THE C45 ALGORITHM METHOD IN PREDICTING DAMAGED GOODS CASE STUDY: SEMULI RAYA INDOMARET SHOP

The industrial world that is the target of the process and learning outcomes of vocational high school (SMK) has its own character and nuance. Therefore, vocational education institutions in the learning process should be able to make the appropriate learning approach and in accordance with the industrial world. One approach to learning that is based on production and learning in the world of work is by industry-based learning or known as Teaching Factory, where in this model apply learning that involves direct students in goods or service activities are expected to have the quality so it is worth selling and accepted by consumers. The method used is descriptive approach. The purpose of this research is to get the design of the teaching factory based on the competency requirements of the graduates of the spouse industry, especially in the engineering department. The results of this study is expected to be one of the choice of model factory teaching in the field of machinery engineering in accordance with the products and competencies of the graduates that the industry needs.

Sabilillah Educational Cash Unit is a unit within the sabilillah educational foundation which is engaged in education. The stored cash processing data will be utilized using data mining so that it can be used as a decision support for finding information that is useful in evaluating the data used. Various methods contained in the data mining, the authors will make a comparison of the method techniques from the data mining. The use of the decision tree and, K-means method is implemented using the Rapid Miner application, which will later be analysed of each of these methods to determine the strategy to look for students who have the potential to save the hajj savings. This research was conducted with a group of data to determine the percentage value of precision, recall and accuracy. The results of this study that the C.4.5 method has a better value than other methods on the recall and accuracy, while the K-Means method has a precision value better than the other methods.

PT. Elleair Interantional Manufacturing Indonesia is a company engaged in the field of manufacturing baby diapers. With the increasing market demand causing an increase in the production process, what is often experienced is that there is often a lack of finish good product to meet consumer de mand due to delays in the production process. To make it easier for companies to look for factors that can increase production result, the authors coduct research with data mining using the naïve bayes method. In this study the training data and testing data were tested using the RapidMiner application with the naïve bayes algorithm where the tested data were 500 data. Testing is done by calculating the value of precision, recall, AUC dan accuracy using the RapidMiner Application and using Microsoft Excel and calculating the final probability of each class to calculate predictions of product result. With the naïve bayes method we can calculate predictions of production result based on data from the previus year as training data to anticipate shortages in production due to factors that can hider the production process. From the results of the analysis obtained factors that affect production result, namely, the number of materia used for the production of 318 data. The human error factor with the category of “No” as much as 305 data also influences because the less the occurrence of human error the production results are also high. Stop delivery factor with the category “No” as many as 299 data, with fewer cases of stop delivery, the more finish good product that can be sold

Homeless and beggars are one of the problems in urban areas because they can interfere public order, security, stability and urban development. The efforts conducted are still focused on how to manage homeless and beggars, but not for the prevention. One method that can be done to solve this problem is by determining the age pattern of homeless and beggars by implementing Algoritma Apriori. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtaines combination pattern of 11 rules with a minimum support value of 25% and the highest confidence value of 100%. The evaluation of the Apriori Algorithm implementation is using the RapidMiner. RapidMiner application is one of the data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. The test results showed a comparison of the age patterns of homeless and beggars who had the potential to become homeless and beggars from of testing with the RapidMiner application and manual calculations using the Apriori Algorithm.

Data mining is a form of method advancement in computerization that can dig past data into very valuable information. The problem in this study is that the sale of beauty and skincare carried out by Toko Hayati Store is still done manually so that it can cause it to not match the stock in the storage warehouse with changing market demand. Data mining with the K-Means method is one solution to this problem by grouping similar data, in this study grouping into two, namely best-selling and unsold products. The purpose of this study is that the store can provide stock of products in the warehouse according to market demand. Using a sample of 30 data resulted in 18 data as skin care products were not selling well and 12 data as skin care products were not selling well. With the results of a 100% similarity between manual calculations and using the rapid miner application, it can be concluded that the K-Means algorithm can be used as a solution to the problems that exist in the Toko Hayati Store.

