Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers

Research on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction.

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Ritonga,

Juledi,

Mutia

2024

“…Data mining is the process of analyzing large sets of data to discover patterns, trends, and previously unknown information, which can help in decision making (Aji & Devi, 2023) (Bustomi, Nugraha, Juliane, & Rahayu, 2023). This technique is widely used in a variety of industries, including finance, healthcare, retail, and telecommunications, to gain insights that can increase efficiency, identify new market opportunities, or improve marketing strategies (Saputra, Hindarto, & Haryono, 2023) (Abas et al, 2023). Data mining combines methods from statistics, machine learning, and databases to extract useful information from large and complex data sets (Sinaga, Marpaung, Tarigan, & Tania, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method

Ariani,

Masrizal,

Muti’ah

2024

This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.

“…This technique is often used in various industries such as finance, marketing, healthcare, and retail to make more informed and efficient decisions. Data mining combines statistics, predictive analysis, and artificial intelligence technology to process and analyze data from various sources (Bustomi, Nugraha, Juliane, & Rahayu, 2023) (Saputra, Hindarto, & Haryono, 2023). This process involves several main steps, including data collection, data cleaning, data transformation, data mining, evaluation, and application of results (Abas et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

Melyani,

Harahap,

Irmayanti

2024

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.