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All Journal Jurnal Teknologi Informasi dan Pendidikan Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Alfin Syarifuddin Syahab
Universitas Teknologi Yogyakarta
Computer Science & IT Control & Systems Engineering Education Engineering

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Journal : Jurnal Teknologi Informasi dan Pendidikan

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Komparasi Algoritma Machine Laearning untuk Klasifikasi Indeks Ultraviolet Alfin Syarifuddin Syahab; Andriyan Widiyanto; Luky Rafi Anuggilarso; Anugerah Bagus Wijaya
Jurnal Teknologi Informasi dan Pendidikan Vol 15 No 2 (2022): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v15i2.692

Abstract

Indonesia is a tropical climate country which has the potential for high intensity of sunlight exposure. Several types of exposure that receives are ultraviolet rays. According to the National Agency for Meteorology, Climatology and Geophysics, it provides information regarding the impact on human activities in an ultraviolet index which has a risk scale. The aim of this research was creating a recommender system based on the ultraviolet index category in providing daily activity advice to users. The methods used the K-nearest Neighbor algorithm and Support Vector Machine with a Collaborative Filtering Model-Based approach that could recommend items based on the results of a model trained to identify input data patterns. The stages carried out in this study included data collection, data pre-processing, data division into test data and train data, dataset testing, analysis of the results of models that had been trained in the accuracy values using the algorithm tested. The results of the confusion matrix calculation produced test evaluations in accuracy values, precision values, and recall values. The comparison of result had the highest performance in K-nearest Neighbor with an accuracy value of 99.69%, a precision value of 99.00%, and a recall value of 96.20%. In research used the Support Vector Machine showed the lowest performance with an accuracy value of 97.91%, a precision value of 93.20%, and a recall value of 86.40%.
Co-Authors Andriyan Widiyanto Anugerah Bagus Wijaya Enggar Novianto Ilham Fannani Luky Rafi Anuggilarso