cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Fadlan
Contact Email
fadlan@ppkia.ac.id
Phone
+6281216123988
Journal Mail Official
jbidai@ppkia.ac.id
Editorial Address
Kampus STMIK PPKIA Tarakanita Rahmawati, Jl. Halmahera 99 Oval Ladang IV Tarakan 77113 – Kalimantan Utara
Location
Kota tarakan,
Kalimantan utara
INDONESIA
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 41 Documents
 1   2   3   4   5 
Sistem Pakar Diagnosa Kerusakan Printer Menggunakan Metode Teorema Bayes Ellisa Harini; Indra Tri Saputra; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 1 (2023): JBIDAI Juni 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i1.34

Abstract

Trijaya Computer performs computer sales and repair services located on Jl. Panglima Batur, Markoni. Printer repair demands quite a lot. Its massive usage is for printing reports, documents, and other things. Extensive use or even non-use of the printer can damage it. The research applies Bayes' Theorem analysis to an expert system to help admins diagnose printer damage. The discoverer of Theorem by Reverend Thomas Bayes (1701-1761). In general, it is to calculate the probability truth value of evidence. It can also interpret to calculate data uncertainty into definitive data by comparing "yes" or "no" data. The study result was an expert system for diagnosing printer damage as a support system or admins assistant with 80% accuracy. Eight of ten test data were equal to the expert's result.
Application of K-Means Clustering for Student Class Division System Tri Martuti; Eviana Tjatur Putri; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 2 (2023): JBIDAI Desember 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i2.35

Abstract

SMP Negeri 2 Malinau Utara is a junior high school in Desa Putat, Malinau Utara, Malinau, Kalimantan Utara and has 127 students. Currently, the class division process is inefficient and random. On the other hand, the clustering process' class division must be able to provide each class a balanced number of students. This study proposes the grades of Indonesian and English languages, Mathematics, and Natural Sciences for the clustering. K-means is applied to evenly group students based on predetermined value criteria to achieve the expected class formation. K-Means Clustering is an algorithm in data analysis to group a set of data into several groups based on their similar characteristics. In the clustering process, the distance between the data and the Centroid was calculated using the Euclidean Distance. Initial centroid determination and data distance calculation with the initial centroid were performed until the centroid member remains unchanged. The initial centroid was determined using a combination of 1,081 times obtained from 47 data combinations for two clusters. This research has been successfully applied to classify students using the K-Means Clustering method and select a balanced number of students between one class and another. Next, combine some students in each cluster with other clusters, so that each class has different levels of learning ability. With the combination of two clusters in one class, it is expected that students can help each other during the learning process.
Perbandingan Metode Klasifikasi Naïve Bayes dan C4.5 untuk Menentukan Potensi Nasabah Pada NSC Finance Marhaeni; Eviana Tjatur Putri; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 1 (2023): JBIDAI Juni 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i1.36

Abstract

NSC Finance is a service business that provides loans to the public to meet their needs. However, NSC Finance does not use customer data to obtain necessary information. This research classifies customer data to gain information about promising customers, considered customers, and unpromising customers to loan re-offer. This study compares Naïve Bayes and C4.5 to help customer classification systems be more accurate by measuring accuracy using recall precision. These methods' comparative analyses are to investigate which methods have the highest classification accuracy. Therefore, the company can discover the highest accuracy rate of the classification results of these two methods. Results revealed that the classification patterns of 80 training data and 20 test data make it possible that data still have classification differences from the original data. Methods comparison indicated that the Naïve Bayes classification is better, with 85% accuracy, 94.44% precision, and 89.47% recall.
Implementasi K-Means Clustering Untuk Mengelompokan Data Sparepart Alat Berat Rahmawati, Andira; Fadlan, Muhammad; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.37