Facilities are a support for the implementation of a process in a business in this case the Dharmawangsa University campus, by increasing the level of student satisfaction with the facilities available, student comfort in learning will be achieved. This study used questionnaire data on 70 respondents who were students of Dharmawangsa University. Previously, the questionnaire consisted of 42 questions. After being tested for validity and reliability, 20 questions were obtained. Then from the results of the questionnaire data, a classification of student satisfaction levels will be carried out using one of the algorithms in data mining, namely Naive Bayes. The results obtained by using the rapid miner application with 50 training data and 19 data testing data, the results obtained are a classification accuracy of 73.68% with a recall value of 83.33% and a precision of 83.33%, then there are 9 attributes that have a value dissatisfaction is higher than the satisfaction score given by respondents, this can be a concern of the leadership to improve these facilities so as to increase the level of satisfaction with the facilities provided by Dharmawangsa University.Keywords: Student Satisfaction, Data Mining, Naive Bayes

Companies in the industrial world, both manufacturing and service industries, are required to face market competition in order to compete and survive. One thing that companies need to pay attention to is the quality of the products produced. Good product quality will make consumers like the product. Therefore, improving product quality must be considered by the company. One of the companies that produces fabric, namely PT. XYZ, has a problem of not knowing the level of quality of the fabric produced, so this study aims to determine the quality of the product and provide suggestions for improvement to the company. There are defects in fabric production, namely double weft with a percentage of 27.7%, loose weft 26.7%, broken warp 24.5% and thick weft 21.2%. Improvement proposals are given to the highest percentage of defects, namely double weft with the following proposals: Carrying out machine maintenance and replacing the new gun eye so that the gun eye is not blunt, replacing the roller on the worn warping machine with a new roller, providing comprehensive and intensive training to workers on how to use the machine effectively and correctly. It is hoped that this research can help the company improve the quality of the fabric products produced.

Student delays in completing their studies are experienced by most higher education institutions, for example at STMIK Widuri. STMIK Widuri must be able to predict student graduation early to prevent graduation that is not on time and maintain a good name and the accreditation assessment that has been obtained. For this reason, this research was conducted to predict the graduation of STMIK Widuri students using the classification method with the Naïve Bayes algorithm. Naïve Bayes is a classification algorithm that uses probability and statistics to predict a class. The dataset used is lecture activities of STMIK Widuri students class of 2021 from 2021-2022 odd to even 2022-2023 academic year and processed using the Rapidminer application. The dataset is processed through the stages of Knowledge Discovery in Database, including selection, pre-processing, transformation, data mining and evaluation stages. From the evaluation results using the confusion matrix on the distribution of training data 50% and data testing 50%, this study resulted in an Accuracy 93,10%, Precision 95,24%, and Recall 90%. In this way, it is hoped that STMIK Widuri can utilize attributes of the data stored in the database to be processed more optimally, for example using existing techniques in data mining.

Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock. Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index

Advances in technology and information currently produces smart innovations in business, which can be called business intelligence. One that we can use is Data Mining technology in digging useful information from sales company data warehouse. The purpose of this research is to apply data mining decision decision tree algorithm C4.5 on fast food outlets business and expected to provide information in the form of sales information about food menu that most liked by customers and less popular (bestselling and less in demand). The methodology used in classifying the sales of this research uses the steps of Algorithm C.45, The process uses five steps in KDD (Knowledge Discovery in Databases), which perpetuates activities such as pre-processing, transformation, data mining, interpretation and evaluation. In addition to performing calculations manually, this research case is also tested using Rapidminer application. From the results of the experiment to find data from the sales data of fast food outlets using algorithm C4.5 results of entropy and the highest gain is 1.501991 on the Food Menu attributes on manual calculations. When using the Rapidminer application the results of the results tree as shown in Figure 3.2. Price (s) - Sold Out - Food Menu (Bento Rice = Less Selling, Chest = Laris) Weight (weight) each attribute: Price (0.738), Menu Type (0.067), Sold Number (0.156), Sales Status (0.040).

Employee discipline is one of the most important factors for the progress of the company. PT. Sumatra Core Cellular (PT. SIS) Pekanbaru has implemented a digital attendance application, but the company has not evaluated the application to determine the level of employee discipline. Data mining is the process of extracting useful information from a large database population. One of the data mining methods is the K-Means algorithm. The data mining process uses the method of K-Means algorithm with 2 clusters namely discipline and less disciplined categories. The data used is attendance data of 159 employees, namely data on tardiness, non-attendance (TAP), attendance hours and 4 selected questionnaire questions. Tools for grouping with the Rapidminer application. Using the K-Means algorithm method, it is known that cluster 0 consists of 133 employees or 83.64% with a disciplined category and cluster 1 produces 26 employees or 16.35% with a less disciplined category. Judging from the accuracy of attendance hours, employees in cluster 0 are more likely to be present at 07.45 - 08.15 and in cluster 1 they are more likely to be present at 08.15 - 08.30. In terms of lateness and TAP, there is a lack of discipline in cluster 1. From the level of satisfaction with the application based on 4 selected questions, it can be concluded that the digital attendance application increases the discipline of the employees. The results of this analysis can be used as a reference for evaluating employee discipline, determining promotions and improving employee discipline in the future.

