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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.
Introduction to AI and Computational Thinking for Teachers at SDIT Mafatih Bekasi Linda Nur Afifa; Adam Arif Budiman; Aji Setiawan; Afri Yudha
JEPTIRA Vol 3 No 2 (2025)
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jeptira.v3i2.127

Abstract

The development of Artificial Intelligence (AI) and the demand for strengthening Computational Thinking (CT) skills make AI and CT literacy a key competency for educators in the 21st century. At the elementary level, particularly in Integrated Islamic Elementary Schools (SDIT), teachers play a crucial role in instilling systematic thinking and technological literacy from an early age, yet many teachers lack conceptual understanding or practical skills related to AI and CT. This community service activity aims to improve SDIT teachers' basic understanding of AI concepts, examples of AI applications in education, and the CT process and its implementation in elementary school learning activities. The activity was carried out in the form of face-to-face training that included material presentations, demonstrations of educational AI applications, and practical CT activity designs tailored to the characteristics of elementary school students. Evaluation was conducted using pre- and post-tests to measure knowledge gains, and questionnaires to assess participants' perceptions and satisfaction levels. The implementation results showed an increase in participants' knowledge scores between before and after the training, accompanied by a more positive change in attitudes towards the use of AI and CT in the classroom. Teachers were also able to design simple and contextual CT-based learning activities for elementary school students. This activity shows that structured training with a combination of conceptual material and directed practice is effective in building AI and CT literacy among SDIT teachers.
Implementasi Data Mining Untuk Mendukung Program Reduksi Sampah di Daerah Khusus Jakarta Dengan Menggunkan Algoritma Time Series dan K-Means Clustering Muhammmad Krisna Adiputro; Afri Yudha
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.74

Abstract

This study aims to analyze the trend of waste growth in Jakarta using the ARIMA method and to group areas based on waste volume using the K-Means Clustering algorithm. The waste accumulation problem at the Bantargebang TPST continues to worsen each year, with increasing volumes from various sub-districts. Data used in this study were obtained from the DKI Jakarta Environmental Agency, covering the period from January 2022 to April 2024, focusing on organic waste, plastic, and household hazardous waste (B3). The research applies the CRISP-DM methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data processing includes cleaning, normalization, and splitting into training and testing sets. The analysis results show that the ARIMA model achieves good forecasting accuracy, with MAPE, MAE, and RMSE values around 3652. The K-Means algorithm successfully classifies Jakarta areas into three main clusters dominated by organic, plastic, and mixed waste types. A web-based system was developed using Streamlit and MongoDB Atlas to facilitate data analysis and visualization for policymakers, especially the Environmental Agency. The study concludes that ARIMA is effective in forecasting waste growth, while K-Means supports more targeted waste management strategies. It is recommended to enhance the system by incorporating external variables such as policy changes and socio-economic factors, and to improve model accuracy using more advanced machine learning techniques. Additionally, the system should be continuously updated and expanded to support more optimal and sustainable waste management across Jakarta.
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 Linda Nur Afifa Muhammmad Krisna Adiputro Romanda Ilham Suzuki Syofian Yan Sofyan Yan Sofyan Andhana Saputra