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All Journal MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Journal of Software Engineering and Information System (SEIS)
Adesyahputra, Muh Kevin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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VISUALISASI DATA LOKASI RAWAN BENCANA DI JAWA TENGAH MENGGUNAKAN POWER BI Adesyahputra, Muh Kevin; Febrianto, Ricky; Khilmi Wibowo, Muhammad Nanang; Handayani, Titis
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 1 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i1.6619

Abstract

The Central Java Province, as a disaster-prone region, faces risks due to both natural and human factors. Low awareness of disaster risks and insufficient mitigation efforts worsen the situation. This research utilizes Power BI to visualize disaster data, contributing to the understanding of risks. A quantitative method is employed with a focus on data analysis, collected from the Indonesian Disaster Risk Index. The data blending and cleaning phase ensure dataset quality and relevance before implementation into Power BI. An interactive dashboard is created with graphics such as tables, bar charts, and donut charts. Evaluation and analysis of the results are conducted to ensure the effectiveness of the visualizations. The findings indicate that forest fires are the most dominant disaster, followed by floods and landslides. Volcanic eruptions have the lowest frequency. Recommendations include enhancing preparedness for forest fires, in-depth analysis of disaster causative factors, periodic data updates, and improved collaboration among stakeholders.
Analysis of corn production in Indonesia using business intelligence technology based on Power BI Adesyahputra, Muh Kevin; Rachmawati, Eka Putri
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 1 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i1.21-31

Abstract

Corn production trends in Indonesia from 2020 to 2024 were analyzed to address regional disparities and enhance data-driven agricultural decision-making. Datasets from the Ministry of Agriculture and the Central Bureau of Statistics were integrated, transformed, and visualized using Microsoft Power BI, with a focus on evaluating fluctuations in harvested area, production volume, and productivity. Key objectives included identifying challenges linked to fragmented data and external disruptions. An Extract-Transform-Load (ETL) framework harmonized pre-2023 and post-2023 datasets, enabling standardized comparisons across 38 provinces. Results indicated a production peak of 486,000 tons in 2022, followed by a 4.5% decline in 2023 due to adverse climatic conditions and supply chain instability, and partial recovery to 15.2 million tons in 2024. Pronounced regional disparities emerged:  West Java recorded 80 quintals per hectare productivity, while urbanized regions like Jakarta reported negligible output. The analysis underscores the efficacy of Business Intelligence (BI) tools in converting raw agricultural data into strategic insights, offering policymakers pathways to optimize resource allocation, mitigate inequities, and strengthen climate-resilient practices. These outcomes highlight BI’s transformative potential in advancing sustainable agricultural development and adaptive governance frameworks.
Co-Authors Febrianto, Ricky Khilmi Wibowo, Muhammad Nanang Rachmawati, Eka Putri Titis Handayani