Minimum Confidence Research Articles

Implementation of Stock Goods Data Mining Using the Apriori Algorithm

Stock inventory in the pharmacy should be well recorded. This is to provide the best service to customers/buyers. Buyers who come with empty results or the drug they want to buy will not feel disappointed, especially if they have made several purchases. This research implements data mining of drug stock inventory, where in the research carried out there are many empty and excess inventory items, resulting in less than optimal service. The research method, namely the survey research method, is a research method that is carried out using surveys or data collection through research respondents. The algorithm used in analyzing the data is the Apriori algorithm. The results of this study are the results of association rules based on predetermined parameters, namely a minimum support of 25% and a minimum confidence of 60%. The rule that is formed is that if consumers buy amoxicillin, they will buy mefenamic acid with a support value of 37% and a confidence of 183%.

Data mining technique for grouping products using clustering based on association

<span>There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.</span>

Meta-Analysis: The effectiveness of artificial diets to increase on survival rate of vannamei shrimp (Litopenaeus vannamei)

The use of artificial diets for shrimp culture has been widely used at this time, but it was not yet known how large the effect of artificial diets on the survival rate of Litopenaeus vannamei. This study aims to determine the effectiveness of artificial diets on on the survival rate of Litopenaeus vannamei by meta-analysis research. The measurement results obtained z-value of 5.336 and p-value of 0.001, this p-value was smaller than the significance value of 5% (0.05). Therefore, at a significant level of 95%Cl the correlation between artificial diets and the survival rate of vannamei shrimp was significant with a minimum confidence limit of 0.192 and a maximum of 0.343. Based on Egger’s test, the z-value which was the magnitude of the regression coefficient is 1.714, while the p-value of 0.086 is greater than 0.05 which indicates that the selection of articles and the results of the study were not indicated by publication bias. In addition, the p-value > 0.05 indicates that the funnel plot was symmetrical. Therefore, the results of this study are very useful theoretically in the development of artificial diets for the survival of vannamei shrimp.

FOOD MENU ARRANGEMENT RECOMMENDATIONS USING TRANSACTION HISTORY DATA USING THE APRIORI METHOD (CASE STUDY: BEBEK PONDOK GALIH)

REKOMENDASI PENATAAN MENU MAKANAN MENGGUNAKAN DATA HISTORI TRANSAKSI DENGAN METODE APRIORI (STUDI KASUS: BEBEK PONDOK GALIH)The food business goes hand in hand with the development of information technology. Process efficiency must be improved to increase sales transactions and achieve consistent profits. Bebek Pondok Galih has historical data on sales transactions from year to year, with this data being useful if management can analyze consumer buying patterns so they get information on what menus are frequently ordered and the relationship between one menu and another. Based on these problems, a data analysis process is needed to form a menu arrangement recommendation by utilizing data mining using the Apriori algorithm. The a priori algorithm is a data mining method that can produce association rules. Based on its application in this study with a minimum support value of 30% and a minimum confidence level of 80%, the final rule is that if consumers purchase 600 ml of roasted duck and aqua, they will also purchase a portion of rice. so that the three menus can be made into one menu package.

IMPLEMENTASI ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELI CUSTOMER

CV. Syahfira Bakery and Cake is a business engaged in the production of cakes, pastries and bread. The more a business develops, the transactions that occur daily also increase. However, sometimes the sales transaction data is not fully utilized properly by administrative staff and company owners. In fact, if it is processed and used by administrative staff and supervised by business owners, then sales data will have a major influence on product sales in the future. Data processing can be done using data mining techniques, these data sets can produce new information, one of which is customer purchasing patterns. Syahfira Bakery and Cake is a cake, sponge and bread shop that has not fully utilized data for determining sales business strategies. With these problems, it is necessary to use transaction data to be processed with the Apriori algorithm so that it can provide new knowledge that can be utilized by shop owners. Based on the research that has been done, from the 20 processed transaction data, results are obtained that meet a minimum support of 30% and a minimum confidence of 70% and one rule is obtained, namely if a customer buys chocolate jam products, he will buy plain bread

Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online

In today's online technology, we have to learn various things so that we can adjust to social behavior, for example, buying and selling activities that cover all walks of life, now everyone doesn't hesitate to shop online. The online market is growing fast, especially in Indonesia. Anyone can sell and buy goods easily through online stores, so that people's purchasing power and sales increase due to the proliferation of online stores, XYZ stores are still beginners in terms of sales strategies so they try all efforts without careful preparation, one of the ways to do this is to expand its market coverage even to export goods abroad. the process of exporting goods in the current era is not a difficult thing to do, but it requires knowledge and preparation of a good strategy. from existing sales data can be analyzed to determine the right strategy in marketing goods. Data Mining is a science that helps analyze large amounts of data with the aim of gaining new knowledge that can be utilized. In Data Mining Analysis there is the FP-Growth algorithm which is a data mining technique for finding association rules from lots of data. This is known as the Association rule, where the rule will be determined from the minimum support and confidence results. the rules that are formed produce 10 rules out of 51 existing data, these rules help determine the export sales strategy at the XYZ store.

Penerapan Algoritma FP-Growth untuk Menentukan Strategi Promosi Berdasarkan Waktu dan Pembelian Produk

Sales is the main activity in every business. In making business decisions, sales patterns can be used to provide useful information such as strategies for promotion. Wandri Mart is a business engaged in the sale of products or goods commonly referred to as minimarkets in the city of Payakumbuh. In conducting promotional strategies, the owner of Wandri Mart does not know when to do promotions and what promotions are needed in order to increase sales. The purpose of this study is to obtain purchasing patterns related to the time of purchase and the type of goods purchased, so that a more effective promotional strategy can be developed. The method used by researchers is data mining techniques with the FP-Growth algorithm. The data used was taken as much as 5471 sales transaction data for 1 year. The results of this study indicate that the FP-Growth algorithm can be used to determine association rules using a minimum support of 1%, 2%, 3% and a minimum confidence of 10%. Experiments using Minimum Support 1% and Minimum Confidence 10% have the highest lift ratio value and produce more rules compared to other experiments so that it is obtained if on Tuesdays in August, customers buy instant noodles and packaged drinks with 6% and 5% support respectively and 50% and 45% confidence respectively with a lift ratio of 1.75 and 1.59 respectively. The lift ratio means that the rules have high association accuracy, and this also has a positive impact on sales and can be used as useful information for Wandri Mart to increase sales

Analisis Pola Asosiasi Data Transaksi Penjualan Minuman Menggunakan Algoritma FP-Growth dan Eclat

Every day transaction activities between companies and consumers continue to be carried out. This makes transaction data more and more and accumulate. This transaction data can be processed into more useful information using technology. Data mining is a technology that can work on a collection of transaction data into information that can be taken by companies as decision makers. The association rule method is used as a method to see the relationship between items in a transaction data. To analyze transaction data, researchers used the FP-Growth and Eclat algorithms. There are three stages of association in this study which are distinguished from the confidence value. The results in the first stage have a minimum confidence value of 0.4, the FP-Growth algorithm produces 41 association pattern rules, while the Eclat algorithm produces 32 association pattern rules. Then in the second stage the minimum trust value is 0.5, the FP-Growth algorithm produces 40 association pattern rules, for the Eclat algorithm it produces 32 association pattern rules. In the third stage, the minimum trust value is 0.6, the FP-Growth algorithm generates 32 association pattern rules, while the Eclat algorithm generates 30 association pattern rules. The results of the association pattern rules show that the Eclat algorithm is more efficient in determining the association pattern rules than the Fp-Growth algorithm

Student Identification Based on Patterns of Association For Student Misbehaviour Using Frequent Pattern Growth Algorithms

