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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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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.
PENERERAPAN METODE DEMPSTER-SHAFER DALAM PEMBANGUNAN SISTEM PAKAR DIAGNOSA PENYAKIT KULIT Fandli Supandi; Arif Budiman; Kusrini _
PROSIDING SNAST Prosiding SNAST 2018
Publisher : IST AKPRIND Yogyakarta

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The skin is the outermost part of the human body because they have experienced many skin problem. Skin diseases are usually due to bacterial, fungal, viral, and allergic infections. But there are still many people who do not care about skin health and lazy to consulting with a dermatologist because of several factors such as economy, busyness, doctor working time, and so forth. With this the authors will build an expert system that can diagnose about skin diseases that can be accessed online through the browser. The way of data collection is done by direct interview with the dermatologist. Data obtained from experts then written in the design of the system by using the method Dempster-Shafer, php programming language and mysql database. The dempster-shafer method for calculating probabilities is based on the values ​​of an expert on the symptoms felt by the sufferer affecting the type of illness. The result of this research is a prototype application of expert diagnosis system that allows patients to diagnose symptoms that he felt through accessible websites without must to consulting with dermatologist. Results compared to the diagnosis with the expert and by the system more or less equal to the difference of about 5 - 8%.
SISTEM PENDUKUNG KEPUTUSAN PENERIMA DANA DESA DENGAN MENGGUNAKAN METODE PROMETHEE DI KECAMATAN GODEAN KABUPATEN SLEMAN Arif Budiman; Adi Prasetyo; M Hamzah
Informasi Interaktif Vol 4, No 3 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

Decision support systems for village funds were used to assist the head of the Community and Village Empowerment Agency in deciding the priorities of recipients of village funds. Village funds can be used for the purposes of financing governance, development and community empowerment. The method used in designing and making this application is the PROMETHEE method for ranking the priorities of village fund recipients in sleman district. Village funds are funds originating from the APBN intended for villages that are transferred through the APBD and used to finance the administration, implementation, development, community development, and empowerment of village communities. The purpose of this study is to develop a decision support system that can be used by regional heads as a tool to assist in the selection of priority recipients of village funds to be given funds. Based on calculations using the Promethee method, the results obtained ranking with the highest net flow value of 0.292 namely A4 so that the village of Sidokarto will be the priority of the recipient of village funds in the district Sleman Keywords : Village Funds, Decision Support System, Promethee
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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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