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All Journal Journal of Electrical Engineering and Computer (JEECOM)
Nurfauzi, Beni
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Control & Systems Engineering Electrical & Electronics Engineering Energy

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Early Detection System For Shallot Diseases Using Deep Learning With Mobilenet V2 Architecture Nurfauzi, Beni; Yulianto, Slamet; Wajihuddin, Moh; Wicaksana, Tangguh Islam; Priyadi, Dedi Eko Yunanto; Kusrini, Kusrini; Agastya, I Made Artha
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.14236

Abstract

This study investigates the application of Artificial Intelligence, specifically Convolutional Neural Networks (CNN), to support early detection of shallot leaf diseases, namely Moler and Purple Spot, which are commonly identified through manual visual inspection and are prone to subjectivity. The MobileNetV2 architecture is employed using a transfer learning approach on a publicly available shallot leaf image dataset. The research stages include data preprocessing, image augmentation, model training with a fine-tuning strategy, and implementation within a web-based system. Experimental results on the test dataset indicate that the proposed model achieved an accuracy of 99.07%. In particular, the model demonstrated high recall in detecting Moler disease and high precision in identifying Purple Spot disease. These findings suggest that lightweight architectures such as MobileNetV2 are suitable for efficient and accurate plant disease detection with relatively low computational requirements.
From Active to Zombie: A Literature Review on Bus Factor's Role in Open Source Project Lifecycle Decline Nurfauzi, Beni; Kusrini, Kusrini
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.15699

Abstract

Open Source Software (OSS) has become critical global digital infrastructure, yet its sustainability remains profoundly threatened by the concentration of knowledge within small cohorts of core developers a vulnerability formally quantified as the Bus Factor. This systematic literature review examines the role of Bus Factor dynamics in driving OSS projects toward the zombie phase, a deceptive intermediate state wherein repositories remain administratively active while genuine human-driven development has ceased. Following a PRISMA-based selection process across Google Scholar, Semantic Scholar, and the ACM Digital Library, 14 peer-reviewed studies were identified, quality-assessed, and thematically categorized to answer four research questions concerning Bus Factor measurement, lifecycle decline, computational prediction methods, and contributor behavioral dynamics. The review traces the evolution of Bus Factor measurement across four methodological generations and constructs a formal causal framework linking knowledge concentration to project death via sleeping developer behavior and zombie phase onset. Empirical evidence confirms that 89.65% of OSS projects experience total core developer loss, with only 27.07% recovering successfully. Critically, dynamic Bus Factor fluctuation remains entirely absent from existing abandonment prediction models. This review identifies this absence as the central research gap and identifies several candidate methodological directions for future empirical investigation. These findings establish a cohesive theoretical framework to guide proactive OSS sustainability research.
Co-Authors Agastya, I Made Artha Kusrini Priyadi, Dedi Eko Yunanto Slamet Yulianto Wajihuddin, Moh Wicaksana, Tangguh Islam