Publication Search

Ketidakseimbangan antara tingkat persediaan dan permintaan pasar dapat mengakibatkan kelebihan stok, meningkatnya biaya penyimpanan, dan meningkatnya risiko kerusakan produk. PT. Tridaya Sakti Medima, sebuah perusahaan distribusi farmasi yang berlokasi di Palembang, menghadapi tantangan serupa dalam mengelola persediaannya. Penelitian ini bertujuan untuk menganalisis pola pembelian dengan menerapkan teknik penambangan data menggunakan algoritma Apriori dan untuk merancang sistem rekomendasi produk yang mendukung pengambilan keputusan dalam pengendalian persediaan. Studi ini mengadopsi metodologi CRISP-DM (Cross Industry Standard Process for Data Mining), yang terdiri dari tahap pemahaman bisnis, persiapan data, pemodelan, evaluasi, dan penerapan. Data transaksional yang dianalisis terdiri dari catatan penjualan historis dari Januari hingga Desember 2024, dengan total 5.410 entri setelah proses pembersihan data. Analisis dilakukan menggunakan Python, sedangkan Streamlit digunakan untuk mengembangkan dasbor interaktif untuk visualisasi. Temuan menunjukkan bahwa algoritma Apriori berhasil mengidentifikasi aturan asosiasi antarproduk berdasarkan metrik dukungan dan kepercayaan. Nilai kepercayaan dan peningkatan tertinggi ditemukan pada kombinasi produk (ITR) PECTORIN SYRUP 120 ML @ 24 dan (ITR) ITRABAT SYR 100 ML @ 24 dengan rasio lift 16,43. Hasil ini disajikan melalui dasbor yang dirancang untuk membantu PT. Tridaya Sakti Medima dalam meningkatkan manajemen persediaan dan mengatasi masalah ketidakseimbangan stok.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

This study is motivated by the problem faced by Toko Polirindo, where sales transaction data are stored only as archives and have not been utilized for analytical purposes, resulting in unstable product availability, recurring stock shortages, and difficulties in predicting customer purchasing behavior; therefore, this research aims to identify patterns of item associations that frequently occur together by applying the Association Rule Mining method using the FP-Growth algorithm, which is recognized for its ability to extract frequent itemsets efficiently without the need to generate candidate combinations as in the Apriori algorithm. The dataset consists of sales transactions recorded from January to September 2025. It undergoes several stages, including preprocessing, binary transformation, and analysis using RapidMiner to generate frequent itemsets and association rules, evaluated using support, confidence, and lift metrics. The results reveal that item 3 consistently appears as the most dominant consequent across almost all generated rules, with confidence values ranging from 0.322 to 0.347, indicating that this item is most strongly associated with other items and frequently appears as a complementary product in customer transactions. These findings provide practical contributions by offering insights to optimize stock management, improve product placement, and develop promotional strategies based on actual purchasing patterns, while also demonstrating that the FP-Growth algorithm is an effective analytical tool to support data-driven decision-making aimed at enhancing operational efficiency and customer satisfaction in retail environments.

Automatic motorcycle spare part sales have increased along with the high use of automatic two-wheeled vehicles in the community. To support optimal sales strategies and stock management, customer purchasing pattern analysis is required. This study uses the FP-Growth algorithm to identify association patterns between automatic motorcycle spare part products that are frequently purchased together. FP-Growth was chosen because of its ability to efficiently find frequent itemsets without the need to generate candidate itemsets as in the Apriori algorithm. Transaction data is processed to form an FP-Tree which is then extracted to find relationships between items. The analysis results show combinations of products that frequently appear together, such as brake pads and engine oil, which can be used as a basis for compiling sales packages, product placement, and product recommendations. By implementing the FP-Growth algorithm, spare part stores or workshops can improve service and efficiency in sales management.

