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All Journal Building of Informatics, Technology and Science Bulletin of Informatics and Data Science
Suryani Suryani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Electrical & Electronics Engineering Engineering

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Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine Suryani Suryani; Mustakim Mustakim
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

SMK Negeri 5 Pekanbaru aims to prepare competent graduates who can compete in the global market. The realization of these goals is influenced by student achievement at school. Student achievements determine the ability of students to work in certain fields. Based on observations, it is known that student achievement at SMK Negeri 5 Pekanbaru tend to be low. This is also shown by the data that has been collected through the Curriculum section. Based on the data, there can be extraction using the supervised learning method to make a classification model of student achievements. The supervised learning algorithm used in this research is a Support Vector Machine (SVM). The data used in this study are student's data grade X SMK Negeri 5 Pekanbaru in 2020 totaling 160 data. The classification process is carried out by applying the GridSearch method to find the best kernel to be implemented. Based on the implementation of GridSearch, the kernel to be used is Radial Basis Function (RBF) with Cost (C) and Gamma (?)  parameters. Based on 16 experiments with different parameter values, the best classification results are obtained using the value of  Cost (C) = 0.1 and the value of Gamma (?)  = 0.01, with accuracy values of 94%.
Klasifikasi Text Dokumen Web Berbasis Supervised Learning Sebagai Pemodelan Aplikasi Pembelajaran Kebudayaan Melayu di Indonesia Mustakim Mustakim; Febi Nur Salisah; Suryani Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8499

Abstract

Indonesia, as the largest archipelagic country, is home to diverse cultures, including Malay culture in Riau Province. The website features numerous text documents, including articles, news, and personal documents, uploaded by members of the cultural community. This study aims to support the preservation of Malay culture through technology by implementing a digital learning system based on Machine Learning. Previous research has identified weaknesses in the application of intelligent systems and machine learning algorithms. This study tests five classification algorithms Random Forest, SVM, Naïve Bayes, KNN, and PNN to improve the system's accuracy and performance. The results show that Random Forest achieved the highest accuracy of 91.17%, followed by KNN at 88.23%, SVM and NBC at 82.35%, and PNN at 76.47%. The developed Digital Learning System (DLS) received positive feedback, with a User Acceptance Test (UAT) score of 86% and a 100% success rate in Blackbox testing, demonstrating stable performance across various devices. This research introduces a new innovation in Malay cultural preservation applications, utilizing Machine Learning algorithms to enhance both accuracy and functionality.
Co-Authors Febi Nur Salisah Mustakim Mustakim