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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Informatics and Data Science Bulletin of Artificial Intelligence
Mustakim Mustakim
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Algoritma Association Rules Dalam Penentuan Pola Pembelian Berdasarkan Hasil Clustering Sania Fitri Octavia; Mustakim Mustakim; Inggih Permana; Siti Monalisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6129

Abstract

Zanafa Bookstore is one of the bookstores in Pekanbaru city that is required to meet customer needs and has the right focus in developing sales strategies every day. During the new school year there is an increase in sales, it is known that in July there are the most purchase transactions which are the beginning of the new school year for students and students. In addition, the placement of the book layout is only based on the employee's estimated shelf so that it will affect the convenience of consumers in choosing and finding books if the books are arranged far apart. By placing the layout in accordance with consumer purchasing patterns, it can improve the quality of customer service in bookstores. The book layout can also be used as a reference when adding book stock, information is needed by utilizing transaction data using data mining, namely by using Association rules commonly called Market Basket Analysis. This research uses K-Medoid for clustering on Apriori and FP-Growth in generating rule patterns on large-scale data. Several experiments were conducted on K-Medoid starting from cluster 2 to cluster 7, each of which will be applied to Apriori and FP-Growth with 30% support and 70% confidence. By comparing the evaluation results of each algorithm with each other, it is known that FP-Growth has superior results to Apriori with a total strength of rules of 1.2012. So that the results of the association rules obtained can be used as a reference in the placement of book layouts in the Zanafa bookstore.
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%.
Implementasi Algoritma Support Vector Machine untuk Analisis Sentimen LGBT di Indonesia Mustakim Mustakim; Muhammad Ridwan; Nanda Try Luchia
Bulletin of Artificial Intelligence Vol 1 No 2 (2022): October 2022
Publisher : Graha Mitra Edukasi

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Abstract

LGBT cases began to appear openly in Indonesia in 2016. This case has received a lot of discussion in that year until now because of the number of people who commented agreeing and disagreeing with actions, activities, and the existence of the LGBT gender in Indonesia. The sentiments from the community's comments refer to various aspects of life so as to produce community opinions that are positive, negative and neutral. Seeing this, it is necessary to perform a classification and analysis of tweet sentiments to see the tendency of each community's opinion. Analysis and classification is done with text mining data processing techniques using the Support Vector Machine (SVM) algorithm. The classification process is done in 3 stages with the division of data 90%:10%, 80%:20% and 70%:30% using 3 kernels namely linear, polynominal and Radial Basic Function (RBF). The classification results obtained from the three kernels show that the tendency of society's view of LGBT cases is negative and neutral which is shown with the highest accuracy on the linear and RBF kernels. The SVM experiment produced an accuracy of 74% on the linear kernel with 90%:10% and 74% data experiments and on the RBF kernel with C=100 gamma=0,01. The grouping of this tweet sentiment data resulted in an analysis of the tendency not to support or disagree with the LGBT gender because it is not in accordance with the established basis in the country of Indonesia which prioritizes religious aspects over other aspects
Co-Authors Adyah Widiarni Ahmad Harun Bella Oktavianti Dedi Pramana Delvi Nur Aini Dian Ayu Sabillah Dzul Asfi Warraihan Ena Tasia Inggih Permana Jeni Adhiva Kharisma, Oktaf Brillian M Afdal Muhammad Jalal Husain Muhammad Ridho Anugrah Muhammad Ridwan Nanda Nazira Nanda Try Luchia Pangeran Fadillah Pratama Qurotul A'yuniyah Rice Novita Said Thaufik Rizaldi Sania Fitri Octavia Siti Monalisa Suryani Suryani