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Mosque-based community economic empowerment activities have significant potential to improve community welfare, particularly for small businesses in the mosque's immediate vicinity. However, implementation is still largely conventional and lacks a data-driven approach, resulting in suboptimal beneficiary identification and activity evaluation. This community service initiative aims to promote a data-driven approach to mosque-based community economic empowerment through sharia bazaar activities. Implementation methods include initial observation, outreach and education for mosque managers and business owners, technical assistance for sharia entrepreneurship, and activity evaluation. The results demonstrate an increased public understanding of the importance of data use in determining beneficiaries, managing bazaar activities, and developing businesses based on sharia economic principles. The outcomes of this initiative include improved data management literacy, a simple data collection format for sharia bazaar activities, and recommendations for developing a mosque-based data collection system. It is hoped that this initiative will be the first step in building a sustainable, transparent, and data-driven community economic empowerment model within the mosque environment.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

This research aims to design and build a letter archive information system at the KOFIPINDO Law Office to improve the effectiveness and efficiency of document management. The manual filing system that has been used so far poses various obstacles, such as search delays, the risk of losing documents, and low storage accuracy. To overcome these problems, this study applies the Waterfall System Development Life Cycle (SDLC) model in the process of designing and building the system. Web-based technologies used include PHP, HTML, CSS, Bootstrap, and MySQL. The results of the study show that the developed letter archive information system is able to simplify the process of storing, searching, and managing incoming and outgoing letters in a faster, structured, and safer manner. The implementation of this system not only improves administrative performance, but also strengthens accountability and supports the need for professional legal documentation within the KOFIPINDO Law Office. Thus, this web-based mail archive information system can be a strategic solution in modernizing legal document management.

Infeksi Saluran Pernapasan Akut (ISPA) merupakan masalah kesehatan utama di Kabupaten Tegal, terutama menyerang anak-anak dan lansia, dengan 25.273 kasus tercatat pada Januari–Juni 2025. Faktor seperti kepadatan penduduk, polusi udara, dan sanitasi buruk mempersulit analisis konvensional dalam mengidentifikasi wilayah berisiko tinggi. Penelitian ini bertujuan mengembangkan Sistem Informasi Geografis (SIG) berbasis web untuk memetakan dan memantau kasus ISPA secara akurat menggunakan pendekatan spasial. Metode Waterfall digunakan dalam pengembangan sistem, meliputi analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Data spasial (koordinat lokasi) dan non-spasial (usia, jenis kelamin, jumlah kasus) dikumpulkan dari Dinas Kesehatan dan dianalisis menggunakan teknik kepadatan titik dan klaster untuk mengidentifikasi pola sebaran. Sistem ini dibangun dengan framework Laravel sebagai backend dan Leaflet.js untuk visualisasi peta interaktif. Hasilnya, SIG ini mampu memvisualisasikan sebaran kasus ISPA per kecamatan, mendukung Dinas Kesehatan dalam mengalokasikan sumber daya medis secara efisien dan mengidentifikasi wilayah prioritas seperti Dukuhwaru dan Tarub. Bagi masyarakat, sistem ini meningkatkan akses informasi digital, mendorong kesadaran pencegahan, dan mendukung edukasi kesehatan. Sistem ini terbukti efektif sebagai alat strategis untuk pengambilan keputusan berbasis data, mempercepat respons terhadap ISPA, dan menjadi fondasi untuk pengembangan SIG dalam memantau penyakit lain di masa depan.

