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The family is the first and main environment for a child to receive education, guidance and instill different values ​​and norms and develop various behaviors that are important for personal life, family and the environment. Parental division will lead to arguments and anger so that these emotions will tend to dominate the child's emotions. Based on the explanation above, researchers are interested in studying the impact of parental divorce on children's character. This research seeks to reveal how character formation influences children's psychology from the impact of parental divorce. In this research, descriptive qualitative methods were used. because to explore a particular case by conducting data mining to understand the case in detail (Tobing, H. et al., 2016). Researchers try to understand events or phenomena regarding the impact of parental divorce on the formation of children's character. According to Moh. Nazir (1988).

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.

This study aims to determine the best selling food and beverage products at Caffe Kopi Kito. Data mining is the process of extracting useful information and patterns from very large data. Data mining includes data collection, data extraction, data analysis, and data statistics. The Apriori algorithm is a classic algorithm in data mining. This algorithm is used to see the intensity of occurrence of the relevant itemset or frequent items or association rules. This study uses consumer transaction data for 30 days in January 2023. Transaction data will be collected first based on the day and number of transactions, then the transaction data that has been collected will be grouped according to each item, the data that has been grouped will be carried out a priori algorithm process to determine the most dominant product. Then a system design will be carried out whose result will be a website. The results showed that using the website-based a priori algorithm could determine the most dominant product at Caffe Kopi Kito and make it easier for users to determine the most dominant product. Based on the results of product sales analysis at Cafee Kopi Kito, it can be concluded that working on the a priori algorithm on Caffe Kopi Kito using a website can be said to have the result of a product combination and in the future it can be used to create the best-selling menu packages at Cafee Kopi Kito.

Data mining is a technology that plays an important role in supporting data-driven decision making, especially in complex and dynamic higher education environments. In the context of education management, the ability to predict student graduation is an essential aspect because it can help institutions plan strategic steps, intervene earlier, and optimize academic resources. This study aims to apply the C4.5 decision tree algorithm to build a student graduation prediction model based on academic data. The research dataset includes key variables such as Grade Point Average (GPA), total Semester Credit Units (SKS) taken, and student attendance rates during lectures. The analysis was conducted using the C4.5 algorithm, which is known for its high level of interpretability, making the model results easy to understand by policy makers. The test results showed an accuracy of 84.6%, indicating that this method has the potential to support data-based academic management systems. These findings are expected to serve as a basis for educational institutions to improve the effectiveness of monitoring and evaluating the student learning process.

Divorce cases in West Java Province have increased every year. The profile map of divorce cases that occurred in each region is not yet known, so efforts to provide guidance to minimize divorce cases are not optimal. Divorce case data is also not equipped with a visualization feature that makes it easier for authorized officials to easily understand and analyze data. This study analyzes divorce cases in regencies/cities in West Java Province, using the K-Means Algorithm Clustering data mining method. The clustering method is grouping data based on the same characteristics. In determining the number of clusters, that is by using the value of the Davies Bouldin Index. The results of this study obtained the best cluster of grouping divorce cases, there were 2 clusters, namely cluster 0, there were 5 regencies and 9 cities, while in cluster 1 there were 13 regencies, with a Davies Bouldin index value of 0.168 and an avg.within centroid distance value of 5,870. Cluster 0 is the city/district with the lowest divorce cases and cluster 1 is the city/district with the highest divorce cases.

Lot of problems arise in selecting scholarship recipients in a large number of submissions, the existence of several searches used, and the selection of files for scholarship applicants is still manual, so a system is needed that can speed up, help, and make it easier in the decision-making process to lighten work. student section. In supporting decisions this system will use the Naïve Bayes Classifier Method to determine what is acceptable and not acceptable. The NBC method can analyze and make improvements to old data, and the resulting data will provide simpler probability values that can later be used to make decisions. From the results of the research that has been carried out, it can be realized that the application of the data mining algorithm using the Naïve Bayes Classifier can be carried out to select scholarship recipients at Stikubank University Semarang. The result of the selection of Unisbank Semarang scholarship recipients is the accuracy value. 72% of the 135 data which is divided into 100 training data and 35 test data.

