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Journal of Big Data Analytic and Artificial Intelligence
Published by Sekolah Tinggi Ilmu Manajemen Informatika dan Komputer Tarakanita Rahmawati
ISSN : 25979604     EISSN : 27223256     DOI : https://doi.org/10.71302
Core Subject : Science,
Computer Science & IT
JBIDAI adalah jurnal nasional berbahasa Indonesia versi online yang dikelola oleh Prodi Sistem Informasi STMIK PPKIA Tarakanita Rahmawati. Jurnal ini memuat hasil-hasil penelitian dengan cakupan fokus penelitian meliputi : Artificial Intelligence, Big Data, Data Mining, Information Retrieval, Knowledge Doscovering in Database dan bidang-bidang lainnya yang termasuk ke dalam rumpun ilmu tersebut.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol 7 No 1 (2024): JBIDAI Juni 2024" : 5 Documents clear
Implementasi Algoritma K-Medoids Dalam Mengelompokkan Siswa Berdasarkan Keaktifan Dalam Proses Pembelajaran Ih’Diati, Noor Oktavia; Anto, Anto; Rosmini, Rosmini
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.38

Abstract

Grouping students based on their level of engagement is an effective strategy to improve the quality of learning. SMP 9 Tarakan currently does not have a system that can group students based on their engagement in the learning process, which could assist in evaluating learning outcomes. In the initial stage of applying this method, the data collected came from the report card grades of 8th-grade students (Class VIII I) in the 2nd semester (Even Semester) of the 2022/2023 academic year. The characteristics used in the analysis include grades in Religion, Civic Education (PPKn), Mathematics, Science (IPA), Social Studies (IPS), Indonesian Language, English, Physical Education (Penjaskes), and Cultural Arts and Skills, with a total of 31 data points analyzed. The second step is to determine the number of clusters. The third step involves randomly selecting clusters with an initial medoid. The fourth step is to calculate the distance for each student using the Euclidean distance method, then mark the nearest distance and calculate the total distance. The fifth step is to calculate the total deviation (S) and use the Davies-Bouldin Index (DBI) to find the optimal value of k by conducting tests five times with k=3. Based on the calculation results, the analysis of student data grouping produced three clusters using Euclidean distance and Davies-Bouldin Index calculations. The results show that 3 students fall into the Highly Interested cluster, 4 students into the Interested cluster, and 24 students into the Less Interested cluster.
Implementation of Data Mining for Sales Data Clustering Using K-Medoids Algorithm Kamila, Nurul Cahaya; Fitria, Fitria; M. Hafid
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.39

Abstract

This study aims to assist a trinket shop in achieving its monthly sales targets by applying data mining techniques using the K-Medoids clustering method. The research was conducted in six main stages: (1) data collection, (2) data cleaning, (3) data mining implementation, (4) evaluation of clustering results using the Davies-Bouldin Index (DBI), (5) determination of the optimal number of clusters (best k), and (6) visualization of clustering results. The data used consists of three selected attributes out of six available attributes. The clustering process with the K-Medoids method produced varying clusters due to the random selection of centroids. Based on the DBI evaluation, the optimal number of clusters was found to be k=3, providing the best clustering results to support the shop's marketing strategies.
Implementasi Data Mining Menggunakan Algoritma Hash Based Terhadap Pola Pengeluaran Obat Pada Rumah Sakit Pertamina Tarakan Febriyan, Febriyan; Juliansyah, Rizky; Sinawati, Sinawati; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.41

Abstract

The role of information systems has expanded to other layers of life, especially in the health sector. Improving service and performance productivity in the health sector, including pharmacies. This study identifies drug dispensing patterns at the Pertamina Tarakan Hospital.This research was carried out in several stages. First, create tabular transaction data. Second, determine minimum support. Third, determine the memory address. Fourth, calculate the minimum confidence. Fifth, calculate confidence, and last, make a decision.This study focuses on Rs Pertamina Tarakan to identify common spending patterns. For example, in the previous 12 months, what drug combination was most commonly administered to patients. Knowing dispensing patterns allows Rs Pertamina Tarakan to arrange the layout of medicines, resulting in more optimal and excellent service in the medicine administration department. The results showed that in 2022, three pairs of drug combinations placed close together, namely Ranitidin, Urdahex Cap first pair, Urdahex Cap, Folamil genio kap, Propylathiorasil second pair, and Propylathiorasil, Ranitidin, and Urdahex Cap  third pair.
Sistem Pakar Dalam Penentuan Minat dan Bakat Anak Usia Taman Kanak-Kanak Menggunakan Metode Dempster Shafer Devaus, Lea; Amaliah, Yusni; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.53

Abstract

Early childhood education relates to the teaching of children from birth up to the age of six, which prepares children for further education by providing educational stimulation to aid physical and spiritual growth. Thus, it is necessary a system to help parents learn their children's interests and talents to direct and develop their potential, especially in the arts. Dempster Shafer is a mathematical theory of evidence based on belief function and plausible reasoning to combine discrete pieces of information to calculate the probability of an event. The results show that children's interest and talent in art at Paud Pelangi Desa Sedulun consists of 5 types: Mosaic Art, Acting Art, Music Art, Montage Art, and Painting Art. Manual calculations and computerized tests produce the highest percentage value and the final conclusion. Determining children's interests and talents from the input of behavioral criteria generates a final assessment that helps children's development in the arts.
KLASIFIKASI KELAYAKAN MENERIMA BANTUAN SOSIAL MENGGUNAKAN METODE K-NEAREST NEIGHBOR Melpin, Melpin; Praseptian M, Dikky; Obert, Obert
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.54

Abstract

Data classification is the process of grouping data based on attributes (Congregation Employment, Congregation Dependents, Congregation Home Status, and Congregation Income). The problem currently occurring is that data collection on congregational social assistance recipients is often not on target, so that if social assistance enters the church, it is given to congregations who are actually less well off, but it is transferred to congregations who are well off, giving rise to confusion between one congregation and another. In this research the author used the K-Nearest Neighbor method or what is usually called KNN and measured algorithm performance using a confusion matrix to calculate accuracy, precision and recall. This researcher used 50 data that had been input via Google Form and then filled in the congregation from 50 data divided into 35 training data and 15 testing data. After the data has been input it will go through several stages, the first step is initialization where in the process of this initialization stage it changes the category value, the second stage is the process of dividing the value by the largest value in the attribute and the third stage is calculating the distance to then calculate the confusion matrix to determine accuracy, precision and recall. This research produces an application that can automatically determine which congregations are and are not worthy of receiving social assistance. From trials of 15 testing data, accuracy was 88.89%, precision 100% and recall 75%

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