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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 6 No 2 (2023): JBIDAI Desember 2023" : 5 Documents clear
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.
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.
Analisis Sentimen pada Ulasan Penyedia Layanan Menggunakan Algoritma C4.5 Miske Marcillia; Muhammad Fadlan; Hartono, Lies
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.v6i2.45

Abstract

Sentiment analysis is essential for understanding and processing textual data to derive meaningful insights. PT. XYZ, a company operating in the GSM cellular telecommunications sector, faces a challenge due to the lack of a specific application for analyzing visitor reviews on their services. This gap impedes their ability to gain detailed insights into consumer feedback, hindering efforts to improve service quality. This research addresses this issue by developing an application that utilizes the C4.5 algorithm to analyze PT. XYZ's reviews. The study uses 140 consumer reviews collected from Google Maps. The C4.5 algorithm, which creates decision trees to find relationships among variables, is employed for classifying and predicting service review sentiments. The research involves several stages: data crawling, text preprocessing, term frequency (TF) calculation, and applying the C4.5 algorithm for classification.The results demonstrate the effectiveness of this approach. With 126 training data samples and 14 test samples, the model achieved an accuracy of 78.57%, precision of 83.33%, and recall of 90.91%. These findings indicate that increasing the amount of training data enhances pattern recognition and accuracy. The study successfully meets its objectives, proving that sentiment analysis using the C4.5 algorithm can effectively predict service review sentiments and aid in improving service quality.
Analisa Metode Association Rule Untuk Penjualan Skincare Menggunakan Algoritma Pincer Serach Nadia Indah Tarakanita; Yusni Amaliah; 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.v6i2.46

Abstract

Rumah Masker Tarakan is a business entity in the field of selling cosmetic and skincare products. A common problem faced by Rumah Masker is the placement of products, which often leads to difficulties in finding similar products. Therefore, a method is needed to organize the layout of skincare products that are frequently purchased together, making it easier for customers to select the products they want. The Pincer Search Algorithm, also known as the Two-Way Search, uses two approaches: Top-Down and Bottom-Up. In its process, the primary direction of the Pincer Search is Bottom-Up. The Maximum Frequent Set is a collection of maximal itemsets that are classified as frequent. The purpose of the Maximum Frequent Set is to reduce (prune) the number of candidate Frequent Itemsets that need to be examined in the Bottom-Up process. Based on research conducted using the Pincer Search Algorithm with sales data from January to June 2022, involving a total of 148 data points, it was found that 72 transactions yielded a value of 0.028% for a candidate of 3 itemsets. This demonstrates that the best sales performance occurs with a combination of 3 itemsets.
Implementasi Clustering K-Means Untuk Pengelompokan Perkembangan Anak Didik Indra Dwi Cahya; Andi Tenri Puji; Fitria
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.v6i2.49

Abstract

TK PAUD KB Kasih Ibu Tulin Onsoi is an early childhood education institution independently established by the Sebuku Village Government. This institution evaluates the child’s progress every semester and provides the progress information through report cards. The teacher assesses the development based on the report card value. There is no student clustering to decide which children will be monitored further by emphasizing learning from students who have not yet developed. One method to implement is K-Means clustering. This study used report card data for one semester. Then, analyze the data using K-Means with 4 clusters, namely Not Developed (BB), Starting to Develop (MB), Developing As Expected (BSH) and Very Well Developed (BSB). The implementation of child clustering has succeeded in becoming several clusters with almost the same similarity values. Clustering 38 children's data shows that 13 children are developing very well (BSB), 4 are developing according to expectations (BSH), 8 starting to develop (MB), and 13 are not yet developing (BB).

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