Student infractions are incidents often committed by students who break the rules at school. This naturally worries school authorities and overwhelms them with student misbehavior. Student rule-breaking is a common problem that can interfere with a safe and orderly learning environment. The more students break the rules, the greater the impact on several aspects, including student achievement, discipline, suboptimal teaching and learning activities, and students' social lives outside of school. Identifying students who are prone to rule violations can help school officials implement more effective prevention programs. Data mining is a process of extracting information from large data sets to discover patterns and relationships hidden within them. This study aims to identify frequent student infractions using the Frequent Pattern Growth algorithm. The Frequent Pattern Growth (FP -growth) algorithm is used to generate frequent itemsets that are then used in the association rules process. The association rules process aims to find rules or relationships between violations based on the discovered Frequent Itemsets. This process is influenced by predefined minimum support and minimum confidence values. A Minimum Support value of 30% and a Minimum Confidence value of 50% are used to obtain rules with a sufficiently high confidence level. It is expected that the identification results from this study will provide a better understanding of the types of violations commonly committed by students in school. This information can be used by school officials to develop more effective prevention strategies and focus on.

Estimation of region of attraction with Gaussian process classification

This paper proposes a methodology for assessing the region of attraction (ROA) of stable equilibrium points, a challenging problem for a general nonlinear system, using binary Gaussian process classification (GPC). Interest in this method stems from the fact that an arbitrary point belonging to the system’s state space can be classified in the region of attraction or not. Importantly the proposed GPC approach for determining ROA gives a minimum confidence level associated with the estimate. Moreover, the active learning scheme helps to update the GPC model and yield better predictions by selecting informative observations from the state space sequentially. The methodology is applied to several examples to illustrate the effectiveness of this approach.

Data Mining Modeling Feasibility Patterns of Graduates Ability With Stakeholder Needs Using Apriori Algorithm

This The speed of information, the accuracy of data, the ease of information services, and accountability are very important reasons for the implementation of the system. Semarang University (USM) is a private university in Semarang that has the most 2 students in Central Java. Based on the 2019 USM tracer data showing horizontal alignment, namely how close the relationship between the field of study and alumni work is, it appears that there is still a discrepancy in the ability of graduates with stakeholders. The Apriori algorithm is the best-known algorithm for finding high-frequency patterns Rules that state associations between attributes are often called affinity analysis or market basket analysis. The use of the Apriori Algorithm in data mining calculations using data from the Semarang University tracer that the limit of the minimum support is 50% and the minimum confidence is 100% so that it forms 4 rules. From the four rules produced that modeling using the Apriori Algorithm can produce several rule formations so that it can provide an evaluation to the University for compiling steps, this can be seen because the resulting rules are different because each graduate relationship with the desired desires and different styles.

Penerapan Algoritma Apriori Untuk Rekomendasi Produk Bagi Pelanggan Toko Online Berbasis Website

Today, almost all activities in various sectors of life have been transformed into digital systems to support various related activities. Consideration of the efficiency of dissemination of information outreach and data management that can be recorded properly is the main reason for switching to a digital system. In the economic sector, global business developments are rife with the use of digital devices in marketing various products to increase marketability. Based on this, this research was conducted to apply the Apriori algorithm association data mining to provide product recommendations for online shop customers. The CodeIgniter framework and the waterfall method are used to build a website-based information system. Sampling data was obtained from transaction data at the Rizvenastore store which was used for the database using MySql. The application of the a priori algorithm can form association rules as a reference in-store product promotions and decision support in providing product recommendations to customers based on predetermined minimum support and confidence values. From testing the system using the black box method, the results obtained for all activities on all actors can run as expected.