Pengelolaan persediaan pada bisnis penjualan suku cadang motor sering menghadapi tantangan ketidakseimbangan stok akibat kurangnya pemahaman terhadap pola pembelian konsumen. CV FOLC MORA, sebagai salah satu pengecer sparepart di Palembang, mengalami ketidakefisienan dalam pengendalian stok yang dapat mengakibatkan biaya penyimpanan berlebih atau hilangnya peluang penjualan ketika barang populer tidak tersedia. Kebaruan dari penelitian ini terletak pada konteks penerapan algoritma Apriori yang difokuskan pada skala usaha mikro, kecil, dan menengah (UMKM) penjualan suku cadang motor, yang relatif jarang dieksplorasi pada penelitian terdahulu yang umumnya menitikberatkan pada e-commerce berskala besar. Selain itu, penelitian ini tidak hanya menghasilkan analisis pola pembelian, tetapi juga mengintegrasikan hasil tersebut ke dalam dashboard interaktif berbasis Streamlit yang memudahkan pelaku UMKM dalam melakukan pemantauan stok dan perencanaan pembelian. Kombinasi konteks UMKM dan pemanfaatan dashboard analitik inilah yang menjadi nilai kebaruan sekaligus kontribusi praktis penelitian ini. Penelitian ini bertujuan untuk menganalisis pola pembelian menggunakan algoritma Apriori guna mendukung pengambilan keputusan dalam optimalisasi stok dan peningkatan penjualan. Metode yang digunakan adalah CRISP-DM (Cross-Industry Standard Process for Data Mining) yang terdiri dari enam tahapan: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan implementasi. Data transaksi penjualan yang dianalisis berasal dari periode Januari hingga April 2025, dengan total 1.550 transaksi dan 6.478 data item. Pengolahan data dilakukan menggunakan Python dengan bantuan pustaka Pandas dan MLxtend, sedangkan hasil analisis disajikan melalui dashboard interaktif berbasis Streamlit. Algoritma Apriori menghasilkan keterkaitan kuat antar produk, seperti “Ban Luar Swallow 80/90-17” dan “Velg TK Excel Rim” dengan nilai support 1%, confidence 76%, dan lift 2,15. Temuan ini menjadi dasar penyusunan strategi bundling produk dan keputusan restok barang. Dashboard yang dikembangkan memudahkan pemilik usaha untuk mengunggah data transaksi, mengatur parameter analisis, serta melihat frequent itemset dan rekomendasi kombinasi produk. Pendekatan ini meningkatkan efisiensi pengelolaan stok, mendukung strategi pemasaran berbasis data, serta berkontribusi pada peningkatan kepuasan pelanggan dan kinerja penjualan.

Currently, the use of data mining technology has become essential in enhancing business management efficiency, including in the trending coffee shop industry. Data mining allows business owners to analyze sales information in depth, enabling more accurate decision-making regarding inventory management, promotions, and sales strategies. This study aims to implement the Apriori algorithm to analyze sales data at Menrabic Coffee Shop. The Apriori algorithm is used to discover association patterns or relationships between products frequently purchased together by customers, which can assist management in providing inventory that aligns with customer preferences. The research method illustrates the detailed implementation process of the Apriori algorithm, starting from sales data collection, data cleaning, programming, and analysis of the results. The implementation uses web programming languages such as HTML, CSS, MySQL, and JavaScript, while back-end logic is programmed with PHP. The results of applying this algorithm reveal the most popular sales patterns among customers, providing valuable insights for management to improve operational performance and customer satisfaction. Therefore, this study demonstrates that applying data mining with the Apriori algorithm can be an effective tool for understanding consumer behavior and supporting data-driven decision-making at Menrabic Coffee Shop. By utilizing these insights, management can optimize inventory, enhance sales strategies, and ultimately increase overall business efficiency.

