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Website security is an important aspect of designing a website and managing web systems. However, many developers still pay little attention to security aspects from the early stages of development. In fact, the website that has been built will be the target of attacks by hackers at any time. Therefore, this research aims to analyze the vulnerability of the SMAN 1 Banjar Agung website based on the OWASP Top 10 standard. The research method was conducted through vulnerability assessment using OWASP ZAP tools with the stages of spidering, passive scanning, and active scanning. This test allows identification of vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), and security configuration weaknesses. The scan results showed eight vulnerabilities, consisting of two medium, three low, and three informational vulnerabilities. Although the risk level is low, the website still requires mitigation through the application of security headers, dependency updates, and removal of sensitive information to make the system more secure and stable.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

As a vocational education institution, SMK Swadhipa 1 Natar is required to provide adequate facilities to support the development of its students' technical and practical skills. Although some facilities are already available, student complaints remain regarding the condition, availability, and utilization of these services, particularly those related to information technology.This study aims to analyze the level of student satisfaction with information technology services at SMK Swadhipa 1 Natar using a combination of Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The study was conducted through a quantitative approach by distributing questionnaires to 100 respondents selected using stratified random sampling techniques. The data collected were analyzed to determine the overall satisfaction score and identify factors of information technology services that were a priority for improvement. The results of the CSI analysis showed that the level of student satisfaction with school information technology services was in the good category, with an average score of 82%. Furthermore, the results of the IPA analysis revealed that information technology services such as computer services in the school lab, wifi networks, and school websites consisting of school exam applications, student registration applications and information about the school on the website were in the top priority quadrant because they had a high level of importance but their performance was still low. Based on these results, it can be concluded that although in general students stated that they were quite satisfied with the information technology services available, there were several important aspects, especially technology-based information technology services, that needed more attention from the school. Thus, recommendations for improving technological infrastructure and periodic evaluation of educational information technology services can help SMK Swadhipa 1 Natar in improving the quality of educational services and student satisfaction. 

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

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

Web security is an important aspect in maintaining data integrity and confidentiality in the digital age, where cyber threats are increasingly complex and difficult to detect. This research was conducted because there are still many web systems that are vulnerable to attacks due to weak early detection of security gaps. For this reason, this study implements a combination of Nmap and Metasploit Framework as the main tools in proactively detecting and testing system vulnerabilities. The research method was carried out in three stages, namely data collection by scanning the network using Nmap to identify open ports and services, selecting the appropriate testing tools, and controlled exploitation using Metasploit on the Metasploitable2 test system. The results of the study show that Nmap is capable of mapping the attack surface in detail, while Metasploit can validate the scan results through exploitation of vulnerable services such as vsftpd 2.3.4, which successfully provided root access to the target system. The combination of these two tools has proven to be effective in conducting systematic, fast, and accurate early detection of attacks, so that it can be used as a preventive measure to improve web security from potential cyber threats.

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.

Pada era modern yang ditandai oleh dinamika ekonomi yang terus berkembang dan tantangan keuangan yang semakin kompleks, pengambilan keputusan mengenai kelayakan penerima pinjaman kredit menjadi sangat krusial. Untuk mengatasi kompleksitas ini, pen-erapan Metode Multi-Attribute Utility Theory (MAUT) dan Rank Order Centroid (ROC)  dalam Sistem Pendukung Keputusan dianggap sebagai langkah strategis. MAUT adalah metode perbandingan kuantitatif yang menggabungkan pengukuran biaya risiko dan keuntungan yang berbeda, mengubah berbagai kepentingan menjadi nilai numerik dalam skala 0-1, di mana 0 merepresentasikan nilai terburuk dan 1 nilai terbaik, sedangkan ROC membantu menentukan bobot kriteria secara efisien berdasarkan prioritas. Penelitian ini bertujuan untuk mendiskusikan konsep dasar MAUT dan ROC serta integrasinya dalam mengevaluasi kelayakan penerima pinjaman kredit. Penelitian ini fokus pada kredit sebagai pinjaman dengan perjanjian pembayaran antara peminjam dan pemberi pinjaman. MAUT dan ROC diaplikasikan untuk menilai kelayakan nasabah peminjaman kredit FIF Group dengan melibatkan 8 alternatif dan 5 kriteria. Hasil perhitungan menunjukkan bahwa Teguh Syahputra dengan alternatif A2 mendapatkan nilai tertinggi 0.875, sehingga direkomendasikan sebagai nasabah yang layak menerima Kredit Pinjaman dari perusahaan Finance.

Penelitian ini mengusulkan model waterfall yang digunakan dalam pembuatan sistem pendukung keputusan multi attribute dengan menggunakan metode Analytic Network Process (ANP). Model ini menyediakan kebutuhan pengguna dan kebutuhan perangkat lunak yang meliputi penganalisaan domain informasi, tingkah laku (behaviour), unjuk kerja (function) dan antarmuka. Penggunaan metode waterfall dapat menghasilkan perangkat lunak yang reliable dan bekerja secara efisien. Dari metode waterfall yang diusulkan dapat menghasilkan sebuah analisis kebutuhan atau persyaratan untuk menentukan atau memberikan gambaran umum bagi pengguna agar dapat membantu atau mendukung pencapaian sasaran pembuatan sistem yang meliputi kebutuhan data dan informasi, kebutuhan proses dan fungsi dan kebutuhan antarmuka atau interface.

Academic data increases every year in line with the increase of students. Abundant data store is alsoan abundance of information. Data mining technology is a tool for extracting information on largedatabases and has been widely used in many domains. Predicting student performance (study evaluation) isan activity to determine a future state based on existing data. Data in the field of academic research hasbeen done with various methods and algorithms, but the use of algorithm SLIQ (Supervised Learning InQuest) has not been done.SLIQ is an algorithm developed by the IBM's Quest project team in 1996 for mining large datasets.SLIQ algorithm classify and predict the students performance, beginning with the data cleaning, conductedelection training and testing data. By calculating gini index of each attribute and then selecting thesmallest gini index data table is split according to the criteria until find the same class. From the results ofthe calculation process can produce a set of rules that can be used to predict student performance.From the experiment it can be concluded that the algorithm SLIQ with decision tree technique canbe used as an alternative in designing a system datamining applications. Tests conducted system showedthat the constructed model can be used to predict the performance of new students. The resulting accuracyof the model system in fact has a lower score than the accuracy of other applications that are used as acomparison of Tanagra. Advantages of the proposed system is in its design does not need complexcalculations in obtaining the gini index attributes.