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All Journal Proceeding International Conference on Information Technology and Business Journal of Informatics, Electrical and Electronics Engineering
Rosyana Fitria Purnomo
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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RAINFALL PREDICTION USING DATA MINING TECHNIQUES Riko Herwanto; Rosyana Fitria Purnomo; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Rainfall is an important factor in agrarian countries such as Indonesia. Rainfall prediction has become one of the most challenging technological challenges and challenges in the world. And also the most significant and difficult task for researchers in recent years. In Data Mining, the classification algorithm is primarily used to predict rainfall, temperature, various methods of available rainfall estimation that will be used to determine the cultivation time for a particular crop, a particular crop varieties.The reliability of this prediction depends on accuracy in choosing correlated variables. If existing historical databases fail to record the most correlated variables, then the reliability of these data-driven forecast approaches is questionable. In this paper, an attempt has been made to develop a methodological framework that leverages the power of a predefined data mining analysis (decision tree). The decision-based rainfall prediction model developed maps climate variables, namely; a) temperature, b) humidity, and c) wind speed over the observed rainfall database.This paper uses data mining techniques such as Clustering Technique, Decision Tree and classification for rainfall prediction. Keywords: Rainfall,  Rainy Season, Data Mining,  Classification, Decision Tree, Bayesian Technique.
Penerapan Algoritma C4.5 untuk Klasifikasi Tingkat Kedisiplinan Siswa Sekolah Menengah selipuri; Rosyana Fitria Purnomo; Yodhi Yuniarthe
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2630

Abstract

Abstract?This study aims to evaluate the performance of the Decision Tree algorithm based on the entropy criterion (C4.5) in classifying student eligibility by considering both academic and non-academic data. The dataset consists of 200 entries with nine attributes, including attendance percentage, number of lateness incidents, disciplinary violations, average academic scores, participation, study hours, and extracurricular activities. Data processing was carried out through several stages, namely cleaning, transformation, feature selection, training and testing data splitting, and model evaluation using a confusion matrix. The experimental results show that the proposed model achieved an accuracy of 87.5%, an average precision of 85.6%, an average recall of 84.2%, and an F1-Score of 84.8%. These findings confirm that the C4.5 algorithm can be effectively applied to support student performance classification with a fairly high level of reliability.
Co-Authors Riko Herwanto selipuri Sriyanto - Yodhi Yuniarthe