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All Journal MIX : Jurnal Ilmiah Manajemen Jurnal Teknoinfo Jurnal Teknik Informatika (JUTIF) Jurnal Sisfotek Global Jurnal Algoritma Jurnal Sistem Informasi dan Teknologi (SINTEK) Journal Information & Computer (JICOM) Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian kepada Masyarakat The Scientific Journal of Information Systems Inovasi Gagasan Abdimas & Kuliah Kerja Nyata Jurnal Pengabdian Masyarakat Tekno Sintek Kuwera
Lukas Umbu Zogara
Universitas Utpadaka Swastika
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Implementation and Analysis of Multiple Interface Policies through System Feature Visibility on Fortigate FG-60F Alfaujianto, Moh; Muttaqi, Fajar; Surahmat, Asep; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.229

Abstract

Fortigate FG-60F is one of the popular firewall appliances utilized by small and medium-scalenetworks in managing security. However, some of the needed features such as multiple interfacepolicies are not displayed by default on the user interface. This study explores the functionality andeffectiveness of enabling system-feature visibility for easier management of inter-interface policies.Employing an experimental approach, the Fortigate FG-60F device was configured to activate thehidden feature, and subsequently, a set of policy rule scenarios with multiple interfaces wereestablished and tested. The results indicate that supporting system-feature visibility enhancessignificantly the administrator's ability to implement more specific traffic policies that arecommensurate with network topology requirements. Moreover, performance analysis showed nonegative impact on device performance after the implementation of multi-interface policy. Thefindings are expected to serve as a valuable reference for network administrators in optimizingFortigate FG-60F security capabilities by leveraging advanced, previously hidden features
Deteksi Penyakit Hawar Daun Bakteri pada Tanaman Padi Menggunakan Algoritma Data Mining Umbu Zogara, Lukas; Rindi Widya Yato, Dhimas Buing
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2917

Abstract

Penyakit tanaman merupakan tantangan serius dalam sektor pertanian, khususnya Hawar Daun Bakteri (HDB) pada padi yang dapat menurunkan produktivitas dan menimbulkan kerugian ekonomi. Penelitian ini bertujuan mengembangkan model klasifikasi HDB berbasis algoritma Gaussian Naive Bayes yang ringan dan dapat diterapkan di wilayah dengan keterbatasan teknologi. Data citra daun padi dikumpulkan dari lapangan, diproses melalui tahap preprocessing dan ekstraksi fitur visual, lalu diklasifikasikan menggunakan Gaussian Naive Bayes dengan evaluasi berbasis akurasi, precision, recall, F1-score, dan AUC. Hasil menunjukkan akurasi 63,07%, precision 56,16%, recall 90,64%, F1-score 69,35%, dan AUC 0,7728. Nilai recall yang tinggi menegaskan kemampuan model dalam mendeteksi sebagian besar daun terinfeksi, sementara AUC menunjukkan performa klasifikasi yang cukup baik. Model ini juga telah diintegrasikan ke dalam prototipe aplikasi web dengan antarmuka pengguna yang sederhana, di mana pengguna dapat mengunggah citra daun untuk dianalisis secara otomatis. Hasil penelitian ini diharapkan dapat digunakan untuk mendukung sistem peringatan dini penyakit tanaman dan membantu petani dalam pengambilan keputusan pengendalian penyakit secara cepat dan efisien. Penelitian ini berkontribusi pada pengembangan sistem deteksi dini berbasis machine learning untuk meningkatkan produktivitas pertanian berkelanjutan.
Peningkatan Kompetensi Digital Siswa melalui PelatihanPembuatan Website di SMK PGRI 1 Kota Tangerang Umbu Zogara, Lukas; Asep Surahmat; Fajar Muttaqi; Moh. Alfaujianto
Jurnal Igakerta Vol. 3 No. 1 (2026): Jurnal Igakerta
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/b4akhz97

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi digital siswa Sekolah Menengah Kejuruan (SMK) melalui pelatihan pembuatan website menggunakan bahasa pemrograman Python. Kegiatan dilaksanakan di SMK PGRI 1 Kota Tangerang dengan melibatkan 40 siswa jurusan Teknik Komputer dan Informatika. Metode pelaksanaan meliputi ceramah interaktif, demonstrasi, praktik langsung menggunakan framework Flask, serta pendampingan bertahap. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur peningkatan kemampuan peserta. Hasil menunjukkan adanya peningkatan rata-rata sebesar 43% pada pemahaman konsep dan keterampilan teknis siswa. Hal ini membuktikan bahwa metode pelatihan berbasis praktik efektif dalam meningkatkan kemampuan berpikir logis, analitis, dan pemecahan masalah. Secara keseluruhan, kegiatan ini berkontribusi dalam meningkatkan literasi digital siswa serta kesiapan mereka menghadapi tuntutan dunia industri dan perkembangan teknologi.
Kontribusi Akademisi dalam Workshop Pendidikan Pemilih KPU Kota Tangerang: Penguatan Literasi Pemilih Pemula dan Penyandang Disabilitas Lukas Umbu Zogara; Tb. Yudi Muhtadi; Syukron Makmun
Jurnal Igakerta Vol. 2 No. 4 (2025): Jurnal Igakerta
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/g85gas88

