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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.

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.

Berkembangnya industri kuliner di Indonesia yang pesat menuntut pengusaha untuk beradaptasi dengan cepat, terutama dalam teknologi dan pemasaran. Karena Masih terlalu banyak pengusaha yang masih mengandalkan metode pemesanan dan promosi manual, maka dari itu penerapan Customer Relationship Management (CRM) menjadi solusi yang efektif untuk menarik pelanggan baru dan mempertahankan loyalitas konsumen. Penelitian ini fokus pada penerapan CRM di Rainbow Cafe, yang bertujuan dalam memikat konsumen baru dan meningkatkan loyalitas konsumen di Rainbow Cafe. Metode penelitian yang digunakan adalah analisis kualitatif melalui wawancara dan survei kepada konsumen serta pengusaha. Hasil penelitian menunjukkan bahwa penerapan CRM sangatlah penting dikarenakan dapat meningkatkan interaksi dengan konsumen, mempercepat proses pemesanan, dan meningkatkan kepuasan konsumen. Kesimpulan dari penelitian ini menegaskan bahwa implementasi CRM tidak hanya penting untuk meningkatkan loyalitas konsumen, tetapi juga sebagai strategi yang krusial dalam menghadapi persaingan di industri kuliner yang semakin ketat dan untuk interaksi dengan konsumen lebih efektif.

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

Pencucian pakaian dan layanan dry cleaning, yang saat ini dapat dijalankan di rumah atau melalui bisnis yang tersedia, menjadi semakin populer. Hal ini telah menyebabkan persaingan sengit di industri ini. Untuk mengatasi persaingan ini, diperlukan usaha untuk memperkenalkan dan mempromosikan layanan pencucian pakaian dengan menggunakan metode Customer Relationship Management (CRM). Tujuan utamanya adalah menarik lebih banyak pelanggan, menjaga hubungan baik dengan pelanggan yang sudah ada, serta efektif mengelola data pelanggan dan keuangan bisnis pencucian pakaian. Penelitian ini bertujuan untuk mengembangkan sistem informasi yang menggunakan metode CRM untuk meningkatkan interaksi yang positif dan nyaman dengan pelanggan. Dalam pengembangan sistem ini, bahasa pemrograman seperti PHP dan HTML digunakan, dengan penggunaan database PostgreSQL dan framework Laravel. Hasilnya adalah sebuah sistem informasi yang kompatibel dengan penerapan metode CRM, memberikan kemudahan bagi pelanggan dalam mencari informasi tentang layanan pencucian pakaian. Saran dari penelitian ini adalah untuk terus meningkatkan penggunaan metode CRM agar interaksi dengan pelanggan dapat lebih aktif dan efektif.

This paper presents n conceptual model of Internet-based business-consumer relationship marketing with a focus on the context, content, and the process of relationship development from the consumer's perspective. The model con be divided into two broad groups. The first group is a set of contextual factors (environment, parties to the relationship, consumption task) that influence relationship development. The second group is the content and process of relationship development that can be viewed via three interrelated conceptual phases: consumer motives to seek interaction, exploration and interaction, and relationship bonding. With the rapid growth of E-commerce and on-line consumer shopping trends, the importance of building and maintaining customer loyalty in electronic marketplaces has come into sharper focus in marketing theory and practice. This paper also to present a conceptual framework of "e-loyalty" and its underlying drives.