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All Journal Scientific Journal of Informatics Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat Intechno Journal : Information Technology Journal
Arifin, Oki
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Nursing

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Journal : Scientific Journal of Informatics

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Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.
Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Purpose: This study aims to developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC), which expected will give information and can use by the farmer and industrial food crops. Methods: On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. Result: The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438. Novelty: Prediction system of food crops by data mining.
Co-Authors Alim, M. Fathin Abdul Annisa Fitri, Annisa Eko Win Kenali Fanti, Firli Nur Fitri, Aulia Raey Haldian, Haldian Halim Fathoni, Halim Kurniawan Saputra zukryandry, zukryandry