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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Prasetyo Nugroho, Eddy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Pengembangan Sistem Monitoring Prestasi Mahasiswa Berbasis Data Management Framework Mia Karisma Haq; Rani Megasari; Prasetyo Nugroho, Eddy
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.485

Abstract

The issue of limited structured data has often hindered the role of academic advisors in effectively monitoring students’ participation and achievements. At Universitas Pendidikan Indonesia (UPI), student achievement data, both academic and non-academic, is still largely processed manually through forms or online messaging groups, which does not support comprehensive analysis. This study aims to develop a Student Achievement Monitoring System based on the Data Management Framework (DAMA-DMBOK) to ensure that data management is standardized, integrated, and supports data-driven decision-making. The research method includes data collection through literature studies, observation, and interviews; designing the data architecture; formulating key performance indicators (KPIs); developing data visualization and reporting features; and evaluating data management maturity using the Data Management Maturity Assessment (DMMA). The implementation results show that the system has successfully increased the maturity level of data management in key areas such as Data Modeling and Design, Data Storage and Operations, Data Integration & Interoperability, Metadata Management, and Business Intelligence, reaching the Optimizing level. With its analytical dashboard, reporting features, and dynamic data filters, the system supports academic advisors in monitoring student achievement more accurately, continuously, and in a well-documented manner. This study is expected to serve as a reference for developing more adaptive and integrated student achievement monitoring systems at the study program level in higher education institutions.
Internet of things-drone trajectory planning model with edge computing based on long range payload in rural areas Prasetyo Nugroho, Eddy; Djatna, Taufik; Sukaesih Sitanggang, Imas; Hermadi, Irman
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8776

Abstract

The integration of internet of things (IoT) with unmanned aerial vehicle (UAV) or drone, for precision agriculture (PA) in rural tea plantations is required to ensure optimal outcomes. However, rural settings presents exceptional challenges for data transmission, particularly in maintaining effective communication between drone and ground control stations (GCS). Therefore, this research aimed to develop a payload metadata identification model using long range (LoRa) technology, known for robust IoT capabilities of the model. LoRa was used to transmit drone data packets to GCS, including image data computations and onboard sensor information. Additionally, the research proposed IoT-drone trajectory planning model, specifically designed for PA in rural tea plantations. This model incorporated LoRa technology for data transmission, leveraging the effectiveness of the model in remote areas. Edge computing was also integrated into model to classify the suitability of tea plantation picking areas based on image captured with drone. An important component of the research was trajectory planning system, which optimized drone flight paths by considering location data, throughput data, battery energy consumption, and the computation of suitable picking locations. Finally, experimental results showed the effectiveness of the proposed model in identifying payload metadata, monitoring drone trajectory, and optimizing picking location paths in rural tea plantations.
Co-Authors Erliza Hambali Imas Sukaesih Sitanggang Irman Hermadi Mia Karisma Haq Rani Megasari