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All Journal Journal of Big Data Analytic and Artificial Intelligence
Hartono, Lies
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Computer Science & IT

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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.
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.
Implementasi Metode Market Basket Analysis Untuk Menentukan Menu Paket Penjualan Di Bius Café Susilayani, Asrini; Putri, Eviana Tjatur; Hartono, Lies; Ardi, Mohamad
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.v7i2.63

Abstract

A café in Tarakan City, Bius Café, is looking for sales strategies due to increasing business competition. One promotional step that a restaurant industry can take is creating package menus that are more affordable than buying items individually, making consumers happier with such menus. Bius Café, a café with a considerable number of patrons, intends to create package menus. These packages will be a combination of food and drink items from the café, taking into account how often these combinations are chosen by customers. The Apriori Algorithm is a data mining algorithm for extracting association rules. It is a type of association rule in data mining, often referred to as affinity analysis or market basket analysis. Based on the analysis results, using the Apriori algorithm to determine sales patterns shows that to form rules, the process starts with the formation of combinations from transaction analysis of 1-itemset, 2-itemset, and 3-itemset combinations that meet the support and confidence values in forming these combinations. Several transactions are selected according to the minimum support and confidence requirements, forming rules that are then ranked with the highest confidence value of 72%, which includes "Mie Goreng" and "Bius Café". Thus, the business owner can create promotional package menus by combining these two items.
Co-Authors Ardi, Mohamad Eviana Tjatur Putri Miske Marcillia Muhammad Fadlan Rahmadania, Nova Tari Susilayani, Asrini Syafiqoh, Ummi Widyasari