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p-Index From 2021 - 2026
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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
Afri Yudha
Darma Persada University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences Other

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Optimizing Google Apps in Improving the Skills and Productivity of the Young Generation of Bojong Village Pondok Kelapa Yan Sofyan; Afri Yudha; Suzuki Syofian; Bagus Tri Mahardika
JEPTIRA Vol 2 No 2 (2024)
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jep.v2i2.68

Abstract

Effective and efficient administrative and office management remains a primary challenge for organizations in the digital era. This community service activity aims to enhance the understanding and skills of Bojong youth in utilizing Google applications (Google Drive, Google Docs, Google Sheets, and Google Forms) as solutions for administrative management. The methods applied include theoretical training, hands-on practice, and evaluation of application implementation in daily workflows. The results indicate that using Google applications accelerates data processing by up to 30%, reduces paper usage by 40%, and improves collaboration and communication effectiveness among participants. Additionally, this training fosters a transition toward a digital work culture that is adaptive and responsive to technological challenges. Thus, using Google applications has proven to be a practical and relevant solution for supporting better organizational administrative governance.
Advanced Prompting Techniques for Artificial Intelligence-Based Learning Innovation Yan Sofyan Andhana Saputra; Adam Arif Budiman; Aji Setiawan; Afri Yudha; Ade Supriatna; Ario Kurnianto; Asyari Dariyus
JEPTIRA Vol 3 No 1 (2025)
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jeptira.v3i1.98

Abstract

This community service program was designed to strengthen the capacity of teachers and lecturers in utilizing advanced prompting techniques based on Artificial Intelligence (AI) to support instructional innovation. The focus of the training was on two effective methods Chain of Thought (CoT) and Role Prompting which enhance human-AI interaction in educational contexts. The activity was conducted through face-to-face workshops involving 25 participants from various educational institutions, combining theoretical explanations, hands-on practice, and case-based discussions. Participants learned how to construct structured and contextual prompts for teaching applications such as lesson planning, explanation of concepts, and simulation-based learning. Evaluation results showed a significant improvement in participants’ understanding and ability to apply prompt engineering strategies, as reflected in both assessment scores and the quality of practical outputs. The program also contributed to raising awareness about ethical AI usage in education and emphasized the role of digital literacy in enabling educators to adapt to the demands of digital transformation.
Implementation of Support Vector Machine and Multilayer Perceptron Algorithms for Patient Diagnosis Based on Patient Profile and Complaints at Jatibening Public Health Center Romanda Ilham; Afri Yudha
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.104

Abstract

Community health centers (Puskesmas) are primary healthcare institutions that play a crucial role in providing services to the community, especially in areas with limited access. However, the disease identification process at the Jatibening Community Health Center still uses traditional methods that are time-consuming and potentially biased. This study aims to create a disease prediction system for patients using the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) machine learning algorithms that utilize data from patient profiles and complaints. The methods used in this study include collecting information from patient medical records, data processing, training SVM and MLP models, and assessing the model's accuracy level. Test results show that the MLP algorithm achieves 100% accuracy, while the SVM also demonstrates 100% accuracy in predicting the likelihood of a patient's disease based on factors such as age, gender, and chief complaint. Thus, the use of machine learning algorithms on patient data at the Jatibening Community Health Center can accelerate the initial diagnosis process and support more efficient medical decision-making
Co-Authors Adam Arif Budiman Ade Supriatna Aji Setiawan Ario Kurnianto Asyari Dariyus Bagus Tri Mahardika Romanda Ilham Suzuki Syofian Yan Sofyan Yan Sofyan Andhana Saputra