Abstract

Data mining is a crucial process for extracting valuable information from existing data, which can then be used by companies for quick and accurate decision-making. One of the commonly used methods in data mining is the K-Means Clustering method. In this study, the author applied K-Means Clustering in the retail sector to address the challenges faced by PT. Patria Jaya Mandiri. The author designed an application that can cluster heavy equipment spare parts based on sales data, with the aim of helping the company identify which spare parts are most favored by consumers. This clustering is expected to simplify the process of determining optimal spare part stock, ultimately positively impacting the company’s revenue. The results of this study indicate that heavy equipment spare parts can be categorized into three groups: Most Popular, Popular, and Least Popular. Cluster 1 (Most Popular) consists of 3 data points, Cluster 2 (Popular) consists of 39 data points, and Cluster 3 (Least Popular) consists of 8 data points. This clustering result can serve as a guide for PT. Patria Jaya Mandiri in determining the optimal spare part inventory in the future.
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.
DESAIN APLIKASI PERAMALAN PENYEWAAN LAPANGAN FUTSAL MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING Saputri, Sasya Rahayu; Sinawati, Sinawati; lusi, lusiana
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 1 (2023): JBIDAI Juni 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i1.40

Abstract

The Futsal Field is a sports venue that is usually rented out by the hour. The problem that occurs is that there is no computerized system that is used to predict the number of rental hours on the futsal field for the next period, so that the owner can know whether in the next period it will increase or decrease. Based on the description above, the author wants to create an application that can predict the amount of rent on one of the futsal fields for the next period. The author will use Brown's Double Exponential Smoothing method to calculate the forecast, then to calculate the accuracy of the forecast calculation using the Mean Absolute Percentage Error method. This research produces an application that can predict the amount of field rent in the next 3 months. The results obtained are, in field 1 for January 2020 it was 136 hours, February 2020 was 136 hours, and March 2020 was 135 hours. Then in field 2, for January 2020 it is 74 hours, February 2020 is 74 hours, and March 2020 is 73 hours. The accuracy rates for fields 1 and 2 are 2.18% and 9.42%, which means very good.
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.
Perbandingan Metode Euclidean Probability dan Teorema Bayes untuk Diagnosa Penyakit Gigi Natalia Cangera; Yusni Amaliah; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 1 (2023): JBIDAI Juni 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i1.42

Abstract

Dental disease is a disease that interferes with the normal function of the teeth. Dental disease has almost similar symptoms, so it requires an expert system of dental disease diagnosis for the proper treatment before the disease becomes more serious. The research employs Euclidean probability and Bayes' Theorem. Euclidean probability is a case approach for measuring probability based on causes, while Bayes' Theorem is a mathematical formula for determining conditional probability. Both of these methods determine the disease percentage based on the input symptoms. Their differences reflect in the calculation. Research shows that the Bayesian analysis is better than Euclidean probability, as evidenced by the similarity in the systems diagnostic with experts of 80% accuracy, while Euclidean probability is 40%.
Sistem Pakar Pendeteksi Kerusakan Mesin Genset Menggunakan Metode Case Based Reasoning Lestari Ningsih; Fitria; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 2 (2023): JBIDAI Desember 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i2.43

Abstract

PT. Sabindo Raya Gemilang is an exporter of shrimp and fish in Indonesia. This company requires an enormous flow of electricity to support activities so that it is not bothered by blackouts from the PLN. A generator set or genset is a substitute for a voltage source. The problem is that genset damage requires reliable mechanical handling. Junior mechanics sometimes misanalyze damage, requiring the supervision of a senior mechanic. This study applies an expert system with the Case-Based Reasoning (CBR) method to detect genset damage. It meets the case researched for having old cases to compare to new ones through the Retrieve, Reuse, Revise, and Retain so that problems are solved easily and quickly. The validity test showed that 5 of 6 test data were equal to the expert's diagnosis. Expert system accuracy 83%. There was a system diagnosis different from experts because the system’s diagnosis was based on the symptoms weight calculation and made a diagnosis with the highest similarity value. Meanwhile, experts observed the genset damage directly.

Page 2 of 5 | Total Record : 41

 1   2   3   4   5 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol 8 No 1 (2025): JBIDAI Juni 2025 Vol 7 No 2 (2024): JBIDAI Desember 2024 Vol 7 No 1 (2024): JBIDAI Juni 2024 Vol 6 No 2 (2023): JBIDAI Desember 2023 Vol 6 No 1 (2023): JBIDAI Juni 2023 Vol 5 No 1 (2019): JBIDAI Juni 2019 Vol 4 No 1 (2018): JBIDAI Juni 2018 Vol 3 No 1 (2017): JBIDAI Juni 2017