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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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Peningkatan Kompetensi Digital Siswa melalui PelatihanPembuatan Website di SMK PGRI 1 Kota Tangerang Lukas Umbu Zogara; 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.
Efficient Waste Classification in Cisadane River Using Vision Transformer and Swin Transformer Architectures Surahmat, Asep; Mutiarawan, Rezza Anugrah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4451

Abstract

The increasing volume of waste in rivers has become a serious environmental problem. This study proposes the implementation of Artificial Intelligence (AI)-based models, specifically Vision Transformer (ViT) and Swin Transformer, for an automatic waste sorting system in the Cisadane River, Tangerang. The dataset used combines public sources and field data, processed through preprocessing and augmentation to improve robustness. Model training was conducted using k-fold cross-validation, pruning, and deployment testing on edge devices to ensure generalization and efficiency. Several architectural innovations were introduced, including Dynamic Patch Size for adapting to various waste shapes and sizes, and Spatial-Aware Attention to enhance focus on waste objects against complex river backgrounds. The evaluation involved a confusion matrix and statistical analysis using a paired t-test to validate the significance of the results. Experimental findings show that Swin Transformer achieved the highest accuracy of 94.2%, surpassing ViT at 91.8%, with precision of 93.5%, recall of 92.7%, and F1-score of 93.1%. Swin Transformer also proved more reliable in dynamic lighting and cluttered environments. This study demonstrates the potential of Transformer-based architectures in automatic waste classification, contributing to smarter and more efficient AI-based environmental management technologies.
CCTV-Based River Waste Detection Using a Hybrid CNN–Graph Attention Network with Spatial–Contextual Feature Learning Surahmat, Asep; Zogara, Lukas Umbu; Muttaqi, Fajar
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.
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.
Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru Di Twitter Rizqi Darmawan; Indra; Asep Surahmat
Jurnal Kajian Ilmiah Vol. 22 No. 2 (2022): May 2022
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/g0dv0y21

Abstract

The significant increase in the number of users has caused public opinion on the Ruang Guru application to be widely spread through social media, especially Twitter. From 15,000 twitter data taken with the keyword Ruang Guru, a total of 2,358 datasets were obtained through the process of handling duplicates. In this study, sentiment analysis was carried out using the Support Vector Machine (SVM) algorithm which was optimized with Particle Swarm Optimization (PSO) then tested using the 10-Fold Cross Validation method which resulted in the highest accuracy rate of 89.20%, while the Support Vector Machine algorithm (SVM) only produces the highest accuracy rate of 88.56%. There is an increase of 0.64% with Particle Swarm Optimization optimization. Sentiment analysis results are positive, with positive results as much as 1463 data or 62.04% and 895 or 37.96% negative sentiment. From the results of this study, it is expected to be a material consideration for Ruang Guru to improve the quality of the service sector found on social media, especially Twitter.
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 Zjevassel Venequenn; 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.v4i1.334

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 Moh. Alfaujianto Mutiarawan, Rezza Anugrah Muttaqi, Fajar Nurul Badriah Pupista, Orinda Rizqi Darmawan Triana Dewi Salma Umiyati, Hesti Yato, Dhimas Buing Rindi Widra Zjevassel Venequenn