eprintid: 39759
rev_number: 10
eprint_status: archive
userid: 12259
dir: disk0/00/03/97/59
datestamp: 2020-07-20 03:53:30
lastmod: 2020-07-20 03:53:30
status_changed: 2020-07-20 03:53:30
type: thesis
metadata_visibility: show
creators_name: TUNDO, NIM. 18206050001
title: ANALISIS FUZZY INFERENCE SYSTEM METODE TSUKAMOTO DALAM MEMPREDIKSI JUMLAH PRODUKSI MINYAK KELAPA SAWIT MENGGUNAKAN BASE RULE DECISION TREE
ispublished: pub
subjects: TB
divisions: sains_mi
full_text_status: restricted
keywords: Fuzzy Logic, Fuzzy Inference System, J48, REPTree, Random Tree, Fuzzy Tsukamoto
note: Dr. Shofwatul ‘Uyun, S.T., M.Kom
abstract: This study explains the J48, REPTree and Random Tree decision tree analysis using Tsukamoto's fuzzy method in determining the amount of palm oil production in PT Tapian Nadenggan's company with the aim of finding out which decision tree results are close to the actual data. The decision tree J48, REPTree, and Random Tree is used to accelerate the making of rules that are used without having to consult with experts in determining the rules used. Based on the data used the accuracy of the rule formation of the J48 decision tree is 95.2381%, REPTree is 90.4762%, and the Random Tree is 95.2381%. The results of the study have calculated that the Tsukamoto fuzzy method using REPTree has a smaller Average Forecasting Error Rate (AFER) rate of 23.17% compared to using J48 of 24.96% and Tree Random of 36.51% in the prediction of the amount of palm oil production. Therefore an idea was found that the accuracy of decision trees formed using WEKA tools does not guarantee the greatest accuracy is the best, the proof of this case REPTree has the smallest rule accuracy, but the predicted results have the smallest error rate, compared to J48 and Random Tree.
date: 2020-04-24
date_type: published
pages: 189
institution: UNIVERSITAS ISLAM NEGERI SUNAN KALIJAGA
department: Pascasarjana
thesis_type: masters
thesis_name: other
citation:   TUNDO, NIM. 18206050001  (2020) ANALISIS FUZZY INFERENCE SYSTEM METODE TSUKAMOTO DALAM MEMPREDIKSI JUMLAH PRODUKSI MINYAK KELAPA SAWIT MENGGUNAKAN BASE RULE DECISION TREE.  Masters thesis, UNIVERSITAS ISLAM NEGERI SUNAN KALIJAGA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/39759/1/18206050001_BAB-I_IV-atau%20-V_DAFTAR_PUSTAKATA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/39759/2/18206050001_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf