Implement Data Mining Research Articles

Implementasi Data Mining Prediksi Penjualan Produk Semen Menggunakan Metode Linear Regression (Studi Kasus PT. Toyo Mortar Indonesia)

The cement industry in Indonesia plays a vital role in infrastructure development and the construction sector. PT. Toyo Mortar Indonesia, established in 2015 in the Tangerang industrial area, focuses on the production of instant mortar cement. However, the national cement industry faces the challenge of declining sales due to the pandemic, which has affected people's purchasing power and slowed down property and infrastructure projects. To achieve future growth potential, the right strategy is needed in sales planning. This study implements data mining using the linear regression method with the RapidMiner application to predict cement product sales. The data used includes historical sales data and product information from PT. Toyo Mortar Indonesia. In the tests conducted, the data was processed using 12 variables, of which 10 variables influenced the prediction results: TM-168, TM-178, TM-188, TM-189, TM-198, UM-100, UM-101, UM BIRU, raw materials, and other sales. The test results show that the Linear Regression method can predict cement product sales with an average Root Mean Square Error (RMSE) value of 493,125.701 with a standard deviation of 126,330.525. The average Absolute Error is 303,270.965 with a standard deviation of 46,207.586, and the average relative error is 1.89% with a standard deviation of 0.36%, indicating very good prediction accuracy. This can help the company in making decisions related to sales planning using the available sales data.

Exploration of Data Mining Techniques in Business Decision-Making

This research examines the use of data mining techniques in business decision-making. By analyzing various data mining methods such as classification, clustering, and association, the study demonstrates how data mining can enhance operational efficiency and marketing strategies. The literature review provides insights into the practical applications and benefits of data mining across different industries. The research highlights the potential of data mining to uncover hidden patterns and trends in large datasets, which can be used to make more informed and timely business decisions. Additionally, the study identifies key challenges in implementing data mining, such as data integration, selecting appropriate algorithms, and interpreting results. The findings are expected to offer practical guidance for companies aiming to leverage data mining in their operations. By understanding the advantages and applications of data mining, businesses can improve their decision-making processes, optimize resource allocation, and develop more effective strategies. This research serves as a valuable resource for organizations looking to harness the power of data mining to gain a competitive edge in the market.

Data Mining Techniques Against Cyber Threats: A Review

Abstract Data mining is a technique used to extract useful data from existing databases. Those datasets are now being shared globally. Secure communication and confidentiality are required because data from multiple sources must be collected and stored in one central location. Data mining technology involves methods that rapidly and efficiently convert vast quantities of data into relevant insights adapted to the user's requirements. Unfortunately, the utilization of data mining expertise to acquire sensitive personal information poses a threat to individuals' privacy rights. This paper provides a review of the current techniques for preventing cyber risks and safeguarding privacy through the application of data mining. Data mining is used to examine, analyze, and figure out the structure and behavior of data mining organizations. Implementing data mining with optimal outcomes is a challenging task. In the past decade, academics have extensively studied many elements of data extraction. Therefore, it is crucial to provide published research evidence pertaining to this field. For this study, a thorough evaluation was carried out of more than thirty research papers sourced from reputable literature databases. The objective was to extract significant information regarding the field of data mining. The collected data was then used to address various study inquiries about cutting-edge extraction methodologies, data mining mechanisms in cyber dangers, data extraction procedures, algorithms, and evaluation techniques. This paper discusses various research areas and issues in data mining, serving as a valuable reference for researchers in this domain.

Fisheries Inspection in Portuguese Waters from 2015 to 2023

As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas. This georeferenced dataset describes the fisheries inspections done in Portuguese waters between 2015 and 2023. Since we are dealing with occurrences that may have originated some legal process to the ship’s owner, we have ensured data anonymization by pre-processing the dataset to maintain its accuracy while guaranteeing no unique identifiers exist. All the pre-processing performed to ensure data consistency and accuracy is described in detail to allow a quick analysis and implementation of new algorithms. The data containing the results of these inspections can be easily analyzed to implement data mining algorithms that can efficiently retrieve more knowledge and, e.g., suggest new areas of actuation or new strategies.

Classification Model of Student Placement Datasets and Evolution of Classifiers of Weka Tool

Abstract: Placement of students is one of the most important objective of an educational institution. Reputation and yearly admissions of an institution invariably depend on the placements it provides it students with .Placement is a foremost expectation concerning both the institute and the student's perspective. This paper analyze the different data mining techniques and implement data mining technique to extract Knowledge from MBA student placement datasets. In this regard we takes student placement data to extract pattern with using supervised learning techniques of data mining. In this paper we have used WEKA software for experimental work and author also try to evaluate some supervised learning classifiers of WEKA with using student placement dataset.

Abstract 4937: Potential therapeutic targets with anti-inflammatory activity spectrum(COX-2/PGE2) of compounds associated with anti-cancer activityagainst oral and colorectal cancers

Abstract Introduction: COX-2 and PGE2 are prevalent inflammatory cytokines that exert influence on several cancer-related pathways throughout the gastrointestinal tract, spanning from oral cancers to colorectal cancers. One of the tactics being developed for the treatment of oral and colorectal malignancies is the identification of drugs that can selectively target receptors associated with COX-2 and PGE2. Materials and Methods: The objective was to identify potential therapeutic targets for oral cancer and colorectal cancer, specifically focusing on drugs that act as inhibitors or antagonists of the COX2/PGE2 pathway and its related receptor. A method for systematic data mining was created with a total of 32 targets associated with the COX-2/PGE2 pathway which were identified using the KEGG database. These targets were shown to be linked to both the oral cancer pathway and the colorectal cancer pathway. The compounds of interest were obtained from the Chembl25 database and their similarity was assessed using the Tanimoto similarity index, which was generated based on the Morgan fingerprints. The study employed molecular docking techniques to examine the interaction between the chosen drugs and the anticipated targets. A systematic data mining approach and computational methodology were devised utilizing the KNIME platform. Results: A total of 16,900 compounds were extracted for a set of 32 targets. Following the implementation of rule-based filters, the number of compounds was reduced to 13,800. The compounds that have a similarity index greater than 0.7 were chosen for inclusion in final analysis. Among various targets examined, empirical evidence was found to support the inhibition of expected target for PI-3 kinase-mTOR, GSK-3CD1, GSK3-AKT and PI3kinase-AKT pair. However, no evidence was found to support the inhibition of GSK3-Ikappa B-kinase. In order to examine the likelihood of the compound's interaction with anticipated target, molecular docking was employed to explore the interaction of one inhibitor compound for each pair. This investigation was conducted in contrast to the existing inhibitor compound and the subsequent findings were analyzed and discussed. Conclusion: The implemented data mining technique has demonstrated a notable decrease in the duration needed for comparable investigations, enabling the execution of experiments including numerous targets and a substantial number of ligands within a cumulative timeframe of 30 minutes. The findings of this study have the potential to be utilized in the identification of targets and the discovery of lead compounds for the purpose of managing oral as well as colorectal malignancies. Citation Format: Saravanan Sampoornam Pape. Potential therapeutic targets with anti-inflammatory activity spectrum(COX-2/PGE2) of compounds associated with anti-cancer activityagainst oral and colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4937.

Social Media, Mobile Payment, and Mobile Gaming for Intentional and Behavioral Recommendations

During the corona virus disease 2019 (COVID-19) pandemic, the Internet-based zero-contact economy model has increased people’s online social, entertainment, and business activities. Social media are social network platforms that support users’ social interaction to foster an information society. Mobile gaming refers to the use of mobile terminal devices for online entertainment. The combination of mobile payment and social media provides online users with an integrated information system platform for socializing, mobile gaming entertainment, and payment. A recommendation system filters information to predict a user’s rating or preference for a product/service. With recent advances in computer science, recommendation systems are gradually moving to develop intentional and behavioral recommendations. Thus, this study implements data mining analytics, including clustering analysis and association rules, to investigate mobile gaming, mobile payment, and social media preferences in Taiwanese gaming players. This study first finds several meaningful clusters/patterns/rules to present research findings and practical implications for intentional and behavioral recommendations (N = 2326).

Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)

Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.

Medical Costs Estimation Using Linear Regression Method

Medical costs are a significant issue in the health sector. High healthcare cost lead to the need to anticipate financial risks for individuals and insurance providers. Therefore, medical cost data analysis is necessary to estimate future medical expenses. This research implements data mining techniques using Simple and Multiple Linear Regression methods to estimate medical costs. The dataset used consists of insurance claim data obtained from Kaggle, which includes attributes such as age, gender, body mass index, number of children, smoking habits, region, and medical charges. The research findings that Multiple Linear Regression outperforms Simple Linear Regression in estimating the provided dataset, with R2 value of 80% and lower ?? MSE and MAE values than Simple Linear Regression. The application of linear regression in insurance claim data analysis can provide significant benefits for patients, hospitals, and insurance providers. Overall, this research highlights the effectiveness of data mining techniques, specifically linear regression, in estimating healthcare costs.