Abstract

Kegiatan workshop pendidikan pemilih yang diselenggarakan KPU Kota Tangerang menjadi upayapenting dalam meningkatkan pemahaman masyarakat mengenai proses demokrasi, terutama bagipemilih pemula dan penyandang disabilitas yang masih membutuhkan pendekatan edukatif yanglebih inklusif. Partisipasi akademisi dalam kegiatan ini dilakukan untuk memberikan penguatanliterasi demokrasi melalui perspektif ilmiah serta mendukung penyampaian materi yang lebihsistematis dan mudah diakses. Metode pengabdian mencakup observasi partisipatif, diskusi kelompokterarah, dan analisis kebutuhan peserta. Hasil kegiatan menunjukkan bahwa pemilih pemula lebihresponsif terhadap media digital interaktif, sedangkan peserta disabilitas memerlukan materi denganaksesibilitas visual dan komunikasi yang lebih adaptif. Kontribusi akademisi membantu merumuskanrekomendasi sosialisasi yang lebih efektif dan ramah inklusi. Kesimpulannya, keterlibatan akademisimemperkuat kualitas pendidikan pemilih dan mendorong peningkatan literasi demokrasi di KotaTangerang
Strategic Role of Social Media in Enhancing Customer Engagement in Higher Education Hesti Umiyati; Asep Surahmat; Dhimas Tribuana; Lukas Umbu Zogara
MIX: JURNAL ILMIAH MANAJEMEN Vol 16, No 1 (2026): MIX : Jurnal Ilmiah Manajemen
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jurnal_mix.2026.v16i1.012

Abstract

Objectives: The growth of social media has changed the communication space for colleges and universities, especially in conversations with prospective or enrolled students. To fill gap and also provide empirical basis, this study aims to investigate the strategic contribution of social media in developing customer engagement in Indonesian higher education setting focusing on content quality, engagement strategy, and platform diversity.Methodology: The research was carried out using the quantitative method of the descriptive type. Initial data was collected through an online survey that was sent to 150 strategically chosen participants who create content centred on university through platforms including Instagram and TikTok. The analysis was conducted with Partial Least Squares Structural Equation Modeling (PLS-SEM).Finding: From the findings of this research, three main constructs that underpin the impact of a socialmedia strategy on customer engagement were discovered: diversity; interaction and content quality. The effective communication, right choice of the platform and strategic methods of communication are important in maintaining the engaging.Conclusion: This analyses offer institutions a perspective of how to further develop the presence in social media, and is an effort to understand how students can be communicated with using digital channels within higher education. It highlights the necessity for academic programs to move from mere content delivery in a digital environment toward something more meaningful and engaging. It is recommended for future studies to use mixed-method design and compare the results, which can provide a full picture about such contexts.
Comparative Analysis of Cloud Service Models for Professional Use: IaaS, PaaS, and SaaS Alfaujianto, Moh; Nugraha, Fahmi Rizky; Muttaqi, Fajar; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.310

Abstract

This study aims to conduct a structured comparative analysis of cloud computing service models-Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)-for professional use across multiple sectors. A quantitative comparative approach was employed using data collected from scientific literature and semi-structured interviews involving 15 professionals from education, business, and technology sectors. Each model was evaluated based on five parameters: flexibility, scalability, cost efficiency, user control, and sector relevance using a Likert scale (1–5). The results indicate that IaaS achieved the highest score in flexibility (5.0) and user control (5.0), PaaS showed balanced performance across development-related parameters (average score 4.2), while SaaS demonstrated the highest cost efficiency (5.0). These findings highlight that no single model is universally superior, and selection should be aligned with organizational priorities. This study contributes by providing a parameter-based quantitative comparison framework to support decision-making in cloud service adoption.
Machine Learning for Predicting Property Purchase Behavior: A Systematic Literature Review Lukas Umbu Zogara; Asep Surahmat
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.329