This research aims to develop a recommendation system that can help retail business owners design more effective, data-driven promotional strategies. This system utilizes data mining techniques and the Apriori algorithm to extract association rules from consumer transaction data, thereby identifying more specific and accurate consumer purchasing patterns. Based on these patterns, the system can provide relevant promotional recommendations, such as product bundling, buy-one-get-one offers, or special discounts, which can attract consumer interest and increase sales. The system's implementation process is presented in the form of an interactive dashboard, which allows business owners to upload their transaction data, adjust analysis parameters, and visualize the promotional recommendation results in a way that is easier to understand and can be directly applied to their marketing strategies. This system not only provides well-structured promotional recommendations but also enables retail business owners to make more informed and efficient decisions in determining the type of promotion to implement, based on insights gained from analyzing their own transaction data. By utilizing this system, business owners can optimize their promotional strategies more efficiently and effectively, because they can quickly identify promotions that best suit consumer purchasing patterns. This can increase impulse sales, as relevant promotions will encourage consumers to purchase more products. Furthermore, this system shows great potential in increasing consumer engagement, as the promotions provided are more personalized and tailored to each consumer's preferences. Therefore, the implementation of this recommendation system has the potential to drive significant sales growth and help retail business owners achieve greater profits, as well as accelerate their business decision-making process. This system, ultimately, not only benefits business owners but also enhances the consumer shopping experience with promotions that are more tailored to their needs and preferences.

The increasingly rapid development of the culinary sector makes commercial competition in this field increasingly fierce. Food stalls serve a variety of menus and drinks, but stall owners need to strive to create product innovations in order to provide satisfactory service to customers. Under these conditions, a computer technician is needed to find out recommendations for food stall menus. The analysis method used is a data mining technique with the Apriori algorithm, where this algorithm is used to identify the data sets that appear most frequently (frequent itemset). The research results show that the highest support and confidence values ​​are Ayam Penyet and Fried Rice with 50% support and confidence values 76%. This can be a combination of menus suggested from data that has been collected and applied according to an a priori algorithm which is expected to be used to evaluate service and possibly increase guest satisfaction so that the Food Stall can develop more quickly.

PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

Children under five are the gold brain generation where brain growth and development is currently developing. The toddler years are a time when children experience rapid growth, a very important period for basic growth to occur which will influence and determine the child's further development. In terms of food consumption, at this age children are still passive consumers, who are not yet able to pick and choose their own food according to their needs, so at this age children are very vulnerable to various health problems if they are malnourished.

Jasindo Insurance Company is one of the insurance companies that receives insurance coverage both directly and indirectly, with ownership of 1 share of dwiwarna series A owned by the Republic of Indonesia and 424,999 shares of Series B owned by PT Bahana Pembinaan Usaha Indonesia (Persero). PT Asuransi Jasa Indonesia has several products and choices in choosing which insurance is needed by customers in agriculture, health, education and many more. Due to the large amount of competition in the business world, it requires management to find the right strategy in increasing the use of Jasindo insurance by knowing the relationship between age, gender, marital status, occupation and the type of Jasindo insurance that is widely chosen by customers. In order to find out the use of insurance that is widely used by the community, it is necessary to analyze the data on the use of insurance using the apriori algorithm method to determine the combination between item-sets of transaction data on Jasindo insurance data. Based on the research conducted after experimenting with the above case with a minimum support = 25%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained, namely if the gender is male, the marital status is Unmarried, then the type of insurance is jasimdo health and jasindo rainbow then giving value is successful with 25% support, 100% confidence.  

Police Sector (Polsek) is one of the agencies that provide protection, order and ensure public safety in the sunggal area. The number of cases of criminal acts that occur makes residents feel unsafe and always feel threatened in certain areas in the Sunggal sub-district, the pattern of criminal acts that often occur due to several factors, one of which is due to the lack of security in the area so that many criminal acts occur as well as behaviour that has been planned by the perpetrator to achieve their goals by planning, preparing, implementing, disposing of evidence, even hiding or escaping depending on the type of crime committed based on the characteristics of the perpetrator, and the situation or context in which the crime occurred. Therefore, it is necessary to analyse techniques from existing criminal data using the a priori algorithm method to find patterns of relationships between variables that can assist agencies in taking action for public safety. Based on the research conducted, the above case is tested with a minimum support = 10%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained: ‘If the criminal act is theft then the job is self-employed’, then giving value is successful with 15% support, 100% confidence. And ‘If the age of 17-25 years, the criminal act is Theft then the job is unemployed’, then giving value is successful with 10% support, 100% confidence.

