Articles
<![CDATA[User Satisfaction Analysis of Paylater Services Using K-Means Algorithm in Campus ]]>
<![CDATA[Anwar, Syahrul]]>;
<![CDATA[Hikmawati, Nina Kurnia]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2533
<![CDATA[In the 4.0-based digital era, the use of e-commerce is increasing. The convenience provided to e-commerce users is increasingly being considered by companies engaged in e-commerce. Paylater is a fairly new payment method among Indonesian e-commerce, so research is needed to improve the service and satisfaction of e-commerce users, especially those using the paylater payment method. The purpose of this study is to analyze user satisfaction with paylater services using the k-means algorithm on campuses in region 3 Cirebon. This research is also to find out the benefits of paylater used by students. This research is a type of quantitative research using the k-means algorithm to determine the classification of paylater user satisfaction in several e-commerce applications at several universities in region 3 Cirebon which is then clustered. The results of the study show that Cirebon students in the Campus 3 area are satisfied with services from companies or online shops that have paylater payment facilities]]>
<![CDATA[Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan ]]>
<![CDATA[Sopyan, Yayan]]>;
<![CDATA[Lesmana, Agrian Dwi]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2697
<![CDATA[In marriage, the thing that is most avoided is a divorce. Divorce is the termination of the husband and wife relationship which is carried out legally at the time of trial. From year to year, there is an increase in the number of divorces in Indonesia, including the number of divorces in Kuningan Regency. This study analyzes divorce cases in villages in Kuningan Regency, the analysis is carried out by using data mining clustering methods using the K-Means algorithm. The clustering method is grouping data based on the same characteristics. In determining the number of clusters by using the value of the smallest Davies Bouldin Index, it is hoped that the number of clusters formed can be more optimal. The results of this study are that there are 4 clusters consisting of villages or sub-districts with different divorce rates, namely the highest divorce rate, high divorce rate, medium divorce rate, low divorce rate, and lowest divorce rate]]>
<![CDATA[Penerapan Forecasting Menggunakan Metode Time Series Untuk Menentukan Proyeksi Sales di Perusahaan Manufacturing Furniture ]]>
<![CDATA[Prasakti, Lukito Angga]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i4.2802
<![CDATA[The large population certainly encourages companies, including manufacturing companies, to continue to develop their production both in terms of quality and quantity, especially since the number of companies with the same focus is quite large. This is because, every certain company wants to get a lot of profit and minimal consumer or customer complaints. One way that is considered to be able to overcome this is by carrying out company policy referring to forecasting product sales in the future. Therefore, researchers want to find out more about the application of forecasting to determine monthly sales projections for the following year at a Furniture Manufacturing Company. The aim is to determine the role of forecasting in making policies on the company's production at a later time by considering sales projections based on the company's forecasting results. The method used is time series by collecting data through documentation at the regular local market in 2022 to be precise 12 months. After the data is collected, it will be analyzed in depth so that it is known from the research results that careful forecasting will produce forecasts that are not far from reality and can help in calculating sales projections for furniture manufacturing companies at a later time, with a MAPE value of 0.06]]>
<![CDATA[Market Basket Analysis to Determine Muslim Clothing Supply in Indonesia Ahead of Eid Al-Fitr ]]>
<![CDATA[Indra Gunawan, Gun Gun]]>;
<![CDATA[Aji, Tri Wahyu]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v6i1.5162
<![CDATA[Enterprise transaction data is a valuable source of insights for companies to increase sales. In preparation for Eid al-Fitr, this study leverages Market Basket Analysis with the FP-Growth algorithm to uncover buying patterns within Indonesia's Muslim clothing market. Market Basket Analysis is one way to explore information through data to find customer buying patterns that are often used as insight into company decision-making. The data processing method uses the FP-Growth algorithm, which generates association rules based on calculating the frequency of occurrence of itemsets. Using the FP-Growth algorithm gives good results in the determination of association rules. From Muslim fashion store transaction data over the last 12 months, it produced 30 item set patterns with a minimum support value of 0.009 and confidence of 0.58. By identifying these in-demand product pairings, businesses can make informed decisions about stock allocation. This ensures they have the right combination of items available to meet customer needs during the surge in demand leading up to Eid al-Fitr. Additionally, these patterns can inform targeted promotional campaigns and strategic bundling initiatives, maximizing sales and customer satisfaction throughout this critical sales period.]]>
<![CDATA[Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier ]]>
<![CDATA[Bustomi, Yosep]]>;
<![CDATA[Nugraha, Anwar]]>;
<![CDATA[Juliane, Christina]]>;
<![CDATA[Rahayu, Sri]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i1.11968
<![CDATA[The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process. ]]>
<![CDATA[COMPARISON OF K-N EAREST NEIGHBOR AND NAÏVE BAYES ALGORITHMS FOR PREDICTION OF APTIKOM MEMBERSHIP ACTIVITY EXTENSION IN 2023 ]]>
<![CDATA[Fauzia, Fathia Alisha]]>;
<![CDATA[Adjani, Kannisa]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i2.12081
<![CDATA[So far APTIKOM as the Informatics and Computer Higher Education Association has provided many opportunities for registered members to participate in discussions on the development of science among fellow association members, access to various professional experts, as well as technical and non-technical guidelines in the field of education. With the various opportunities above, it is hoped that all members will support the activities of each member who has joined or has just joined so that a good association can be created. This study aims to find out about the problems that occur in APTIKOM, namely members who have registered as members but rarely renew their membership which results in data accumulation in APTIKOM. This research method uses the k-nn and naïve Bayes algorithms by using data sets from 2012 to 2022. The dataset used is APTIKOM member data and has 5 attributes namely name, gender, last education, institution and validation secret. To calculate the research test using a rapid miner. The purpose of this study is to predict whether in the following year there will be a membership renewal process for all APTIKOM members who have been recorded from 2012 to 2022. Furthermore, the results of this study have a different level of accuracy. Where for k-nn the resulting accuracy is 94.00% and for the result of naïve Bayes is 91.35%.]]>