Abstract

This study aims to examine the application of machine learning algorithms in predicting property purchase behavior based on consumer data. The main problem addressed is the limited use of intelligent data analysis in understanding consumer behavior in the Indonesian property sector, despite increasing market data availability. This research employs a systematic literature review approach by analyzing studies published in the last five years, focusing on classification algorithms such as Decision Tree, Random Forest, and Support Vector Machine (SVM). The analysis includes data collection, evaluation, and synthesis of selected studies. The results indicate that algorithm performance varies depending on data characteristics and application context. Random Forest tends to show strong performance in terms of accuracy and robustness, while Decision Tree and SVM also demonstrate competitive results in certain scenarios. These findings reflect general trends rather than definitive conclusions. Key factors influencing property purchase decisions include location, price, and developer reputation. In conclusion, machine learning has significant potential to support data-driven decision-making in the property sector. Future research should integrate real-time and more diverse data to improve predictive model accuracy
CCTV-Based River Waste Detection Using a Hybrid CNN–Graph Attention Network with Spatial–Contextual Feature Learning Asep Surahmat; Lukas Umbu Zogara; Fajar Muttaqi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5544

Abstract

River waste accumulation has become a serious environmental problem in urban areas, particularly in highly polluted rivers such as the Angke River in Tangerang, where floating waste disrupts ecological balance and increases flood risk. Conventional computer vision–based detection methods often fail under dynamic river conditions due to water surface reflections, turbulence, occlusion, and visually ambiguous debris. This study aims to improve the accuracy and robustness of river waste detection by proposing a hybrid deep learning framework that integrates convolutional and graph-based spatial–contextual reasoning. The proposed method utilizes a ResNet50 backbone for feature extraction from CCTV imagery, followed by spatial graph construction that models adjacency relationships between image regions. A Graph Attention Network (GAT) is then applied to capture contextual dependencies and refine feature representations prior to classification. Unlike conventional CNN-only or YOLO-based detectors that rely primarily on local visual cues and bounding-box representations, the proposed approach explicitly models spatial–contextual relationships between image regions through graph-based attention mechanisms. Experiments were conducted on 4,200 CCTV image frames collected from the Angke River under varying environmental conditions. The proposed model achieved an accuracy of 92.4%, precision of 91.1%, recall of 93.2%, F1-score of 91.9%, and a mean Average Precision (mAP) of 0.78, outperforming CNN-only and YOLO-based baseline models. These findings highlight the contribution of graph-enhanced visual reasoning to the fields of Computer Vision and Intelligent Surveillance, particularly for real-time environmental monitoring systems operating in complex and dynamic visual environments.
Edukasi Kecerdasan Buatan Bidang Jaringan Komputer untuk Meningkatkan Kompetensi Digital Siswa SMK Lukas Umbu Zogara; Hesti Umiyati; Rusdah; Maulana Agung Saputro
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 7 No 1 (2026): Juni 2026 In Progress
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v7i1.7282

Abstract

Perkembangan teknologi Kecerdasan Buatan (Artificial Intelligence/AI) semakin berpengaruh dalam pengelolaan jaringan komputer modern. Namun, siswa SMK jurusan Teknik Komputer dan Jaringan (TKJ) masih memiliki pemahaman yang terbatas terhadap penerapan AI di bidang tersebut. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi dan kompetensi digital siswa melalui edukasi tentang konsep dasar serta penerapan AI dalam jaringan komputer. Metode pelaksanaan dilakukan melalui seminar edukatif dan diskusi interaktif yang diikuti oleh 40 siswa SMK Tunas Harapan. Materi mencakup pengenalan konsep AI, peran AI dalam otomatisasi jaringan, serta contoh penerapannya di industri. Data diperoleh melalui observasi dan kuesioner sebelum dan sesudah kegiatan. Hasil menunjukkan peningkatan pemahaman siswa terhadap konsep AI sebesar 78%, serta peningkatan minat untuk mendalami penerapan AI dalam bidang jaringan komputer. Kegiatan ini berhasil menumbuhkan kesadaran pentingnya penguasaan teknologi cerdas di kalangan siswa SMK dan memperkuat kesiapan mereka menghadapi tantangan dunia kerja berbasis digital. Diperlukan kegiatan lanjutan berupa pelatihan praktik agar pemahaman siswa semakin aplikatif dan berorientasi industri.
Co-Authors Adiiba Dwikanthi Ahmad Sururi Alfaujianto, Moh Alfaujianto, Moh. Ameliana Ameliana Asep Surahmat Dai Payon Binti Gabriel, Cecilia Darul Mukminin Dhimas Tribuana Fajar Muttaqi Fajar Muttaqi Fajar Muttaqi Grace Limiko Hasyanta, Gopas Leny Tritanto Ningrum Maulana Agung Saputro Moh Alfaujianto Moh. Alfaujianto Muttaqi, Fajar Ningrum, Leny Nugraha, Fahmi Rizky Pupista, Orinda Rindi Widya Yato, Dhimas Buing Rusdah Rusdah Sari, Santi Purnama Suharmat, Asep Surahmat, Asep Sutrisno, Gono Syukron Makmun Tb. Yudi Muhtadi Tehupuring, Ronald Tobing, Suasana Hisanty Umiyati, Hesti Yato, Dhimas Buing Rindi Widra