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN UNTUK MENGETAHUI POLA PEMBELIAN KONSUMEN PADA KOPERASI IDN BOARDING SCHOOL

School cooperatives are established to fulfill common interests and meet the needs of students. The transaction data of a cooperative is increasing day by day, but sometimes in a cooperative the data is left without any further utilization, as happened in the IDN Boarding School Jonggol cooperative. Utilization of the collected data can produce new information that can be used as a reference for cooperatives in determining a business strategy. Therefore, research was conducted on sales transaction data in the IDN Boarding School cooperative as an object to be analyzed using data mining techniques with an a priori algorithm, resulting in a pattern of consumer purchases that occur in the Jonggol IDN Boarding School cooperative. The transaction data used is transaction data at the IDN Boarding School cooperative. This study succeeded in implementing data mining techniques using the a priori algorithm in an application that will be used to process available transaction data, resulting in consumer purchasing patterns that tend to occur from a combination of available items. The association pattern that is formed with a minimum support of 40% and a minimum confidence value of 70% produces 9 association rules. The strong rule obtained is that if you buy a white uniform, you will buy a brown uniform with a support value of 50% and a confidence value of 83.33% items.

A Method to Enable Automatic Extraction of Cost and Quantity Data from Hierarchical Construction Information Documents to Enable Rapid Digital Comparison and Analysis

Context: Despite the effort put into developing standards for structuring construction costs and the strong interest in the field, most construction companies still perform the process of data gathering and processing manually. This provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly in large projects. Additionally, the lack of standardization makes cost estimation and comparison tasks very difficult. Objective: The aim of this work was to create a method to extract and organize construction cost and quantity data into a consistent format and structure to enable rapid and reliable digital comparison of the content. Methods: The approach consisted of a two-step method: firstly, the system implemented data mining to review the input document and determine how it was structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data were processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A large variety of information coming from real historical projects was successfully extracted and processed into a common format with 97.5% accuracy using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusions: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

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%.

Implementation of Data Mining in Grouping Data of the Poor Using the K-Means Method

Processed, especially data on people who are classified as poor. So that the provision of assistance from the central government or donors is right on target. In village governance, data processing often occurs that is not good and does not use technology, so if any assistance is provided it will make it difficult for the village government to distribute it to poor people. This study aims to classify the data of the poor by implementing data mining and applying the K-means algorithm for grouping data of the poor by applying the K-means algorithm. The research method used is observation research methods and direct interviews to obtain problems and data needed in data processing. The data used is community data. The results of the study obtained that the poor community group was divided into three parts, namely: Poor, Simple, and Able. So it can be seen that there is no shift or change in the data group towards the center of the cluster.

APPLICATION OF DATA MINING TO PREDICT STOCK INVENTORY IN RAGIL MART SHOP USING MULTIPLE LINEAR REGRESSION METHODS

Along with the development of information technology, the need for fast, accurate and relevant information is increasing. The need for accurate information is needed in everyday life, so that information will be an important element in the development of society today and in the future. . Often this information still has to be extracted from data with a very large population. The Ragil Mart store provides goods for the basic needs of the community, namely goods that concern the lives of many people with a high scale of meeting needs and are a supporting factor for people's welfare. the concept of implementing data mining that is used to assist the store in determining stock availability which is expected to be able to provide the best results so that the risk of errors in providing stock is small. In the grouping process, a method is used, namely the method.

IMPLEMENTATION OF DATA MINING WITH CLASSIFICATION AND FORECASTING METHOD USE MODEL GAUSSIAN NAÏVE BAYES FOR BUILDING STORE (STUDI CASE: TB SINAR JAYA)

In the construction industry, the building materials business is a necessity, one of the businesses that is currently growing rapidly is the property business. The public’s need for a place to live becomes a business opportunity sought by the public. The Industry 4.0 transformation brought building materials stores to the online market, one of which is Tokopedia. TB. Sinar Jaya has a dataset of inventory and sales with a total of approximately 15,000 data rows which are updated each month. With large amounts of data, data mining and machine learning methods are needed in data management. 5 years rapid development of TB. Sinar Jaya has not been without problems, such as competition with online stores that offer lower prices than offline stores and a lack of strong marketing strategy. In this case, TB. Sinar Jaya wants help in making marketing strategy decisions by utilizing information system technology and minimizing existing problems. Based on these problems, it is necessary to implement data mining and machine learning gaussian algorithms naïve bayes to find out the average prices available on Tokopedia to increase sales and carry out classification, forecasting and TSA (Time Series Analysis) at TB Sinar Jaya. Based on the results of testing/research, the gaussian naive Bayes algorithm has good accuracy results with an accuracy level of 0.71 and gains insight, that is, for potential buyers they can do Wishlist efficiency, and for segments that generate profits below 50% of the total profit the researcher recommends carrying out a campaign program according to customer profile in order to improve the resulting profitability.

