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Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

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.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

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.

This study aims to analyze the correlation between learning models and student achievement at SMA Negeri 1 Kuala by applying the Apriori algorithm in data mining, using Rapid Miner software as the primary tool for analysis. The research is motivated by the shift in educational approaches from conventional teacher-centered methods toward more innovative strategies such as project-based learning and cooperative learning, which are expected to foster higher levels of student engagement and improve academic outcomes. In many schools, particularly at the secondary level, the choice of learning model, availability of facilities, and attendance rates are crucial factors that shape learning effectiveness and student performance. The data collected in this study include student grades, the types of learning models implemented, school facility conditions, and attendance rates for the 2023/2024 academic year, covering a total of 680 students. The Apriori algorithm was employed to discover hidden patterns and associations among these variables, enabling the identification of relationships between learning factors and academic achievement. By applying Rapid Miner software, the research systematically generated association rules that reflect meaningful correlations in the dataset. The results indicated that the use of the Indonesian language subject in combination with a cooperative learning model, adequate and complete school facilities, and good student attendance was strongly associated with the attainment of an A grade. This finding was supported by a support level of 53.33% and a confidence level of 100%, suggesting a robust and reliable relationship between these factors. The implementation of data mining techniques through Rapid Miner not only allowed for efficient data processing but also provided practical recommendations for educators and school administrators in designing effective instructional strategies.

This study proposes a hybrid approach combining Frequent Itemset Mining (FIM) and Algorithms and Genetic Algorithm for product layout optimization with a case study at PT. MPI Pharmacy. The FIM Algorithms is employed to extract association rules from 1,000 beauty product sales transactions, while the Genetic Algorithm is utilized to perform product placement based on these rules generated, with storage space constraints. Implementation results demonstrate that this hybrid approach successfully identifies 18 key association rules (support >15, confidence >80%) and proposes an optimal layout configuration model that reduces customer travel distance by 25 compared to conventional layouts used by MPI Pharmacy. The Genetic Algorithm solves complex rule-based optimization problems for product placement, which are limited by traditional market basket analysis (MBA) approaches that rely solely on association rules. This hybrid sistem not only improves pharmacy operational efficiency at PT MPI (reducing service time by 18) but also increases cross-selling opportunities by 22. Hence, inventory operations management impproved efficiently. The research findings contribute to the field of retail space optimization by effectively integrating association rule mining and evolutionary computation.  

This study aims to design and develop a web-based stock and sales transaction management system that can help admins manage accounts, stock, transactions, and sales analysis using the Apriori algorithm. This system is designed with main features such as automatic transaction recording, real-time stock monitoring, and customer purchasing pattern analysis reports. The methods used in this study include needs analysis, system design, implementation, and testing using the black box testing method. The test results show that the system runs according to the design and can increase efficiency in managing sales data. However, there are several limitations such as the need for periodic database maintenance and limitations in raw material management. With this system, it is expected that the process of recording transactions and sales analysis can be carried out faster and more accurately, thus helping in making business decisions.

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.

The Indonesian government has implemented various programs to improve public welfare; however, social assistance often misses its target, primarily due to a lack of accurate data. Sirapit Subdistrict, as a government institution, has access to important population data for policy development, particularly in the distribution of aid based on community welfare levels. Factors such as education, age, number of dependents, and income play a significant role in determining an individual's welfare. To address this issue, this study proposes the use of the Apriori method to analyze the factors affecting population welfare. The Apriori method is a data mining algorithm useful for discovering association patterns within a dataset. The study results show that with a support value of 3% and a confidence level of 100%, a pattern was found where residents with a high school education, 1-2 dependents, aged 35-45 years, earning Rp 500,000 - Rp 999,999, and with a low welfare level tend to work as laborers. These findings are expected to serve as a foundation for formulating more targeted policies to improve community welfare in Sirapit Subdistrict.

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.