This data mining association processes 1224 Gamefantasia ticket redemption transaction data. The goal is to find a pattern of association between goods as a recommendation for structuring the display of goods at the cashier counter and increasing ticket exchange transactions. Modeling uses a comparison of two algorithms, namely the Apriori algorithm and FP-Growth. The data analysis method with the CRISMP-DM method is then processed by RStudio software. The results of the study with the same parameters support 0.02 and confidence 0.1 FP-Growth algorithm formed 53 rules, the strength of the association rule 6.2%, the accuracy was1245%. Whereas the Apriori algorithm forms only 12 rules, the strength of the association rules is 2.1% and the accuracy is 7.8%. Thus, it can be concluded that the use of the FP-Growth algorithm has better results than the Apriori algorithm because it has the highest accuracy in finding transaction patterns.

This study aims to determine the role of information technology in public sector accounting in preventing fraud that occurs in public sector organizations. The research conducted was a literature review research based on articles appropriate to the research topic for further analysis. The result of this research is that there are fraud prevention methods which include technical strategies and preventive strategies. The technical strategy includes efforts to prevent fraud through the use of privacy officer services, IoT access, managing data access, establishing timely reporting, and controlling data in real time. The preventive strategy is in the form of developing a fraud detection system that is run using machine algorithms, data mining, and meta learning methods.

Akomodasi merupakan orientasi sosial yang bermakna keutuhan sosial demi menjauhi serta mendamaikan kegentingan, pertikaian yang saling berkaitan. Seumpama bentuk kegiatan akomodasi yang bermanfaat mempersiapkan perlengkapan guna melengkapi keperluan. provinsi jawa barat terdapat tempat wisata yang ramai dikunjungi oleh wisatawan nusantara dan mancanegara oleh sebab itu pengunjung menggunakan fasilitas akomodasi yang tersedia di setiap daerah, wisatawan nusantara ramai menginap di villa sedangkan wisatawan mancanegara lebih memilih hotel berbintang. Penelitian ini memfokuskan bagaimana pengelompokan wisatawan daerah wisata akomodasi dan jenis wisatawan terbanyak yang ramai berkunjung menggunakan algoritma k-means, analisis ini memakai metode Knowledge Discovery in Database (KDD). Teknik pengumpulan data atau pengumpulan data bersumber dari Dinas Pariwisata dan Kebudayaan Provinsi Jawa Barat. Dengan tujuan kiranya menjadi pengetahuan untuk wisatawan serta mampu mempublikasikan wisata di media sosial dan diharapkan akan lebih meningkat jumlah wisatawan yang berkunjung dan meningkatnya pembangunan sarana dan prasarana di setiap daerah akomodas.Hasil dari penelitian ini bahwa daerah akomodasi yang ramai dikunjungi adalah Kota Bandung dengan jenis wisatawan yang datang yaitu pengunjung  dalam negeri (Nusantara).

Indonesia merupakan negara dengan beberapa kota di setiap provinsinya, namun tentunya setiap kota mempunyai tingkat inflasi yang berbeda setiap tahunnya. Dengan banyaknya data inflasi di Indonesia, sulit bagi pemerintah untuk mengkategorikan angka inflasi. Penulis berinisiatif melakukan penelitian tentang mengelompokkan nilai inflasi pengeluaran di jawa barat. Tujuan membuat laporan ini merupakan untuk memperoleh informasi dengan kualitas terbaik dari data yang diproses. Proses ini di harapkan dapat membantu pemerintah dalam mengetahui nilai inflasi berdasarkan kelompok pengeluaran tertinggi dan terendah di Jawa Barat. Metode yang digunakan adalah metode k-means clustering. Penelitian ini juga di dukung menggunakan salah satu perangkat lunak atau tools untuk mengolah data mining yaitu RapidMiner. Didapat hasil cluster terbaik yaitu 5 cluster berdasarkan nilai DBi 0,063. Dimana yang termasuk ke dalam cluster tersebut adalah cluster 0, cluster 2, dan cluster 4 dengan tingkat inflasi rendah berjumlah 5 Kabupaten/Kota. Dan yang termasuk ke dalam tingkat inflasi tinggi adalah cluster 1 dan cluster 3 berjumlah 2 Kabupaten/Kota. Hasil dari data ini juga menunjukkan nilai akurasi sebesar 100%, sehingga bisa disimpulkan jika algoritma k-means mempunyai nilai akurasi tinggi.