants has an accuracy level amounted 97%. The concept of inference process in Fuzzy Tsukamoto is calculating the weighted average of each result of the nference process. The first layer covers seven inference engines, while each of the second and the third ones only consists of one inference engine. Generally, the algorithm used consists of three main steps those are fuzzification, Tsukamoto inference machine, and defuzzification consisting of three layers. This research uses 18 input variables including 15 non-linguistic variables or crisp and the rest are linguistic ones or fuzzy as the data of growth requirements of cocoa plants. This research aims at implementing the algorithm to determine the land suitability class of cocoa plants using the Multi-Layer Inference Fuzzy Tsukamoto (MLIFT). Determining the land suitability class of plants specifically cocoa (Theobroma cacao) is significant to do because each plant has a different characteristic of growth.
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