Kecelakaan lalu lintas di Kabupaten Brebes merupakan masalah kritis karena tingginya frekuensi insiden yang terjadi di wilayah tersebut. Penelitian ini bertujuan untuk menentukan area yang rentan terhadap kecelakaan dengan menggunakan algoritma K-Means Clustering , yang mendukung proses pengambilan keputusan berbasis data. Isu utama yang dieksplorasi dalam penelitian ini adalah bagaimana algoritma K-Means dapat diimplementasikan untuk mengelompokkan zona rawan kecelakaan dan meningkatkan kesadaran masyarakat terhadap keselamatan jalan. Metodologi yang digunakan meliputi pengumpulan data melalui tinjauan pustaka, observasi langsung, dan wawancara, yang dilanjutkan dengan penggunaan algoritma K-Means untuk mengklasifikasikan data kecelakaan berdasarkan jumlah kejadian, korban jiwa, dan cedera. Temuan menunjukkan bahwa algoritma K-Means secara efektif mengelompokkan lokasi rawan kecelakaan ke dalam tiga tingkat risiko yang berbeda: tinggi, sedang, dan rendah. Dengan demikian, informasi yang terklasifikasi ini dapat membantu otoritas terkait dalam meningkatkan langkah-langkah keselamatan lalu lintas dan mengedukasi masyarakat tentang area berisiko tinggi. Hasil penelitian ini diharapkan dapat berkontribusi pada pengembangan kebijakan keselamatan lalu lintas yang lebih terinformasi dan strategis di Kabupaten Brebes.

Pendistribusian bantuan yang efisien menjadi tantangan utama dalam proses penyaluran. Penelitian ini bertujuan untuk merancang sistem cerdas yang mampu memetakan dan menyalurkan bantuan secara optimal dengan pendekatan algoritmik. Metodologi yang digunakan meliputi pemodelan sistem dengan menggunakan Unified Modeling Language (UML), perancangan struktur basis data relasional, dan perancangan algoritma penyaluran berdasarkan kriteria prioritas dan efisiensi logistik. UML digunakan untuk menggambarkan arsitektur sistem secara visual, meliputi use case, class, dan diagram aktivitas. Perancangan basis data dilakukan untuk memastikan integritas data dan kemudahan pengelolaan informasi bantuan, lokasi, dan kebutuhan penerima. Algoritma yang dikembangkan memanfaatkan pendekatan heuristik untuk menentukan rute penyaluran dan prioritas penerima berdasarkan parameter lokasi geografis. Hasil penelitian ini berupa prototipe model konseptual yang dapat digunakan sebagai dasar pengembangan sistem cerdas berbasis teknologi untuk mendukung proses penyaluran bantuan yang adaptif dan responsif.

Handwashing with soap (HWWS) is one of the simplest, most effective, and cost-efficient behaviors for preventing infectious diseases. However, its practice remains suboptimal at the household level in Indonesia. This research aims to analyze HWWS behavior using community questionnaire data as a basis for developing health promotion strategies through the Problem Solving Cycle (PSC) approach. This descriptive quantitative study was conducted among 50 respondents using a closed-ended questionnaire covering the knowledge, attitude, and practice dimensions of HWWS. The results showed that although respondents had high awareness and positive attitudes toward HWWS, their practices remained inconsistent due to habitual and infrastructural barriers. These findings highlight the need for integrated interventions combining direct education, social media campaigns, and improved access to handwashing facilities. This study contributes to evidence-based, participatory, and contextually adapted health promotion strategies aimed at achieving sustainable behavioral change.

Penelitian ini mengungkapkan sejumlah contoh nyata pelanggaran privasi konsumen di Indonesia akibat pemanfaatan Big Data. Pelanggaran ini meliputi kebocoran data pribadi di platform e-commerce, pengumpulan data tanpa persetujuan melalui aplikasi mobile, pemasaran berbasis data yang agresif, penjualan data kepada pihak ketiga tanpa izin, serta kebijakan privasi yang tidak transparan. Kasus-kasus tersebut menyoroti kerentanan data pribadi konsumen terhadap serangan siber dan penyalahgunaan, serta perlunya regulasi yang lebih ketat dalam perlindungan data. Sebagai respons terhadap tantangan ini, perusahaan di Indonesia mulai mengadopsi langkah-langkah strategis, termasuk implementasi kebijakan perlindungan data yang transparan, peningkatan kesadaran konsumen melalui edukasi, penggunaan teknologi keamanan mutakhir, dan kepatuhan terhadap regulasi, seperti Undang-Undang Perlindungan Data Pribadi. Selain itu, perusahaan bekerja sama dengan pihak ketiga dan melakukan audit risiko secara berkala untuk memperbaiki sistem keamanan mereka. Langkah-langkah ini bertujuan untuk meningkatkan kepercayaan konsumen dan memastikan perlindungan privasi dalam era digital. Penelitian ini menegaskan pentingnya kolaborasi antara pemerintah, perusahaan, dan konsumen dalam menciptakan ekosistem data yang aman dan bertanggung jawab di Indonesia.

