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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 8 No 1 (2025): JBIDAI Juni 2025" : 5 Documents clear
Analisis Sentimen Kritik dan Saran Layanan RSUD Akhmad Berahim Tana Tidung menggunakan Metode Lexicon-Based Maulidia, Nurmala; Praseptian M, Dikky
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.65

Abstract

Rapidly developing information technology must become a significant component in its use in all human life to simplify work. This matter underlined the research with that title, which the author believes will be an element of service assessment, whether positive, negative, or neutral. Sentiment analysis is required while evaluating a service, particularly in hospitals. The Lexicon-Based method uses a dictionary or lexicon as a language basis. This method classifies a sentiment from each opinion so that a sentiment sentence can be classified as positive, neutral, or negative. The text data will then be calculated using a Lexicon-Based to produce service quality sentiment analysis. The research used 100 data, with a questionnaire distributed of as many as 90 data and a suggestion box of as many as 10 data for sentiment analysis. The research received data of 33 criticisms and 67 suggestions. The Lexicon-Based method also classifies data into positive, negative, and neutral. The designed system can assist hospitals in evaluating services.
Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Menentukan Tata Letak Obat Pratiwi, Widya; Fitriana, Ayu; Sinawati, Sinawati; Zulhilmi, Hafizhan
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.62

Abstract

The role of information systems has expanded to other layers of life, especially business sectors operating in the health sector. Improving service and performance productivity in the health sector, one of which is pharmacies. This study aims to identify drug dispensing patterns at 24-hour pharmacies in Tarakan. A 24 hour ready pharmacy is a business place that operates in the field of selling medicines, the sales are still manual and the data is stored as an archive, so there is no application that can help the pharmacy owner in knowing the combination of medicines purchased and in determining the layout of medicines that are still not in accordance with the requirements. alphabetical order, based on tablet drug category. This research aims to determine the relationship between drugs purchased by consumers and how to implement data mining to determine the combination of drug sales at 24 Hour Ready Pharmacies. This research was carried out in several stages. The first stage creates tabular transaction data. The second stage is to determine minimum support and minimum confidence. The third stage is to calculate itemset combinations. The fourth stage is calculating confidence and the fifth stage is making a rule or decision rule..
Optimalisasi Pengelompokkan Konsumen dengan Multi Internal Metric Validation dan Boxplot Analysis Fitriyanto, Rachmad; Nurindah, Nurindah
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.67

Abstract

The simultaneous use of multiple internal validation metrics to determine the optimal number of clusters in K-Means Clustering often results in differing K values, which can confuse data practitioners when extracting insights, such as identifying customer characteristics. This study aims to develop an evaluation framework to address the ambiguity arising from varying K values produced by different internal validation metrics. The proposed K evaluation framework consists of two stages. In the first stage, five internal validation metrics—Davies-Bouldin Index (DBI), Silhouette Score, Elbow Method, Dunn Index, and Calinski-Harabasz Index—are used as filters to generate up to five top K candidates. The second stage involves boxplot analysis, interquartile range (IQR), and elbow visualization to explore the cohesiveness and stability of the resulting clusters. The first-stage evaluation yielded four potential cluster counts: K = 2, 5, 7, and 10. In the second stage, based on the elbow graph of the average interquartile range, K = 5 was identified as the most optimal number of clusters compared to the other candidates. These results indicate that using a larger number of internal validation metrics may increase the likelihood of producing multiple K values. However, a higher number of clusters does not necessarily guarantee better quality. The implications of this research highlight the importance of a layered evaluation approach in determining the optimal number of clusters, especially when employing multiple internal validation metrics. The proposed framework can assist data practitioners in making more informed decisions and reducing ambiguity in the clustering process. In the future, this framework can be extended by incorporating external validation metrics or adapted to other clustering algorithms.
Penerapan Algoritma Hash Based untuk Analisa Pola Penjualan Obat pada Apotek Permata Tarakan Sahira Reggina Putri; Fitria, Fitria; Risma Sakila
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.68

Abstract

In an era of increasingly intense business competition, entrepreneurs in the healthcare sector, such as pharmacies, are required to optimize the use of transaction data. Apotek Permata Sejahtera has experienced a daily increase in sales transaction volume, resulting in an accumulation of data without further analysis. Therefore, a method is needed to identify associations between pharmaceutical products that are frequently purchased together by customers to support sales strategies and product arrangement. This study applies the Hash-Based algorithm to discover association patterns from drug sales transaction data between February and September 2024. The research stages include tabular data construction, determination of minimum support, hash address computation, calculation of minimum confidence, confidence evaluation, and formulation of association rules. The results show that the maximum itemset combination meeting the minimum support threshold of 40% reaches only up to the 5-itemset level, with a single final combination. From the 41 combinations that met the support criteria, 37 rules were identified with a minimum confidence of 80%, indicating strong relationships among pharmaceutical products. These findings offer practical contributions to sales strategy planning, inventory management, and product layout optimization in pharmacies to enhance operational efficiency and customer satisfaction
Pengaturan Tata Letak Produk Fashion dengan FP-Growth untuk Peningkatan Penjualan UMKM Widyasari; Syafiqoh, Ummi; Rahmadania, Nova Tari; Hartono, Lies
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.69

Abstract

The application of data mining techniques in the business sector contributes significantly to strategic decision-making. This study implements the FP-Growth algorithm to analyze consumer purchasing patterns at Zaynthary Store, a fashion retail shop located in Tarakan City. A total of 161 sales transaction records were collected and processed to identify frequent itemsets and association rules that represent relationships between products. The findings reveal that certain item combinations are frequently purchased together, such as {Blouse → Jeans} with a confidence value of 55%, suggesting that these items should be placed near each other in the store display layout. FP-Growth has proven effective in exploring customer purchase patterns and providing layout recommendations that can support increased sales. These results can serve as a strategic reference for designing data-driven store layouts in the fashion retail industry.

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