The Apriori method is one of the algorithms used in data mining to find association patterns, such as "association rules", in large data sets. This method was developed by Rakesh Agrawal and Ramakrishnan Srikant in 1994. The purpose is to test the correlation between facial skin problems and the type of product used by finding min support and min confidence using the apriori method.The results obtained based on this analysis are that there are 2 rules that meet the minimum requirements to form a combination of 2 itemsets with a minimum support value of 95% and a minimum confidence of 100%.  

The rapid development of technology increases the need for effective and efficient information. Information that is not managed properly loses value, especially when large amounts of data are available, making conventional methods no longer adequate to analyze the potential of the data. Therefore, a system capable of analyzing, summarizing, and extracting data into useful information is required. The Department of Agriculture and Food Security, as an agency that handles food security, agriculture, animal husbandry, animal health, and fisheries, is responsible for supporting the increase in agricultural yields to meet the food needs of the population and encourage economic growth. To achieve this goal, the agency needs to utilize technology to process agricultural data quickly and accurately. The system built using the apriori method can analyze data efficiently and provide recommendations for increasing agricultural yields. Based on the test results, a support value of 9% and a confidence of 68% were obtained, with the rule If the crop is Cassava, then the production yield is 6000-8000 tons.

Learning achievement is every learning activity carried out by students which will result in a change in themselves. The learning outcomes obtained by students are measured based on differences in behavior before and after learning is carried out. The economic conditions of students' families at SMP Negeri 2 Binjai have a significant influence on student learning achievement. Many students who come from families with economically disadvantaged backgrounds face various challenges that hinder the learning process. Financial limitations often mean they do not have adequate access to educational resources, such as books, the internet, and additional tutoring which can help improve understanding of subject matter. This research uses the Apriori method as a problem solving method, namely to correlate between Family Socio-Economics, Activities Students Outside the School Environment and Level of Student Learning Motivation with Student Achievement in class. If data A, G, K → O with Support 30% and Confident 100% and S*C value 30%. So, if a student from a family with an income of less than Rp. 1,000,000 who take part in extracurricular activities outside of school, and have family-driven motivation, will have academic achievement with good report cards. This research indicates that family socio-economic conditions have a significant impact on student academic achievement. Through data analysis, it can be seen that factors such as family income, student activities outside the school environment and the level of student motivation to learn can influence the extent to which students can achieve higher academic achievement.

This study addresses the issue of Indonesian migrant workers (TKI) whose characteristics do not match the jobs assigned abroad, often leading to complaints from agencies and companies. This mismatch is caused by incorrect job placements and insufficient training, which prompts TKI to leave their assigned jobs. The research aims to better understand the characteristics of TKI that influence successful job placement. The **apriori** method was used to identify patterns and relationships between TKI characteristics, destination countries, and suitable job types. Based on a 30% minimum support, 3 and 4 itemset combinations were produced, showing correlations between TKI characteristics and job positions. Using lowerboundminsupport 0.001 and minmetric 0.1, this study generated 6 itemsets from 13 data points, providing significant correlations between TKI characteristics and more accurate job placements.

A library is a facility or place that provides reading materials. Good book arrangement can help the library in obtaining good reading sources. The arrangement of library service book collections based on borrowing patterns, there is an alignment between user needs and the availability of reading materials available in the library. Analysis of book borrowing patterns provides valuable insights for library staff in determining the books that are most in demand and often needed by users. Data mining is defined as mining data or efforts to dig up valuable and useful information in a very large database. The most important thing in data mining techniques is the rule for finding high frequency patterns between sets of itemsets called Association Rules. The method used in this study is Apriori (Association Rule). This technique is used to find relationships or associations between items or variables in data. Well-known algorithms such as Apriori and Eclat are used to find association rules in transactional data. The purpose of this study is to find out library visitor data using the Apriori Algorithm method and to find out the application of data mining for compiling book collections based on borrowing patterns. The results of this study are the multiplication of support and confidence, choose the one with the largest multiplication result. The largest result of the multiplication of these multiplications is the rule used when borrowing books. Because the results of the multiplication of the 4 borrowings have the same value, all of them can be used as rules.