Application of the Apriori Algorithm to Purchase Patterns

The purpose of this research is to produce an Apriori Algorithm application system to increase sales turnover at Viona stores. The problem faced by the Viona store is that the Viona store has decreased turnover in the midst of business competition because it has not been able to optimally analyze the products that are often purchased and the combination of purchases by consumers so that sales seem monotonous and do not have a business strategy to attract customers. sales that can attract consumers. One way is to make sales with sales packages at lower prices. It must have a good pattern and analysis to be able to combine products into a sales package. However, with the limited ability of Information Technology, in this study a sales application was built that applies the a priori algorithm. This a priori algorithm is very effective in finding the relationship pattern of one or more itemsets in a large data set so that it is effective in calculating a sales transaction data and finding patterns of combinations of consumer habits and being able to quickly create product sales packages. increase sales turnover. The results of the process of applying the a priori algorithm to sales data at the Viona Store through the RapidMiner application are the same as the results applied to the system built and using sales transaction data for the month of May 2022 using a minimum support of 30% and minimum confidence of 30%. So from this study, information was obtained that the items that were often purchased together during this May period were lighters and cigarettes with 100% Confidence. And for the month of June Viona Stores can recommend packages in their store by looking at the results of a combination of 3 items, which are later expected to increase sales turnover at Viona Stores.

Study of a hull form optimization system based on a Gaussian process regression algorithm and an adaptive sampling strategy, Part I: Single-objective optimization

Simulation-based design (SBD) is recognized as a hull form optimization method with great advantages, but its development process has exposed two obvious shortcomings: difficulty in uncertainty quantification (UQ) of the surrogate model and inefficiencies when solving large-scale optimization problems. To mitigate these deficiencies, this paper first proposes a UQ method for the surrogate model based on a Gaussian process regression (GPR) algorithm. Mathematical optimization results show that when the number of samples is insufficient, coupling this UQ method into the deterministic optimization process can, to some extent, reduce the uncertainty of the optimization results. The drawback of the SBD over-reliance on the surrogate model for global prediction accuracy is addressed by designing a hull form optimization system that performs the sequential sampling specifically for the region of interest. This method is referred to as sequential sampling-based optimization (SBO). It was combined with the basic properties of a GPR surrogate model, and a variety of adaptive sampling strategies suitable for the SBO optimization system were designed. Mathematical optimization results show that a hybrid optimization system, SBO-MSE + LCB, which uses the adaptive sampling strategy with the maximum squared error (MSE) and the minimum lower confidence bound (LCB) of the GPR surrogate model has the best efficiency when solving single-objective optimization problems. This method was also used for optimization design of the total resistance coefficient of a 130,000-ton deep-sea aquaculture vessel. The results show that the optimization efficiency of the SBO-MSE + LCB optimization system was improved by 46.67% with respect to that of a conventional SBD optimization system when the optimization solutions were similar, hence demonstrating the efficiency advantage of this system in practical applications.

Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm

Anthracnose, frogeye leaf spot (FLS), rhizoctonia aerial blight (RAB), soybean mosaic virus (SMV), and yellow mosaic virus (YMV) of soybean are major common soybean leaf diseases that seriously affect soybean yield in India. However, the existing system needs a real-time detection method for soybean leaf diseases, which will help to take appropriate action for disease cure with minimum losses. This study studied a real-time detector for soybean leaf diseases based on deep convolutional neural networks. The 3,127 RGB images of disease-free leaves, anthracnose, FLS, RAB, SMV, and YMV-affected leaves of soybean were collected from the agriculture fields. The Mask R-CNN detection algorithm was used for the detection of soybean leaf diseases by introducing the Res Net 50 module. The pre-processed images (512×512 pixels) were used as input in Mask R-CNN. The model was trained at a number of epochs, training step per epoch training & validation, and learning rate were 80, 500, 50, 8, and 0.001, respectively. The detection accuracy was calculated at three levels of minimum detection confidence i.e. 0.80, 0.85, and 0.90. The results indicate that the maximum detection accuracy i.e. greater than 85% at 0.90 level of minimum detection confidence. This research indicates that the real-time detector based on deep learning provides a feasible solution for diagnosing soybean leaf diseases and provides guidance for the detection of other plant diseases. In addition to that the application of pesticide in the early stage reduce the use of pesticide resulting in less environmental pollution.

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.

