Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Devotion: Journal of Research and Community Service Jurnal Impresi Indonesia Jurnal Ar Ro'is Mandalika (Armada)
Anwarudin Anwarudin
STIKES Guna Bangsa Yogyakarta
Religion Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Implementasi Algoritma Machine Learning untuk Prediksi Curah Hujan di Indonesia Anwarudin Anwarudin
Jurnal Impresi Indonesia Vol. 5 No. 6 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i6.7800

Abstract

Curah hujan merupakan salah satu parameter penting dalam bidang meteorologi, pertanian, mitigasi bencana, dan pengelolaan sumber daya air. Ketidakpastian pola cuaca akibat perubahan iklim global menyebabkan kebutuhan terhadap sistem prediksi curah hujan yang lebih akurat semakin meningkat. Penelitian ini bertujuan untuk mengimplementasikan algoritma machine learning dalam memprediksi curah hujan berdasarkan data meteorologi. Metode penelitian menggunakan pendekatan kuantitatif dengan pemanfaatan algoritma Random Forest, Decision Tree, dan Support Vector Machine (SVM). Data penelitian diperoleh dari data historis cuaca yang meliputi suhu udara, kelembapan, tekanan udara, kecepatan angin, dan intensitas penyinaran matahari. Tahapan penelitian meliputi preprocessing data, pelatihan model, pengujian model, dan evaluasi menggunakan metrik akurasi, precision, recall, dan RMSE. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa terbaik dibandingkan algoritma lainnya dengan tingkat akurasi sebesar 92,4%. Implementasi machine learning terbukti mampu meningkatkan efektivitas prediksi curah hujan dan dapat digunakan sebagai pendukung pengambilan keputusan pada sektor pertanian, mitigasi banjir, dan pengelolaan lingkungan. Penelitian ini memberikan kontribusi dalam pengembangan sistem prediksi cuaca berbasis kecerdasan buatan di Indonesia.
Visual Health Education: A Creative Campaign Through Infographic Design Training Using Canva for High School Students Anwarudin Anwarudin; Arum Nuryati
Devotion : Journal of Research and Community Service Vol. 7 No. 6 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i6.25738

Abstract

Visual Health Education: A Creative Campaign Through Infographic Design Training Using Canva For High School Students explores health education for high school students, which requires a communicative and creative approach aligned with the characteristics of the digital generation. One strategy that can be applied is a visually based health campaign through infographic design training using Canva. This study aims to describe the implementation of health infographic design training using Canva and analyze its contribution to improving students’ health understanding, digital literacy, and visual communication creativity. This study used a descriptive qualitative approach with a participatory training approach. The activities were carried out through stages including identifying needs, delivering health-related materials, introducing infographic design principles, practicing the use of Canva, assisting with product development, and presenting the results of visual campaigns. Data were collected through observation, documentation of student work, participant reflections, and brief interviews. The results showed that Canva-based infographic design training increased student engagement in understanding health issues, primarily because the information was presented in a simple, engaging, and easily shareable format. Students acted not only as recipients of information but also as creators of health messages, able to process data, construct visual narratives, and convey preventive advice to their peers. These findings confirm that visually based health education can be an innovative strategy in school health promotion while strengthening students’ digital literacy and creative communication skills.
The Impact of AI Implementation on Integrated Prediction of TB and Anemia Spread Anwarudin Anwarudin
Jurnal Ar Ro'is Mandalika (Armada) Vol. 5 No. 3 (2025): JURNAL AR RO'IS MANDALIKA (ARMADA)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59613/armada.v5i3.5073

Abstract

This study explores the impact of Artificial Intelligence (AI) implementation in the integrated prediction of tuberculosis (TB) and anemia spread. The primary aim is to assess how AI technologies, such as machine learning algorithms and predictive modeling, can enhance the accuracy and efficiency of forecasting TB and anemia prevalence in different populations. The research employs a combination of data from healthcare databases, epidemiological studies, and patient records, analyzed using AI-driven tools to identify patterns, correlations, and predictive factors for the spread of these diseases. Results show that AI significantly improves the predictive capabilities, offering more precise and early identification of areas at risk, thus aiding healthcare providers in deploying targeted interventions. The integration of TB and anemia prediction using AI also allows for more effective resource allocation, early diagnosis, and improved patient outcomes. This study highlights the importance of AI in transforming healthcare practices and disease control efforts, suggesting that the integration of AI technologies could lead to more proactive public health strategies. The findings contribute to the growing body of knowledge on the intersection of AI and epidemiology, advocating for further research and wider adoption of AI-driven solutions in global health initiatives.
Co-Authors Arum Nuryati