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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer MIX : Jurnal Ilmiah Manajemen Format : Jurnal Imiah Teknik Informatika bit-Tech Jurnal Teknik Informatika (JUTIF) Jurnal Kajian Ilmiah Nusantara Hasana Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Gema Lingkungan Kesehatan 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 Fortech : Journal of Information Technology
Surahmat, Asep
Universitas Utpadaka Swastika
Religion Agriculture, Biological Sciences & Forestry 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 Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Pemanfaatan Generative Artificial Intelligence (GenAI) untuk Prediksi dan Analisis Bencana Alam Arief Wibowo; Asep Surahmat
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Disaster prediction and analysis are crucial components in mitigating the impacts of natural hazards such as floods, earthquakes, and landslides. Conventional systems often rely on deterministic models and limited historical data, which restrict their accuracy and adaptability to dynamic environmental changes. The emergence of Generative Artificial Intelligence (GenAI), particularly models based on deep learning and generative architectures such as Generative Adversarial Networks (GANs) and Diffusion Models, introduces new opportunities for synthetic data generation and predictive simulation. This study aims to explore the implementation of GenAI in disaster prediction and analysis by reviewing recent literature and practical applications in Indonesia. The proposed framework integrates multimodal data—including meteorological, seismic, and remote sensing data—into generative models to simulate disaster scenarios and improve early warning systems. The results indicate that GenAI can enhance data diversity, reduce bias in model training, and support real-time decision-making in disaster management. The study concludes that GenAI has strong potential to revolutionize disaster analytics and strengthen climate resilience through adaptive, data-driven insights. Thus, the output of this research is conceptual and focuses on designing a framework, while empirical testing forms the basis for further research development.
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.
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.
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
Implementation of the Naive Bayes Algorithm for Classification of Public Service Complaints in E-Government at Kunciran Indah Tangerang Venequenn, Zjevassel; Surahmat, Asep
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.v4i1.334

Abstract

The implementation of e-government at the local government level is essential for improving the quality and efficiency of public services. However, the management of public service complaints at Kelurahan Kunciran Indah, Tangerang, is still conducted manually, leading to delays and inefficiencies in handling citizen reports. This study aims to implement the Naive Bayes algorithm to automatically classify public service complaints within an e-government system. A quantitative computational approach was employed using a dataset of 50 complaint records categorized into four classes: infrastructure, cleanliness, service, and administration. Data preprocessing techniques, including case folding, tokenization, and stopword removal, were applied prior to model training. The Naive Bayes classifier was used to build a classification model and evaluate its performance. The results show that the proposed model achieved an accuracy of 90%, demonstrating good performance in classifying text-based complaints across all categories. This indicates that the Naive Bayes algorithm is effective for supporting automated complaint classification in local government services. The implementation of this system can improve service efficiency, accelerate response time, and assist decision-making processes. Nevertheless, the study is limited by the relatively small dataset, and future research is recommended to utilize larger and more diverse data to enhance model performance.
Co-Authors Alfaujianto, Moh Alfaujianto, Moh. Arief Wibowo Darmawan, Rizqi Dhimas Buing Rindi Widra Yato Dhimas Tribuana Didi Kurnaedi Erna Oktora Fahmi Nugraha, Fahmi Fajar Muttaqi Fajar Muttaqi Grace Limiko Hasyanta, Gopas Indra Indra Indra Lukas Umbu Zogara Manorang Sihotang Mirza Sutrisno Moh Alfaujianto Moh. Alfaujianto Mutiarawan, Rezza Anugrah Muttaqi, Fajar Nurul Badriah Pupista, Orinda Salma, Triana Dewi Umiyati, Hesti Venequenn, Zjevassel Yato, Dhimas Buing Rindi Widra