Medical records are a very important source of information in the world of health. Medical records document a patient's medical history, diagnosis, treatment, and care patterns at health facilities. However, with the large amount of data that continues to grow every day, it is often difficult for medical personnel and health facility managers to manually analyze and find useful patterns. Community health centers, as primary healthcare facilities, play an important role in addressing public health issues. Community health centers often face limitations in effectively processing available data. Therefore, methods are needed to help uncover hidden information from medical record data. One approach that can be used to analyze big data is data mining. Data mining allows users to find patterns, trends, or certain relationships that were previously unseen. In medical records, the application of data mining techniques can help identify disease patterns, relationships between diseases, and risk factors that contribute to certain diseases by using the apriori method to obtain better health service planning. From testing using the RapidMiner application, this study identified complaints, medical history, and causal factors. The results showed that there were 5 association rules formed with the highest Best rule value of 14% support and 62% confidence. The rule was “If the causal factor is genetic, the complaint is dizziness, then the medical history includes a history of asthma since childhood.”

The hotel accommodation business highly depends on consumer preferences regarding products and services. The intensity of hotel guest visits and the level of guest satisfaction with the services provided by hotel management can be seen from various guest reviews on websites used as reservation media. Therefore, this research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method to implement the data mining process using the webharvy application and the machine learning process using the Rapidminer application. Meanwhile, the operators used are Synthetic Minority Over-sampling Technique SMOTE in overcoming data imbalances and sentiment extract operators to obtain a total string score before sentiment labels are determined and processed using the Support Vector Machine (SVM) algorithm. The results of this study showed that SVM without using SMOTE operators resulted in an accuracy value of 95.82%, a precision value of 95.80%, a recall value of 100%, and an Area Under Curve (AUC) value of 0.798 (79.8%). Otherwise, SVM performance using SMOTE operators produces an accuracy value of 92.05%, a precision value of 100%, a recall value of 84.08%, and an Area Under Curve (AUC) value of 99.99 (99.9%). Furthermore, based on ten popular words, hotel guests are concerned about breakfast, staff, pool, room, and hotel. Thus, the guests' highlights are the menu served by the hotel, the service provided by employees, room conditions, and hotel brands. Therefore, hotel management needs to improve the quality of products and services to increase satisfaction and intention to stay again.

At present, with the scale expansion of computer software, only rely on manual for software development, maintenance and other work is more difficult. Data mining technology can accelerate the speed of software development, and can in many databases find valuable data. This paper makes in-depth studies on software engineering data mining technology, and introduces the influence of data mining technology.

The use of a learning management system (LMS) is one of the media that can be used to disseminate lecturer materials to students. Materials that can be uploaded on the LMS can be in the form of lecture materials in the form of files, videos, or questions. The effectiveness of LMS can be evaluated by looking at activities in using LMS. The effectiveness of using LMS can be seen from the log. Log results from LMS can be evaluated in various ways and one way is to use data mining clustering models. The clustering model can be used to create student groupings and the clustering results can be labeled in the form of categories, such as very good, good, and bad categories. This labeling depends on the clustering results that will be processed in the modeling. The research method uses CRISP DM which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The beginning of the research process is carried out by taking log data in the Moodle LMS. The clustering model in this research will use the K-Means algorithm and the evaluation of clustering results will be evaluated for performance using the Davies-Bouldin method. Implementation of data mining processing using Rapid Miner application. The datasheet used is a datasheet taken from the LMS log of the Computer Programming course in the Mechanical Engineering study program - AKPRIND Institute of Science & Technology Yogyakarta odd semester of the 2021/2022 and 2022/2023 academic years. The results of the study resulted in the best clustering based on the Davies Bouldin method of 2. The clustering results, cluster 0 consists of 28 data named the category of frequent access to LMS and cluster 1 consists of 54 with the category of not frequent access to LMS.