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All Journal Dinasti International Journal of Education Management and Social Science Gema Lingkungan Kesehatan Jurnal Pengabdian kepada Masyarakat The Scientific Journal of Information Systems
Yato, Dhimas Buing Rindi Widra
Universitas Mercu Buana, Jakarta, Indonesia
Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Environmental Science Health Professions Immunology & microbiology Public Health Social Sciences

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PELATIHAN PENGGUNAAN MEDIA SOSIAL DAN PENULISAN BLOG UNTUK MENINGKATKAN KEMAMPUAN MENULIS DOSEN TETAP Yato, Dhimas Buing Rindi Widra
Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2024): JURNAL PENGABDIAN KEPADA MASYARAKAT (BHAKTIMAS)
Publisher : UNIT PENGABDIAN KEPADA MASYARAKAT - UTPAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/bhaktimas.v2i2.108

Abstract

Peningkatan literasi, kepercayaan diri, serta pengalaman dapat meningkatkan kualitas tulisan dosen pada artikel ilmiah. Pelatihan penggunaan media sosial dan penulisan blog dilakukan untuk memberikan pengalaman kepada dosen-dosen Universitas Utpadaka Swatika. Pelatihan diberikan kepada dosen tetap yang berjumlah 54 orang. Dari hasil pelatihan, dapat dilihat bahwa peserta pelatihan mulai mampu menggunakan media sosial untuk melakukan publikasi, serta menerbitkan tulisan pada blog universitas. Hal yang perlu dilakukan selanjutnya adalah upaya agar kegiatan menulis ini tidak berhenti, dan dapat berlanjut agar dapat meningkatkan kemampuan dosen dalam menulis artikel ilmiah, serta memberikan kehadiran digital yang berkesan.
WEB BASED INTERNAL QUALITY MANAGEMENT INFORMATION SYSTEM A DESIGN AND PROTOTYPING Yato, Dhimas Buing Rindi Widra; Hidayat, Zulham
Scientific Journal of Information System Vol. 2 No. 2 (2024): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v2i2.142

Abstract

Improving the quality of education in higher education institutions is crucial to support the development of human resources and the progress of the nation. Utpadaka Swastika University, as one of the higher education institutions in Indonesia, is committed to producing high-quality and competitive graduates. To ensure this quality, an effective and efficient internal quality assurance system (SPMI) is necessary. This research aims to design and build an internal quality assurance system based on the web using PHP and MySQL at Utpadaka Swastika University with the prototyping method. Software testing is conducted through Black Box Testing. This internal quality assurance system aims to ensure the quality of higher education provided by universities through the implementation of the Tridharma of Higher Education, in order to realize the vision and meet the needs of stakeholders. The achievement of quality assurance goals through the internal quality assurance system will be accredited through external quality assurance systems. This system can assist in presenting document information accessible to program heads, leaders, auditors, administrators, and quality assurance body (LPM) leaders. It facilitates program heads in the process of collecting and submitting documents for evaluation and audit instruments, allowing the LPM to directly identify issues and quality standards in the internal quality assurance process. This system is expected to be used as a reference for further development of an Internal Quality Assurance System so that the system can have better features.
Knowledge Management Strategy to Improve Lecturer Research Performance at College of Economics Yato, Dhimas Buing Rindi Widra; Singmin Johanes Lo
Dinasti International Journal of Education Management And Social Science Vol. 4 No. 4 (2023): Dinasti International Journal of Education Management and Social Science (April
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/dijemss.v4i4.1808

Abstract

Knowledge management is one of the main methods of a higher education institution to develop knowledge-based innovation. During the COVID-19 Pandemic, research performance in the social and economic fields in Indonesia decreased by 433%. One of the Colleges of Economics (STIE) in Tangerang is currently making changes to improve the research performance of lecturers who have various constraints due to the competence of existing lecturers, the current wage strategy, and low motivation. This study aims to find out how knowledge management processes consisting of knowledge discovery, knowledge capture, knowledge sharing, and knowledge application are used for lecturers’ research; the role of the knowledge management component consisting of people, process, and technology in each process; constraints in the knowledge management process; as well as knowledge management strategies are the things that can be implemented by the College of Economics. The research method used is qualitative research using the NVivo software analysis tool for Windows. The results of this study are in the form of a four-stage strategy consisting of the formulation, implementation, monitoring, and evaluation stages that can be implemented by the College of Economics to support efforts to improve lecturer research performance. The formulation stage includes guaranteeing the availability and clarity of policies, road maps and research SOPs, as well as a reward & punishment system. The implementation stage is to provide a tool in the form of a data repository for lecturers to carry out knowledge management processes, and ensure that leaders set an example by playing an active role in using the tool. The monitoring and evaluation phase is used to ensure that ongoing implementation does not deviate from the plan, and to ensure that the strategies provided are useful for improving lecturer research.
HOW TECHNOLOGY AFFECTING RESEARCHERS IN THE ERA OF GENERATIVE AI Yato, Dhimas Buing Rindi Widra; Zogara, Lukas Umbu; Suharmat, Asep
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.175

Abstract

In the rapidly evolving research landscape, generative AI is emerging as a transformative force. This study explores the multifaceted impacts of generative AI on researchers across various disciplines. By automating routine tasks, enhancing data analysis, and generating novel hypotheses, AI tools are significantly boosting productivity and opening new avenues for innovation. However, these advancements also present challenges, including ethical considerations, the need for transparency, and the potential for bias in AI-generated results. Moreover, the integration of AI into research demands the development of new skill sets, presenting both opportunities and risks for researchers. Drawing on recent studies, this article provides a comprehensive overview of how generative AI is reshaping the research landscape and highlights the critical dynamics researchers must navigate in this new era.
AI-Based Waste Detection for Water Quality Monitoring in the Cisadane River: A Deep Learning Approach Surahmat, Asep; Yato, Dhimas Buing Rindi Widra
Gema Lingkungan Kesehatan Vol. 23 No. 3 (2025): Gema Lingkungan Kesehatan
Publisher : Poltekkes Kemenkes Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36568/gelinkes.v23i3.270

Abstract

The rapid accumulation of waste in Indonesia's rivers, particularly the Cisadane River, seriously threatens water quality, ecosystem health, and public well-being. Traditional waste monitoring methods are inefficient and often fail to deliver timely data for effective interventions. This study addresses this gap by proposing an AI-based waste detection system for real-time water quality monitoring using deep learning techniques. A hybrid model integrating Convolutional Neural Network (CNN) and You Only Look Once version 7 (YOLO v7) was developed and tested on a dataset of 10,000 annotated images—60% organic and 40% inorganic waste—collected from the Cisadane River. The CNN model achieved a classification accuracy of 87%, a precision of 84%, a recall of 86%, and an F1-score of 85%. The YOLO v7 model demonstrated % detection accuracy of 82% with a processing speed of 20 frames per second. While mean Average Precision (mAP) was not directly calculated, the model's performance across key metrics supports its real-time applicability. This research offers a scalable and cost-effective approach for river waste monitoring and highlights the potential of AI in supporting sustainable environmental management in Indonesia.
Co-Authors Hidayat, Zulham Lukas Umbu Zogara Singmin Johanes Lo Suharmat, Asep Surahmat, Asep