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dc.contributor.advisorHarahap, Lukman Adlin
dc.contributor.authorAkbar, Abdi
dc.date.accessioned2025-10-13T03:20:41Z
dc.date.available2025-10-13T03:20:41Z
dc.date.issued2025
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/109307
dc.description.abstractProductivity estimation is one of the important aspects that can provide information for effective and efficient decision making, especially in the shallot farming sector in the Deli Serdang area. This study aims to predict the productivity value of shallot plants (Allium Ascalonicum L.) using machine learning, especially in Deli Serdang district. In this study, two machine learning algorithms were used; namely Backpropagation Neural Network (BPNN) and Deep Neural Network (DNN) to predict shallot productivity based on historical data which includes climate data, harvest area and production in the Deli Serdang area for the last 10 years. The results show that for the best BPNN prediction value is MAE of 0.638 from the value of harvest area and MAPE of 6.553 from the value of productivity, while for the DNN prediction value, the best result is MAE of 0.575 from the value of harvest area and MAPE of 2.161 from the value of productivity.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectEstimationen_US
dc.subjectForecastingen_US
dc.subjectMachine Learningen_US
dc.subjectBackpropagation Neural Network (BPNN)en_US
dc.subjectDeep Neural Network (DNN)en_US
dc.subjectShallotsen_US
dc.subjectDeli Serdangen_US
dc.titlePendugaan Produktivitas Komoditi Bawang Merah (Allium Ascalonicum L.) dengan menggunakan Machine Learningen_US
dc.title.alternativeEstimation of Shallot (Allium ascalonicum L.) Productivity Using Machine Learningen_US
dc.typeThesisen_US
dc.identifier.nimNIM180308100
dc.identifier.nidnNIDN0009068202
dc.identifier.kodeprodiKODEPRODI54208#Teknik Pertanian dan Biosistem
dc.description.pages56 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 9. Industry Innovation And Infrastructureen_US


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