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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Terapan (J-TIT)
Wawan Hendriawan Nur
National Research and Innovation Agency
Computer Science & IT Electrical & Electronics Engineering

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A cloud GIS-based framework implementation in developing countries Wawan Hendriawan Nur; Ida Narulita; Yugo Kumoro; Yuliana Susilowati; Yuliana Yuliana; Faiz Rohman Fajary; Sekar Nur Wulandari
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.4195

Abstract

Cloud computing technology has successful cost savings, business effectiveness, and higher scalability in various fields, including the government field. The pandemic Covid-19 era has been accelerating the adoption of cloud technology; the enterprises have instructed the employees to work from home to reduce transmission. The cloud-based framework for government in developing countries was developed. However, it was arduous to apply in Indonesia as a developing country, especially in areas where technology infrastructure, human resources, and funding are insufficient. Thus, the study of the cloud implementation framework in developing countries is essential. This paper used the enterprise architecture planning method for designing a cloud GIS-based framework. The developed framework successfully implemented the cloud-based GIS technology in Indonesia with limited ownership and infrastructure of technology, resources, and funding.
Performance Comparison of CNN Transfer Learning Models for Coffee Bean Quality Classification Nur Muhammad Fadli; Prawidya Destarianto; Hendra Yufit Riskiawan; Bekti Maryuni Susanto; Satrio Adi Priyambada; Wawan Hendriawan Nur; Mukhamad Angga Gumilang
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.457

Abstract

According to SNI Standard No. 01-2907-2008, accurate sorting of coffee beans is crucial for improving export value. Manual sorting is time-consuming, subjective, and error-prone, especially when visual differences are subtle between roast levels. This study proposes and evaluates an automatic, machine-learning based system to support quality assurance in coffee production. We compare three transfer-learning CNN architectures: Xception, MobileNetV2, and EfficientNet-B1 on a publicly available dataset of 1,600 coffee bean images divided into four classes (dark, medium, light, green). All models were trained with the same preprocessing and hyperparameter settings. EfficientNet-B1 achieved the highest test accuracy (100%), followed by Xception (99.5%) and MobileNetV2 (97%). We discuss trade-offs between accuracy and computational efficiency and recommend EfficientNet-B1 for high-accuracy applications and MobileNetV2 for edge/mobile deployment.
Co-Authors Bekti Maryuni Susanto Faiz Rohman Fajary Hendra Yufit Riskiawan Ida Narulita Mukhamad Angga Gumilang Nur Muhammad Fadli Prawidya Destarianto Satrio Adi Priyambada Sekar Nur Wulandari Yugo Kumoro Yuliana Susilowati Yuliana Yuliana