Penerapan Data Mining Untuk Memperediksi Jumlah Hasil Panen Teh Menggunakan Metode Naive Bayes Clasifier Pada PTPN VIII Gunung Mas
Kode Repository :SKI09/MUH/23
NPM :065118048
Nama :Muhammad Aldi
Pembimbing 1 :-Dr. Ir. Hermawan Taher
Pembimbing 2 :-Dian Kartika Utami, M.Kom
Abstrak :-JURNAL TEKNOINFO
Volume X, Nomor X, Bulan Tahun, Page x-x
ISSN: 1693-0010(Print), ISSN: 2615-224X(Online)
Available online at https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/index
1
PENERAPAN DATA MINING UNTUK PREDIKSI JUMLAH HASIL PANEN TEH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER PADA PTPN VIII GUNUNG MAS
Muhammad Aldi1), Dian Kartika Utami2), Hermawan Taher3), Sufiatul Maryana4)
1,2,3,4Program Studi Ilmu Komputer, Universitas Pakuan
Jl. Pakuan No.1 Ciheuleut, Bogor
Email: aldi.065118040@unpak.ac.id
Abstract
This study aims to predict the amount of tea yields as a recommendation for PTPN VIII Gunung Mas so that it can pay more attention to efforts to produce a maximum amount of tea yields each year. In predicting the amount of tea yields, the method used is the Naïve Bayes Classifier. For research data obtained from the park, namely PTPN VIII Gunung Mas. So that this research is expected to be able to help related agencies, especially company leaders, so that they can make the right decisions in the future. Based on the results of research that has been carried out by the author using the Naïve Bayes Classifier method with Google Colab tools, the python programming language for predictions for 2022 and 2023. In 2022 the accuracy obtained from the Naïve Bayes Classifier is 0.78 while the ROC Score for the Naïve Bayes Classifier is 0.722. Class increases occur in block 6, block 22, and the remaining 21 blocks get decreased results. With a tea yield of 1,248,930 kg. The prediction for 2023 is that the accuracy obtained from the Naïve Bayes Classifier is 0.78, while the ROC Score for the Naïve Bayes Classifier is 0.911. Class increases occur in block 6, block 13 and the remaining 21 blocks get decreased results. With a tea yield of 1,160,255 kg.
Keyword: Tea, Classification, Data Mining, Prediction, Naïve Bayes Classifie