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All Journal DEDIKASI JURNAL MAHASISWA DoubleClick : Journal of Computer and Information Technology Al-Insyirah Midwifery : Jurnal Ilmu Kebidanan (Journal of Midwifery Sciences) JOURNAL OF ICT APLICATIONS AND SYSTEM Inventor: Jurnal Inovasi dan Tren Pendidikan Teknologi Informasi JoDENS: Journal of Digital Ecosystem for Natural Sustainability
Budi Yanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Other

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Journal : JOURNAL OF ICT APLICATIONS AND SYSTEM

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Advanced Classification of Oil Palm Fruit Ripeness Deep Learning for Enhanced Agricultural Efficiency Hasibuan, Achmad Alwi; Ali Amran Nst; Aldi Antoni; Ray Handika; Budi Yanto; Akhmad Zulkifli
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.395

Abstract

The classification of oil palm fruit ripeness is a critical factor in optimizing palm oil production. Traditional methods of ripeness assessment, based on the percentage of detached fruits and changes in skin color, are prone to human error due to subjective judgment. This study proposes an advanced approach utilizing deep learning with the ResNet50 model to classify oil palm fruit ripeness into four levels: unripe, under-ripe, ripe, and overripe. The research evaluates the model's performance under various data allocations, optimizers, and learning rates while incorporating data augmentation techniques to enhance accuracy. Experimental results indicate the optimal configuration includes a 90/10 data split, Adam optimizer, and a learning rate of 0.0001, achieving precision of 96%, recall of 98%, F1 score of 97%, and accuracy of 97%. These findings highlight the potential of ResNet50 in delivering reliable, real-time classification for agricultural applications, providing a practical solution for farmers and industries. The study concludes that large and diverse training datasets are essential for achieving robust classification results
Implementation of Business Intelligence for Analyzing and Visualizing Data on Hacker-Affected Regions Using Data Studio Fitrianik Eva Lestari; Budi Yanto
JOURNAL OF ICT APLICATIONS AND SYSTEM Vol 3 No 1 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Implementation of Business Intelligence (BI) using the Google Data Studio platform facilitates the analysis and visualization of data from regions impacted by hacking activities. This study demonstrates the use of a data warehouse to integrate multiple datasets, enabling the generation of accurate, interactive dashboards for informed decision-making. Results highlight the effectiveness of BI in transforming raw data into meaningful insights through visual elements such as bar charts, pie charts, and scatter plots. The analysis provides actionable insights into areas affected by cyberattacks, emphasizing the importance of accessible and real-time data visualization tools. Findings validate the role of Google Data Studio in enhancing organizational responsiveness to security threats through intuitive and customizable reporting solutions.
Co-Authors Agus Suwandi Akhmad Zulkifli Aldi Antoni Ali Amran NST Amner Sastra Andria Bia Arrafah Syifa David Chow Devi Novita Sari Ferry Govert Anwar Fitrianik Eva Lestari Hasibuan, Achmad Alwi Hendrik Rusdian Arifudin Khairul Sabri lustini Ray Handika Raymond Erz Saragih Rindi Atikha Romy Wahyuny Tony Lau Victor