Publication Search

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.

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.

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.

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.

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.  

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.

Student behavior is the actions of students which are influenced by their attitudes and responsibilities at school. Student behavior is a very important factor in determining student achievement in learning. Students who have personalities that improve skills, knowledge, attitudes, habits, understanding, skills, thinking power and other abilities will more easily increase their concentration in learning, and it will be easier to achieve the students' goals. At SDN 053960 MARYKE there are still students who do not know that student behavior greatly influences their level of achievement. Therefore, it is necessary to educate students from an early age so that students can be more responsible for the rules given by teachers at school, and students must understand that the attitude they carry out at school is assessed in improving their achievement, as well as their presence is very influential. his level of achievement at school. Therefore, there is a need for a solution to overcome the problems that exist at SDN 053960 MARYKE by utilizing data mining to collect data and then it will be processed using the a priori method with variables contained in the correlation between student behavior and student achievement levels. The a priori algorithm is able to determine min support and confidence in these variables will later show the relationship between student behavior and student achievement levels, so that researchers will get the best best rules and be able to produce the latest information..

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.

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.

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.