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

All PT Astra International BMW Semarang transactions are recorded in the database but the problem is that the stock management is  efficientless so  the part stock that buyers are interested is not available. This research aims to conduct a comparative mining results using the association rule with apriori algorithm for year 2021, 2022 and 2023 sales transaction dataset with total of 43.694 records using the Rstudio. Data mining process in each year uses the same parameters for each itemset combination. The best association pattern occurs in 2023 with support value 0.05913841 and confidence value 100%. This can be concluded that the rules formed from each year could be different eventhough using same parameters. The item that always appears in the association rule from 2021 – 2023is Z99000000333 (BMW Engine OIL) which is often purchased with items named “Set fil-oil” so it can be a recommendation for  item stocking  in the warehouse.

PT Astra International BMW Semarang operates in the automotive sector, focusing on sales, aftersales, and spare parts for BMW cars. The availability of spare parts is crucial for customer satisfaction, as stock shortages can lead to disappointment. Using data from 52,162 spare parts sales transactions from January 2019 to June 2023, the study applies data mining techniques with the a priori and eclat algorithms to identify consumer purchasing patterns and prevent stock shortages. The research aims to provide recommendations for prioritizing spare parts stock. Utilizing the CRISP-DM methodology and R programming, the study found that the highest confidence in purchasing patterns occurs with a combination of three itemsets: if a customer buys an oil filter set (B11.42.8.593.186) and washer cleaner (B83.12.5.A1A.683), they will also buy BMW engine oil (Z99000000333) with 100% confidence. These findings can help PT Astra International BMW Semarang manage spare parts stock more effectively.

Data mining is a technique for extracting new information from data warehouses, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as is the case with the Dimsum business at Dimsum Madani.toko. This is located on Jalan Lampu gg. Pelita 4, Brayan Bengkel, East Medan, the location is close to many Brayan Resident's Houses. This of course affects sales levels. Increasing daily sales activity results in an accumulation of sales transaction data that continues to increase, thereby burdening data storage. Unfortunately, this data is only stored without further processing. In fact, this data collection holds valuable information.This research uses Market Basket Analysis with the Apriori Algorithm to find association patterns based on consumer shopping behavior. The goal is to identify items that are often purchased together. The research results showed that the combination of Seaweed Dimsum with Tofu Skin Spring Rolls had the highest support value (50%) and the highest confidence (75%).

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

Keberadaan bank sebagai salah satu salah satu institusi keuangan terkemuka dalam perekonomian memberikan peluang yang sangat besar untuk memanfaatkan data nasabah dalam mengambil keputusan yang lebih baik dan efektif. Data deposito adalah salah satu instrumen investasi yang populer di kalangan nasabah bank, yang menawarkan keuntungan bunga yang lebih tinggi dibandingkan dengan tabungan biasa. Dengan menggunakan pemrograman Python, penelitian ini menghasilkan nilai confidence minimum sebesar 90% dan nilai support minimum yang dihasilkan adalah 90%. Maka, antar atribut satu dengan yang lainnya memiliki keterikatan yang kuat dan dapat menjadi acuan untuk pengambilan keputusan pada nasabah untuk penggunaan deposito.

Data mining is a technique to dig up new information from a data warehouse, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in trading businesses such as the Iblite Luxury store.  This store is located in Medan close to residents' houses, Sales transaction data will continue to grow, causing data storage to be even larger. Sales transaction data is only used as an archive without being properly utilized. Basically, a dataset has very useful information. Market basket analysis with a priori algorithm is one of the data mining methods that aims to find association patterns based on consumer shopping patterns, so that it can be known what items of goods are purchased in a At the same time, the results of this study found that the highest support and confidence values were Ysl and Chanel with a support value of 50% and confidence of 75%.

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.