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This study aims to investigate the factors influencing the continuance intention of Al-Kautsar Senior High School students in using metaverse-based learning media. The background of this research lies in the rapid adoption of immersive technologies in education, while students’ levels of acceptance have not yet been fully understood. The objective is to identify the antecedents of satisfaction, which subsequently influence continuous intention. The research model examines the effects of perceived interactivity, perceived sociability, perceived enjoyment, perceived ease of use, perceived security, and social influence on satisfaction. A quantitative approach was employed by distributing questionnaires to students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that satisfaction is a very strong and statistically significant predictor of continuous intention to use metaverse applications (β = 0.716, p < 0.001). The six hypothesized antecedent variables were not found to have a significant individual effect on satisfaction. In conclusion, for digital native students at Al-Kautsar Senior High School, factors such as ease of use, interactivity, and enjoyment have shifted from being drivers of satisfaction to becoming basic expectations (hygiene factors). Satisfaction itself emerges as the primary determinant, likely influenced by more substantive elements such as content quality or pedagogical design rather than merely the technical features of the platform.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.

Pengembangan sistem informasi akademik bertujuan untuk memberikan sarana dasar kepada perguruan tinggi. Sistem informasi akademik diperlukan untuk mengelola seluruh kegiatan akademik diantaranya mengelola kartu rencana studi, mengelola kartu hasil studi, mengelola data dosen dan tenaga kependidikan, mengelola data mahasiswa, mengelola kelas, mengelola pertemuan dan presensi. Memiliki sistem informasi akademik akan mengurangi resiko keamanan data serta mendukung kemandirian pengelolaan teknologi informasi. Pengembangan sistem informasi ini menggunakan metode penghimpunan data menggunakan metode tanya jawab (interview) dan pengamatan lapangan. Performance, Information, Economics, Control, Efficiency dan Service adalah metode PIECES yang akan digunakan dalam menganalisa sistem. Unified Modelling Language (UML) adalah metode perancangan sistem yang digunakan dalam penelitian ini. System engineering, Requirement analysis, Design, Coding, Testing dan Maintenance adalah metode pengembangan sistem Waterfall yang peneliti gunakan. Sistem informasi akademik dikembangkan sesuai kebutuhan institusi saat ini tetapi seiring perkembangan peraturan dan kebutuhan institusi maka perlu dilakukan evaluasi kinerja secara periodik.

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.

Merancang dan memelihara aplikasi Web merupakan salah satu tantangan utama dalam industri perangkat lunak dari tahun 2000. Web Modeling Language (WebML), suatu pemodelan untuk menetapkan website yang kompleks ditingkatan konseptual. WebML memungkinkan uraian tingkat tinggi tentang  website di bawah dimensi orthogonal yang terpisah: isi datanya (structur model), halaman penyusunnya (compotition model), topologi dari relasi antar halaman (navigation model), tata letak dan persyaratan grafis untuk halaman yang dibuat (presentation model), dan pemilihan fitur untuk masing-masing isi (personalitation model). Semua konsep dari WebML dihubungkan dengan notasi grafis dan sintaksis XML. Spesifikasi WebML tidak terikat pada bahasa yang digunakan pada sisi-klien dalam mengirimkan aplikasi ke para pemakai, dan juga  platform yang digunakan pada sisi-server dalam memasukkan data ke site, tetapi dapat secara efektif digunakan untuk hasil implementasi halaman dalam pengaturan spesifikasi teknologi. WebML menjamin pendekatan model-driven pada pengembangan website, merupakan kunci utama dalam menggambarkan generasi CASE tool dalam membangun site yang kompleks, mendukung fitur tingkat lanjut seperti akses multi-device, personalisasi, dan manajemen evolusi.  

This article discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data and also to compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data). Though the application of choice models to clickstream data is relatively new, and review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The article discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation and also offers directions for further research in these areas. Notwithstanding the numerous challenges associated with clickstream data research.

Decision Support System couple the intellectual resource of individuals with the capabilities of the computer to improve the quality of decision. It's a computer based support system for management decision makers who deal with semi-structured problem. An integrated decision support system for use in an machine mollen product has been developed. It incorporates a linear Programming model that represents the contribution optimal and optimizes the production water pump and mollen machine. An optimization model is performed using a management scient model called linear programming approach in older to determine media selection. To use this model, the DSS needs ti interface with another software. Mathematical Programming is a technique used in mathematical models, particularly optimization models, to assist in decision making. The Simplex Method is "a systematic procedure for generating and testing candidate vertex solutions to a linear program." (Gill, Murray, and Wright, p. -137) It begins at an arbitrary corner of the solution set. At each iteration, the Simplex Method selects the variable that will produce the largest change towards the minimum (or maximum) solution. The  development of computer programs to be used as Decision Support Systems involves several tasks such as mathematical modeling, technical and data collection and development of a user friendly interface.

The passage analize about the model of DSS. Models begins step from DSS concept. After that, we must choose modeling approach, beside decision making processes. We have many types of model DSS, especially in statistical, financial and optimization. Finally, we get benefit of models.