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All Journal Journal of Computer Science Artificial Intelligence and Communications Jurnal Pengabdian Masyarakat Berdampak International Journal of Applied Science and Technology Application
Prayoga, Abil Alwi
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Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Social Sciences Other

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Pemberdayaan UMKM Melalui Pelatihan Digital Marketing dan Desain Konten Media Sosial Hasanuddin, Muhammad; Rizki, Cindy Atika; Khodijah, Siti; Prayoga, Abil Alwi
Jurnal Pengabdian Masyarakat Berdampak Vol. 1 No. 3 (2025): September 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jupemba.v1i3.60

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) dalam memanfaatkan digital marketing dan desain konten media sosial sebagai strategi pemasaran yang efektif di era digital. Permasalahan utama yang dihadapi mitra adalah keterbatasan pengetahuan dalam membuat konten promosi yang menarik serta kurangnya pemahaman terhadap strategi pemasaran digital berbasis platform daring. Metode pelaksanaan meliputi sosialisasi, pelatihan praktik menggunakan aplikasi desain (Canva dan CapCut), pendampingan pembuatan akun bisnis di media sosial, serta evaluasi efektivitas kegiatan melalui survei kepuasan dan peningkatan kemampuan peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada kemampuan peserta dalam membuat konten visual dan mengelola akun media sosial untuk tujuan promosi. Sebanyak 85% peserta menyatakan mampu menerapkan teknik desain sederhana dan strategi pemasaran digital secara mandiri setelah pelatihan. Dampak nyata dari kegiatan ini terlihat dari peningkatan interaksi pelanggan dan penjualan produk secara daring. Kegiatan ini diharapkan dapat menjadi model pemberdayaan berkelanjutan yang memperkuat daya saing UMKM di era transformasi digital.
Development of an Employee Performance Monitoring Information System Using a Web-Based Interactive Dashboard Prayoga, Abil Alwi; Hasanuddin, Muhammad; Khodijah, Siti; Rizki, Cindy Atika; PA, Dahrim
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.63

Abstract

This research aims to develop an employee performance monitoring information system that utilizes a web-based interactive dashboard to enhance decision-making and managerial oversight. In many organizations, traditional performance evaluation methods are often time-consuming, static, and lack real-time insight, resulting in inefficiencies in performance tracking. To address these challenges, the proposed system is designed to provide dynamic visualization of key performance indicators (KPIs), attendance records, task completion rates, and other critical metrics through an interactive and user-friendly dashboard interface. The development process follows the Waterfall methodology, encompassing stages of requirements analysis, system design, implementation, testing, and deployment. The system was built using PHP and JavaScript for front-end interactivity, with a MySQL database to manage data storage. The dashboard includes various visual tools such as graphs, charts, and progress bars to facilitate real-time monitoring and performance analysis. Testing results indicate that the system performs effectively, offering accurate and timely information that supports employee evaluation and organizational planning. User feedback also reveals a high level of satisfaction due to the dashboard's ease of use and responsiveness. Overall, the implementation of this web-based performance monitoring system is expected to improve transparency, accountability, and productivity within the organization.
Urban Vegetation Cover Prediction Using Sentinel-2 NDVI and Random Forest: A Brief Narrative Review Hasanuddin, Muhammad; Prayoga, Abil Alwi
International Journal of Applied Science and Technology Application Vol. 1 No. 1 (2026): March 2026
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i1.3

Abstract

A predictive model of urban vegetation cover is developed by integrating remote sensing technology, cloud computing, and machine learning algorithms. The study used the Normalized Difference Vegetation Index (NDVI), calculated from Sentinel-2 satellite imagery and analyzed in Google Earth Engine (GEE), to monitor vegetation conditions at a wide spatial scale. The research approach uses quantitative methods, including spatial analysis based on satellite imagery and predictive modeling with the Random Forest algorithm. The research process includes acquiring Sentinel-2 Level-2A images, pre-processing them with cloud masking and atmospheric correction, calculating NDVI values, and developing vegetation prediction models using machine learning methods. The results showed that the Random Forest model predicted vegetation cover with high accuracy, as indicated by a Coefficient of Determination (R²) of 0.85 and a Root Mean Square Error (RMSE) of 0.045. The resulting vegetation distribution map shows significant variations in vegetation density between natural vegetation areas, agricultural land, and built-up areas. The findings of this study show that integrating NDVI from Sentinel-2, Google Earth Engine, and the Random Forest algorithm is an effective approach for monitoring and predicting urban vegetation cover. The results of this study make a methodological contribution to the development of remote sensing-based geospatial analysis and provide a scientific basis for sustainable urban planning and green open space management in urban areas.
Co-Authors Muhammad Hasanuddin, Muhammad PA, Dahrim Rizki, Cindy Atika Siti Khodijah