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All Journal Journal on Mathematics Education (JME) Journal on Mathematics Education (JME) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengembangan Rekayasa dan Teknologi JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JSAI (Journal Scientific and Applied Informatics) Progresif: Jurnal Ilmiah Komputer Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat PELS (Procedia of Engineering and Life Science) Jurnal Teknik Indonesia Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Pengabdian Masyarakat Tekno SISFOTENIKA
Johannes Hamonangan Siregar
Universitas Pembangunan Jaya
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Penataan Administrasi GKI Maleo melalui Pendampingan Pengembangan Website Data Keanggotaan: Pengabdian Singadji, Marcello; Hulu, Dalizanolo; Siregar, Johannes Hamonangan; Sadira, Aundrel Aza; Abarua, Nathasa Rowen Frederika; Makarena, Maria Rachel Kesya; Christy, Tegar Surya
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.4363

Abstract

Membership administration is a fundamental component of effective church governance. GKI Maleo faces challenges in managing congregational data due to the continued use of manual record-keeping systems, which leads to risks of data loss, duplicated information, and difficulties in data management. This community service activity aims to assist GKI Maleo in reorganizing its membership administration through the development of a membership data website that includes features for personal data, family information, sacramental records, membership transfers, and service history. The implementation method includes needs analysis, system design, website development, user training, as well as monitoring and evaluation. The results show a significant improvement in data accuracy, efficiency in data retrieval, and the church administrators’ ability to manage congregational information digitally. The development of this website provides a tangible contribution to the modernization of church administration and enhances the quality of services provided to the congregation.
Social Network and Sentiment Analysis for Social CRM Optimalization on Indonesian Digital Recruitment Platform Seibah Humayyah; Johannes Hamonangan Siregar
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2871

Abstract

The rapid growth of digital recruitment platforms in Indonesia has generated a large volume of user content on social media, serving as a vital data source for Social Customer Relationship Management (Social CRM) strategies. Consequently, the strategic insights that can be drawn may be limited. This study applied an integrated analytical approach combining Social Network Analysis (SNA) and lexicon-based sentiment analysis to evaluate public interactions regarding Jobstreet, Glints, and Dealls. The research methodology involved collecting data from platform X (previously known as Twitter) during the period of April 1-30, 2025, which was then analyzed using SNA with Gephi to identify influential actors through centrality metrics, alongside sentiment analysis to measure emotional polarity. The main findings revealed that Jobstreet possessed the healthiest conversational ecosystem, characterized by positive and neutral sentiment from its central actors. Glints exhibited sentiment polarization, and Dealls showed reputational vulnerability due to dominant negative sentiment from its influential users. It was concluded that the integration of these two methods provides a robust framework for designing more responsive and data-driven Social CRM strategies.Keywords: Social Network Analysis; Sentiment Analysis; Social CRM; Digital Recruitment; Lexicon-Based Features.AbstrakPerkembangan pesat platform rekrutmen digital di Indonesia telah menghasilkan volume besar konten pengguna di media sosial, yang menjadi sumber data vital untuk strategi Social Customer Relationship Management (Social CRM). Sehingga hal ini dapat menyebabkan insight strategis yang bisa diambil menjadi terbatas. Penelitian ini menerapkan pendekatan analitis terpadu yang menggabungkan Social Network Analysis (SNA) dan analisis sentimen berbasis leksikon untuk mengevaluasi interaksi publik mengenai Jobstreet, Glints, dan Dealls. Metodologi penelitian melibatkan pengumpulan data dari platform X (sebelumnya dikenal dengan Twitter) selama periode 1-30 April 2025, yang kemudian dianalisis menggunakan SNA dengan Gephi untuk mengidentifikasi aktor berpengaruh melalui metrik sentralitas, serta analisis sentimen untuk mengukur polaritas emosional. Temuan utama mengungkapkan bahwa Jobstreet memiliki ekosistem percakapan paling sehat, ditandai oleh sentimen positif dan netral dari aktor-aktor sentralnya. Sebaliknya, Glints menunjukkan polarisasi sentimen, dan Dealls menunjukkan kerentanan reputasi karena sentimen negatif yang dominan dari para pengguna berpengaruhnya. Disimpulkan bahwa integrasi kedua metode ini menyediakan kerangka kerja yang kuat untuk merancang strategi Social CRM yang lebih responsif dan berbasis data.Kata Kunci: Analisis Jaringan Sosial; Sentimen; Social CRM; Rekrutmen digital; Lexicon-Based Features.
Analisis Malware Archer.exe untuk Identifikasi Potensi Ancaman pada Sistem Operasi Menggunakan Metode Hybrid Analysis Juan Haniful Kahfi; Johannes Hamonangan Siregar
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8269

Abstract

Cybersecurity has become a primary concern in the digital era, particularly regarding malware attacks targeting the Windows operating system. This study aims to analyze a Remote Access Trojan (RAT)-type malware named archer.exe, obtained from the Any.run platform. The method used is hybrid analysis, a combination of static analysis and dynamic analysis, to provide a comprehensive understanding of the malware's structure and behavior. Static analysis results show that archer.exe is a Portable Executable (PE) file with a size of 829.35 KB and employs packing techniques to conceal its payload. Meanwhile, dynamic analysis reveals that the malware modifies system registry keys, spawns child processes such as rundll32.exe and cmd.exe, and establishes a network connection to a Command and Control (C2) server at IP address 192.169.69.26 via the domain dominoduck2101.duckdns.org. These findings indicate that archer.exe poses a high risk of remote system access, data theft, and malicious background activity without user awareness. This study demonstrates that the hybrid analysis method is effective in identifying hidden threats and malicious behavior of RAT-type malware on Windows 10 systems.
Penerapan Sistem Informasi Administrasi Jemaat Berbasis Web Responsif Pada GKI Maleo Raya Maria Rachel Kesya Makarena; Marcello Singadji; Johannes Hamonangan Siregar
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.1048