The transformation of Human Resource Development (HRD) through a data-driven decision-making system has become a strategic necessity for organizations aiming to improve performance and competitiveness. This study employs a qualitative descriptive approach by reviewing several relevant journals that discuss the integration of data analytics into HRD functions. The findings indicate that data-driven decision systems enable organizations to accurately identify training needs, predict employee performance, and design development programs that align with evolving organizational demands. Furthermore, the use of data reduces subjective bias in evaluation processes and enhances the objectivity of performance assessments. This transformation also strengthens the strategic role of HRD by providing timely and evidence-based recommendations that support organizational goals. However, successful implementation requires technological readiness, digital competencies among HR personnel, and an organizational culture that encourages data utilization. Overall, the literature review shows that adopting data-driven decision approaches significantly contributes to improving HRD effectiveness and organizational performance.

Fever is the body's response to infection or inflammation, which often indicates a certain medical condition. One common treatment for fever is the use of antipyretic drugs. The purpose of this study was to evaluate the effectiveness of antipyretic drugs in lowering body temperature in adult patients with fever. The method used was a clinical trial with a randomized controlled trial (RCT) design, involving 100 adult patients with fever ≥38°C. Patients were divided into two groups: the group receiving antipyretic drugs (paracetamol) and the placebo group. Body temperature measurements were taken every 2 hours for 24 hours after drug administration. The results showed that the group receiving antipyretic drugs experienced a significant decrease in body temperature compared to the placebo group (p <0.05). The side effects that appeared were relatively mild and not dangerous..

Databases play an essential role in modern information systems because they enable the efficient storage, management, and retrieval of structured data. This study focuses on the implementation of a simple database using two relational database management systems (RDBMS), namely MySQL and PostgreSQL. The research stages are carried out through requirements analysis, designing entity-relationship diagrams, implementing tables and their relationships, and testing the system to assess functionality. The case study used is student data recording, where both RDBMSs are tested through a series of basic CRUD (Create, Read, Update, Delete) operations. The results show that both MySQL and PostgreSQL are capable of carrying out these operations effectively while maintaining data integrity and consistency. However, PostgreSQL offers additional advantages due to its support for more complex data types, a better level of compliance with SQL standards, and advanced features that support the needs of large-scale application development. Conversely, MySQL remains superior in terms of ease of use and application to simpler needs, making it more practical for small projects or applications with standard workloads. Based on these findings, it can be concluded that both RDBMSs are equally suitable for use, but the ideal choice depends heavily on the context of the needs; PostgreSQL is more suitable for applications that demand high scalability and size, while MySQL remains relevant for lightweight and efficient implementations.

In the digital era, universities face challenges in enhancing brand awareness amidst increasing competition. Utilizing digital analytics in data-driven marketing communication strategies becomes crucial to achieve this goal. This study aims to analyze the role of digital analytics in designing effective marketing communication strategies to boost university brand awareness. The research method employed is a literature review, gathering and analyzing various sources related to this topic. The findings indicate that integrating digital analytics enables universities to gain deep insights into audience behavior and preferences, allowing for the crafting of more targeted marketing messages. Additionally, the use of digital analytics assists in measuring the effectiveness of marketing campaigns in real-time, facilitating more responsive strategy adjustments to market dynamics. The implications of this study highlight the importance for universities to adopt digital analytics technology in their marketing communication efforts to enhance brand awareness and competitiveness in the digital age.