<![CDATA[Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes ]]>
<![CDATA[Alam, Alam]]>;
<![CDATA[Alana, Divi Adiffia Freza]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.12998
<![CDATA[Diabetes mellitus is an urgent global health problem and has a major impact on people around the world. This disease is characterized by high levels of sugar (glucose) in the blood due to disturbances in the production or use of the hormone insulin by the body. This study aims to carry out accurate early detection of diabetics so that they can be treated as soon as possible to reduce the risk of death and to compare the two algorithms that have the best level of accuracy. The algorithms used in this study are the C4.5 and Naïve Bayes Decision Tree Algorithms. The results of the experiments carried out in this study the Decision Tree Algorithm C4.5 and Naïve Bayes can be used in modeling the early detection of diabetes. The highest average accuracy results were obtained at 90.835% using the Decision Tree C4.5 Algorithm. As for the Naïve Bayes Algorithm, an average accuracy rate of 90.745% is obtained. The pruning process was carried out using the Decision Tree Algorithm C4.5, the accuracy performance increased to 91.30%. There were 18 patterns or rules for the early detection of diabetics from the built model. The determination of attributes, the number of attribute dimensions, and the number of samples greatly affect the performance of the model built.]]>
<![CDATA[Implementation Of Data Mining In Digital Marketing Of Knit Bag Msmes West Bandung District ]]>
<![CDATA[Saepulloh, Yusep]]>;
<![CDATA[Amirulloh, Moch Mumin]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ International Journal of Science Education and Technology Management Vol 3 No 1 (2024): International Journal Of Science Education and Technology Management ]]>
Publisher : <![CDATA[Yayasan Azka Hafidz Maulana ]]>
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DOI: 10.28301/ijsetm.v3i1.24
<![CDATA[Knitted Bag Micro, Small and Medium Enterprises MSMEs in West Bandung Regency face challenges in exploiting the potential of the digital market. This research aims to apply data mining techniques to understand consumer patterns and increase the effectiveness of digital marketing strategies. In the methods section, the approach used in the clustering analysis is explained in detail. The data used, the variables observed, as well as the clustering techniques and statistical tests applied are described. In addition, the data processing and analysis procedures carried out are also explained to provide a clear understanding of the research methodology. This section presents the results of the clustering analysis and statistical tests performed. This includes the results of the clustering process using K-Means, the resulting cluster centers, as well as the results of the variance test showing the differences in means between cluster groups. Patterns in the data, differences between cluster groups, as well as the potential use of analysis results for developing marketing strategies and customer management are discussed in depth. This research succeeded in grouping customer data into five different clusters based on observed variables. Clustering analysis has helped in understanding patterns in data and identifying different customer groups. The implications of these findings for customer management and marketing strategy development are discussed, and suggestions for further research and development are provided.]]>
<![CDATA[Library Book Loan Data Clustering Using K-Means Algorithm to Improve Book Loans ]]>
<![CDATA[Pratama, Denni]]>;
<![CDATA[Hermawan, Sari]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ Edulib Vol 13, No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Pendidikan Indonesia ]]>
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DOI: 10.17509/edulib.v13i1.50003
<![CDATA[Entering the post-pandemic new standard era in 2022 as of June, the number of borrowed books at the Indonesian University of Education Library could be more optimal compared to the year before the pandemic (2018 - 2019). Lending in 2022 can still be increased by arranging the most borrowed books in one group. This research aims to classify books more optimally, which will be applied to book arrangement. Optimal book arrangement allows library visitors to find books more efficiently based on the books that are most often borrowed so that they are interested in borrowing other books in a group. Data mining is a term used to describe knowledge in a database from a repository by finding patterns and trends in data through examination with statistical and mathematical techniques. Clustering is a data mining method that can be used to determine the data clusters. One of the algorithms that can be used is K-Means. The clustering pattern obtained shows 2 (two) grouping clusters. Book titles in cluster 0 contain book titles related to research methodology, statistics, measurement scales, assessment, and learning evaluation. While cluster 1 tends to contain psychology, counseling, religion, philosophy, management, economics, and history. This data can be used by librarians in prioritizing purchasing a collection of books in the subsequent procurement.]]>
<![CDATA[Comparative Analysis of Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients ]]>
<![CDATA[Damayunita, Aina]]>;
<![CDATA[Fuadi, Rifqi Syamsul]]>;
<![CDATA[Juliane, Christina]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.919
<![CDATA[Heart disease is still the leading cause of death. In this study, we tried to test several factors that can identify patients with heart disease using 3 classification algorithms: Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The purpose of this study is to find out which algorithm can produce the highest accuracy in classifying, analyzing, and obtaining confusion matrix values along with the accuracy of predicting heart disease based on several factors or other comorbidities that the patient has, ranging from BMI to the patient's skin cancer status. From the results of trials conducted by the SVM algorithm, it has the highest accuracy value, which is 92% while the Naive Bayes algorithm is the lowest with an accuracy value of 88%.]]>
