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All Journal Jurnal Ilmu Komputer Informasi Interaktif Prosiding SNAST
Arif Budiman
Universitas AMIKOM Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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Journal : Jurnal Ilmu Komputer

 1 
Klasifikasi kebakaran hutan menggunakan algoritma C4.5 dan Rough Set Arif budiman
Jurnal Ilmu Komputer Vol 15 No 1 (2022): April 2022
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

In recent years there have been large-scale forest fires in forested areas of the world. Forest fires are a major environmental problem that has big impact on wildlife, human health, economic. One solution can be taken is using classification algorithm to predict forest fires based on historical forest fire data. In this research using C4.5 Algorithm combined with Rough Set as feature selection to classify forests fire. Evaluate performance based on created model using confusion matrix to calculate accuracy value. The results show the C4.5 algorithm with Rough Set as feature selection was found accuracy 98.36%. The use of Rough Set as feature selection can reduce irrelevant attributes effectively.
OPTIMASI ALGORITMA C4.5 DAN NAIVE BAYES MENGGUNAKAN K-MEANS UNTUK PREDIKSI KELULUSAN MAHASISWA budiman, Arif
Jurnal Ilmu Komputer Vol 17 No 2 (2024): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Today's education system demands quality-oriented education. The quality of Indonesian higher education is measured based on accreditation issued by the Badan Akreditasi Nasional Perguruan Tinggi. One indicator of success in the process of managing education on higher education is the period of student graduation. Undergraduate students have a study load of 144 credits which can be taken in 8 semesters. but in fact, there are still many students who cannot complete their studies for 8 semesters due to various factors such as lack of motivation, intelligence factors, and economic factors. There is a need for continuous monitoring and evaluation of periods in student graduation using the C4.5 and Naive Bayes algorithms. Optimization is needed to increase the accuracy value of the C4.5 and Naive Bayes algorithms by using K-means for the data discretization process. The experimental result show C4.5 algorithm with K-means produces an accuracy value of 89.74%, a precision value of 90.60%, and a recall value of 98.00% while Naive Bayes with K-means produces an accuracy value of 80.73%, a precision value of 89.60%, a value recall of 87.20%. The comparison of two classification algorithms combined with K-means shows that the C4.5 algorithm has a better performance than Naive Bayes.
Co-Authors Adi Prasetyo Fandli Supandi Kusrini _ M Hamzah