Research on Efficient Work Innovation Effect Evaluation Based on Real-Time Online Public Opinion Data Classification and Detection Algorithm

Various heterogeneous network resources are collected through web crawlers, and after preprocessing such as data cleaning, they are written into the data center. And implement data mining technologies such as classification, clustering, association rules and anomaly detection. If an enterprise wants to maintain a good development ability and maintain a correct development trend, it must pay attention to and do a good job in ideological and political work, and take ideological and political work as the guiding light of its own operation and development. Improved the stability and accuracy of clustering results. The news topics are clustered to form topics, and the topic tracking technology is used to track specific news topics to achieve personalized services.

Prediksi Persentase Body Fat Menggunakan Algoritma CART dan M5’

Body Fat Percentage (BFP) is a measurement of total body fat that is used as an accurate measurement for the diagnosis of obesity. BFP measurement is sometimes difficult and inconvenient to perform, even though the picture of BFP’s value is very important for someone to find out the chances of being obese. To overcome this, data mining techniques can be used to measure the predictions of BFP values in a more practical way. This study implements data mining techniques, namely the CART and M5’ algorithm to predict a person's BFP value based on his/her body measurement. The CART algorithm uses the sample average values at leaf nodes to make numerical predictions, while the M5' algorithm builds a regression model for each leaf node with a hybrid approach. Regression trees provide a simple way of explaining the relationship between features and numerical results, but more complex model trees also provide more accurate results. In this study, the results show that the M5' algorithm is superior to the BFP dataset with a correlation value of 0.86 and an MAE value of 3.86.

Implementation Of The Data Mining Cart Algorithm In The Characteristic Pattern Of New Student Admissions

University of Harapan Medan is one of the private universities in North Sumatra which has an Informatics Engineering Study Program with Good Accreditation. With better accreditation, the number of students who register is also increasing. At the admission of new students, the committee has a huge pile of data, making it difficult in the process of whether the student passed or did not pass. Therefore, in this study, we will implement data mining with the CART (Classification And Regression Tree) algorithm. Data mining is a technique to determine the characteristic pattern of a variable or data criteria with a large amount. In the CART method, the data is first converted into testing data, which will then be used to form a classification tree by calculating the value of information gain, Gini index and goodness of split. From the results obtained, it will be re-determined terminal nodes, marking class labels and finally pruning the classification tree which produces a decision tree. In this study, the number of testing data was 75 with 3 criteria, namely the average value of report cards, CAT test scores, and interview scores. The results of testing data testing using RapisMiner 5.3 software produce 23 number of characteristic pattern rules, where node 1 is the CAT test score, level 1 branch node is the interview score criteria and level 2 branch node is the average report card value.

Comparison of C4.5 and Naive Bayes Algorithm Methods in Prediction of Student Graduation on Time (Case Study: Information Systems Study Program)

In tertiary institutions, students become one of the important parameters in the evaluation of study program organizers. Prediction of student graduation is a special concern to know, early identification for students is needed as an important action. Information processing to predict student graduation is by implementing data mining. The implementation of data mining can be applied if a university, especially a study program, does not yet have an early classification in achieving student graduation on time. The ITTP Information System study program is one of the study programs that does not have an early identification of student graduation on time. Determination of graduation for SI ITTP Study Program students includes GPA, TOEFL scores, and total credits. The purpose of this research is to find out which attributes have the most influence in predicting graduation of ITTP IS Study Program students. The method used in this prediction is by using the classification of the C4.5 Algorithm and Naïve Bayes. The classification is used to determine which attributes have an effect on predicting student graduation on time and to compare the two classification methods. The results obtained are the training set size 70% which has the best accuracy when compared to other training set sizes. Comparing the accuracy between the two methods, it is known that the C4.5 algorithm has good accuracy when training set size is 70% and Naïve Bayes has higher accuracy when training set size is 75%. Decision tree C4.5 interprets that the most influential attribute is the GPA as the root of the decision tree to predict student graduation on time. The research is expected to be used as a reference for the ITTP IS Study Program in formulating student graduation policies on time and as a reference for further researchers in predicting in the same field.