Abstract

Gereja Kristen Indonesia (GKI) Maleo Raya menghadapi tantangan dalam pengelolaan administrasi jemaat yang masih bergantung pada pencatatan manual, sehingga menimbulkan risiko redundansi data, kehilangan arsip, serta keterlambatan dalam penyusunan laporan. Kegiatan pengabdian masyarakat ini bertujuan merancang dan menerapkan sistem informasi administrasi berbasis web responsif untuk meningkatkan efisiensi, akurasi, dan keamanan pengelolaan data gereja. Pengembangan sistem menggunakan metode Waterfall melalui tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Sistem dilengkapi autentikasi One-Time Password (OTP) guna memperkuat keamanan akses dan mendukung kemudahan penggunaan bagi staf Kantor. Hasil implementasi menunjukkan bahwa aplikasi mampu mengelola database jemaat, mencatat sakramen, serta menghasilkan laporan statistik secara otomatis, dengan peningkatan kualitas layanan administrasi lebih dari 90%. Digitalisasi administrasi yang aman dan mobile-ready ini menjadi langkah strategis bagi gereja dalam mewujudkan pelayanan yang lebih efisien, responsif, dan transparan.
Optimization of Confidentiality, Integrity, and Availability Through Network Segmentation: A Comparative Simulation Study Maria Rachel Kesya Makarena; Jasmine Alya; Aundrel Aza Sadira; Vorian Gustaf Sumampow; Johannes Hamonangan Siregar
Jurnal Sistem Informasi dan Teknologi Informasi Vol. 3 No. 1 (2025): December 2025
Publisher : LP2M Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/justinfo.v3i1.3358

Abstract

Flat network architectures present significant security and efficiency challenges, exposing organizations to heightened risks of cyberattacks, including data breaches and service disruption, while impeding optimal network performance. This inherent vulnerability directly conflicts with the fundamental information security objectives of Confidentiality, Integrity, and Availability (CIA Triad). To address these critical issues, this research investigates the strategic implementation of Virtual Local Area Network (VLAN) segmentation as a mechanism to concurrently enhance security and operational performance. The study employs an experimental, simulation-based methodology using Cisco Packet Tracer software to design and evaluate a functionally segmented network topology. The model incorporates role-based traffic isolation and enforces precise access policies through the application of Extended Access Control Lists (ACLs) on inter-VLAN routing points. Simulation analysis demonstrates that the proposed architecture yields a dual benefit: a 70% reduction in broadcast domain traffic and up to a 50% decrease in communication latency, substantially improving network efficiency and stability. From a security perspective, the logical isolation of segments successfully contained and mitigated prevalent Layer 2 threats, including packet sniffing and ARP poisoning attacks, thereby strengthening data confidentiality and network integrity. The findings conclusively establish that VLAN segmentation, when integrated with granular ACL policies, serves as a foundational and highly effective strategy. It provides a robust technical framework for enforcing the CIA Triad, transforming network infrastructure from a vulnerable, flat entity into a secure, performant, and resilient organizational asset.
Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment Budy Santoso, Cahyono; Effendi, Rufman Iman Akbar; Siregar, Johannes Hamonangan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5186

Abstract

Plant diseases significantly reduce agricultural productivity, especially in developing regions with limited access to early detection tools. This research presents a deep learning-based approach for detecting potato leaf diseases, focusing on Early blight, Late blight, and healthy conditions. A modified ResNet-50 architecture was employed and trained using a publicly available potato leaf image dataset. Preprocessing steps included data augmentation and normalization to enhance model generalization. The model achieved a high accuracy of 99.31%, with precision, recall, and F1-score all exceeding 99%, indicating excellent classification performance. This study introduces a novel approach that improves classification performance through an optimized deep learning architecture, achieving higher accuracy compared to existing models. In addition to enhancing predictive capability, the study also addresses the practical need for accessibility by integrating the trained model into an Android-based mobile application. The application allows users to upload or capture leaf images and receive real-time predictions. The interface was designed for simplicity and usability in field conditions, making it accessible to farmers and agricultural workers. The findings demonstrate that combining deep learning with mobile technology can offer an effective and scalable solution for early disease detection in agriculture. Future work may explore cross-crop adaptability and lightweight model optimization for real-time performance on low-resource devices.
Co-Authors Abarua, Nathasa Rowen Frederika Akoso, Muhammad Daffa Raihan Ali Godjali Ali Godjali, Ali Aundrel Aza Sadira Budy Santoso, Cahyono Chaerul Anwar Chaerul Anwar Christy, Tegar Surya Dalizanolo Hulu Effendi, Rufman Iman Akbar Harahap, Nurul Haifa Jasmine Alya Juan Haniful Kahfi Makarena, Maria Rachel Kesya Marcello Sangaji Marcello Singadji Marcello Singadji Maria Rachel Kesya Makarena Michael Nani Grace Berliana Nur Safitri Wakhyuningsih Nur Safitri Wakhyuningsih, Nur Safitri Nurita, Nurita Rufman Iman Akbar E Sadira, Aundrel Aza Seibah Humayyah Setiawati, Nur Fitri Rosa Surya, Shabrina Naras Vorian Gustaf Sumampow Wiwik Wiyanti Wiwik Wiyanti, Wiwik