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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
Proyeksi Data dan Analisis Sentimen Penggunaan Vaksin di Kabupaten Indragiri Hulu Berbasis Machine Learning Ahmad Rizky Andriawan; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 2 (2021): Desember 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3569

Abstract

Sentiment analysis is research that processed used computer that comes from opinion and emotion wich realized in shape of text. The method that used to analyze sentiment is using Text Mining used for mining the text in order to get comprehension about the important aspect. Text Mining done at social media Twitter. This research analyze public sentiment related with vaccination. Step of Preprocessing contains Crawling Data, Cleaning, Filtering, Stemming, TF-IDF, and labeling. Result from labeling and percentage calculation get percentage that Positive Sentiment is 29.17%, Negative Sentiment is 55.09% percentage, and Neutral is 15.74% percentage. Could be seen that Indonesian public still given many Negative Comments related with vaccination. Result of K-NN calculation with K-9 generate the accuracy is 84.53%.
Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau Dedi Pramana; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 2 (2021): Desember 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3570

Abstract

Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China.  This virus continues to grow until it spreads to various countries in the world.  As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19.  Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients.  Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital.  This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm.  The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%.  The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.
Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik Qurotul A'yuniyah; Ena Tasia; Nanda Nazira; Pangeran Fadillah Pratama; Muhammad Ridho Anugrah; Jeni Adhiva; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4781

Abstract

Degenerative disease is a non-communicable disease that arises from an unhealthy lifestyle, so that it can reduce the physical and mental quality of the sufferer. Chronic Kidney Disease (CDK) is a degenerative disease that is included in the world's top 10 causes of death according to the World Health Organization (WHO). This study used CDK data with attributes of age, blood pressure, weight, albumin levels, sugar levels, red blood cells, pus cells, pus cell clots, bacteria, blood sugar levels, blood urea levels, creatinine serum, sodium, magnesium, hemoglobin, the volume occupied by red blood, indications of hypertension, indications of diabetes mellitus, indications of coronary heart disease, appetite, indications of swelling in the calves or feet, and indications of anemia. Therefore, the classification of kidney disease data is carried out with the implementation of the superior Naïve Bayes Classifier (NBC) algorithm and produces a high level of accuracy. The classification results using the RapidMiner tools carried out by the application of the NBC algorithm, the accuracy value is 96.43%, the average recall is 93.18%, the average precision is 93.02%, and the AUC is 93.2%. so it can be concluded that the performance of NBC in classifying chronic kidney disease data is excellent.
Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN) Delvi Nur Aini; Bella Oktavianti; Muhammad Jalal Husain; Dian Ayu Sabillah; Said Thaufik Rizaldi; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4813

Abstract

Agriculture is one of the largest economic driving sectors in Indonesia. The Central Statistics Agency (BPS) in 2021 recorded that 37.02% of Indonesia's population worked in the agricultural sector. The problem faced by farmers today is the decline in yields, both in quantity and quality due to unpredictable weather, making it difficult for farmers to choose the types of plants that are suitable for planting. The application of data mining techniques has problems related to the complexity of weather parameters and natural conditions that support agricultural production, so it is very important to do feature selection, namely to form the most relevant features. This study conducted an experiment to determine the effect of implementing the Principal Component Analysis (PCA) selection feature on the performance of the K-Nearest Neighbor (KNN) algorithm which produces the highest accuracy of 99.64% in this study.
Co-Authors Adyah Widiarni Ahmad Harun Ahmad Rizky Andriawan 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 Qurotul A'yuniyah Rice Novita Said Thaufik Rizaldi Sania Fitri Octavia Siti Monalisa Suryani Suryani