Application of Apriori Algorithm to Determine Sales of Traditional Foods

Competition in the food sales industry is growing, making business owners have to be creative to increase sales turnover. Sales activities that occur every day make sales data increase. Researchers intend to make data on Traditional Restaurant as the basis for data mining processing on food sales using the apriori algorithm. The application of the Apriori Algorithm aims to find the most item combinations based on transaction data and then form association patterns from combinations of items. Association patterns are formed with a minimum support value of 10% and a minimum confidence value of 40% which results in 3 association rules and the most sold food products are Krupuk Bangka and Karedok with a support value of 0.105% and a confidence value of 0.583%

APPLICATION OF THE APRIORI ALGORITHM TO DETERMINE THE COMBINATION OF POVERTY INDICATORS

Poverty is a society that has not been solved until now. The decline in poverty in Laweyan District from 2000 to 2013 was 5.71%, among the five lowest in the reduction in the percentage of poverty in Central Java Province. The problem of poverty is very complex, and the differences in regional characteristics, as well as the techniques used, also influence the indicators of the causes of poverty and the formulation of policies for poverty alleviation. This study uses Principal Component Analysis as part of data preprocessing, followed by applying association rules with the Apriori Algorithm to explore the relationship pattern of poverty indicators. Based on the research that has been conducted on the poverty dataset, which consists of 46 attributes, it is found that the attributes that have passed the preprocessing data are six attributes, namely the Poor Population, ADHB in the Communication Sector, ADHB in the Mining and Excavation Sector, ADHB in the Agriculture and Food Crops Sector, ADHB in the Plantation Sector. and unemployment. These six attributes are transformed into Ascending, Fixed, and Descending categorical data. The fuzzification process for the increase and decrease categories uses the shoulder-type triangle membership function. Applying the Apriori Algorithm to the poverty dataset with a minimum support of 0.4 and a minimum confidence of 0.8 produces 38 rules that show the relationship between indicators and poverty and 134 rules that show the relationship pattern between indicators.

Prediksi Pembelian Barang Pada Distributor Lampu Menggunakan Metode Apriori pada PT. XYZ

Apriori is an algorithm that is widely used to determine the pattern of relationships between products that are often bought together in a store. This Apriori algorithm will be suitable to be applied in the field of determining strategy or promotion. PT. XYZ is one of the Lighting Distributor Companies. One of the problems of the company is the imbalance of stock in the warehouse. So, the purpose of this research is to obtain product purchase predictions from suppliers to maintain stock balance with product solds to customers. The object of this research is the application of apriori algorithm. This research data is in the form of sales transaction data at PT. XYZ. Data analysis using RapidMiner Software by determining the valure of support and confidence. The analysis shows that there are 22 Association Rules with a minimum support 20% and a minimum confidence 50%. This information is excpected to help the company in developing a product purchasing strategy from the supplier.

FPGA modeling of a novel fully-synthesizable and secure TRNG based on key-dependent s-box

In this study, the Field Programmable Gate Array (FPGA) implementation of a True Random Number Generator (TRNG) using dynamic key-based s-box architecture as a post-processing technique is presented. In the proposed architecture, the post-processing method of TRNG consists of two different functions, in a total of 32x32 (32-bit input & 32-bit output), containing four independent 8x8 (8-bit input & 8-bit output) s-boxes. Raw random numbers are subjected to three-stage substitution operations in these functions, and their statistical properties are cryptographically improved. The post-processing unit of TRNG is compressionless as the s-boxes are 8 x 8. Thanks to this distinctive feature of the proposed method, TRNG can reach a final output bit rate of 250 Mbps with an entropy of 0.99. With the same output bit rate, TRNG passes the National Institute of Standard and Technology (NIST) Special Publication (SP) 800-22 statistical randomness evaluation tests with success above the minimum confidence interval of 0.9805. In addition to the NIST tests, bias, entropy, correlation, chi-square, and true randomness analyses are also given for TRNG's cryptographic verification. Besides to the statistical requirements, cost and performance analyses for TRNG are also presented in the study. The estimated dynamic power consumption for TRNG needing a low chip area of 2% (3221 LEs) for hardware implementation, is measured as 10.2 mW. The final results show that TRNG has high security and efficiency standards and can be used successfully in embedded cryptographic applications.