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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL REKAYASA SISTEM INDUSTRI Journal on Education Journal of Computer Networks, Architecture and High Performance Computing Engineering and Technology International Journal (EATIJ) JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Okta Veza
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Simulation Study of EfficientNetB0 Performance for Cocoa Pod Disease Classification Using Literature Based Synthetic Data Okta Veza; Sherly Agustini; Nofri Yudi Arifin; Albertus Laurensius Setyabudhi
Engineering and Technology International Journal Vol 7 No 03 (2025): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v7i03.1335

Abstract

Automated detection of cocoa (Theobroma cacao) pod diseases such as black pod, pod borer infestation, and frosty pod rot is critical for safeguarding yield, yet the development of deep-learning classifiers is frequently constrained by the scarcity of curated, well-balanced image datasets. This study presents a controlled simulation that evaluates the expected performance envelope of an EfficientNetB0 classifier under idealized, literature-grounded conditions before field data collection is undertaken. Rather than asserting empirical field results, a synthetic dataset is constructed whose per-class feature distributions (color, texture, and lesion morphology) are parameterized from values reported across six core references. A balanced corpus of 3,000 synthetic images spanning four classes (healthy, black pod, pod borer, frosty pod) was generated and partitioned using a stratified 70/15/15 split. EfficientNetB0, initialized with ImageNet weights and fine-tuned with standard augmentation, achieved a simulated test accuracy of 93.8%, a macro-averaged F1-score of 0.926, and balanced per-class precision and recall in the 0.90-0.95 range. The confusion matrix indicates that the principal source of error is morphological overlap between pod borer and frosty pod presentations. The results delineate a plausible upper-bound performance band to guide sample-size planning, augmentation strategy, and architecture selection for a subsequent field study. All reported figures are framed explicitly as simulation outputs.
Deep Learning Approaches for Cocoa Pod Disease Classification A Literature Review Okta Veza; Nofri Yudi Arifin; Albertus Laurensius Setyabudhi
Engineering and Technology International Journal Vol 6 No 03 (2024): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v6i03.1337

Abstract

Cocoa (Theobroma cacao) is a cornerstone of many tropical economies, yet its yield is persistently threatened by pod diseases such as black pod rot, frosty pod rot, and cocoa pod borer infestation. Over the past decade, deep learning, and convolutional neural networks (CNNs) in particular, has emerged as a powerful tool for automated plant disease diagnosis from images. This paper presents a structured literature review of deep-learning approaches applied, directly or by close analogy, to cocoa pod disease classification. Following a PRISMA style protocol, 41 studies published between 2016 and 2025 were selected from major databases and synthesized along five dimensions: data sources and dataset construction, preprocessing and augmentation, network architectures, training and transfer-learning strategies, and evaluation methodology. The review finds that transfer learning with compact architectures, notably ResNet, MobileNet, and EfficientNet variants, dominates recent work and consistently achieves reported accuracies above 90% on related tasks. Three persistent gaps are identified: the scarcity of large, balanced, and openly available cocoa specific image datasets; limited validation under realistic field conditions; and inconsistent reporting of evaluation metrics. The review concludes by outlining research directions, including domain adaptation, lightweight on device inference, explainability, and standardized benchmarking, to move cocoa pod disease classification from controlled experiments toward deployable tools for smallholder agriculture.
Co-Authors Abdul Rohmad Basar Afif Rahman Riyanda Aisyah Fajriati Al Rusman Albertus L. Setyabudhi Alex Sandri Sikumbang Alhamidi, Alhamidi Alif Sahputra Azharye Putri Aulia Catur Budi Susilo David Saro Dedy Irfan Delia Meldra Della Daphiza Devega, Army Trilidia Dina Mayadiana Suwarma Doni Damara Doni Syofiawan Eka Safira Fernandes, Atman Lucky Hanafi Hernando, Luki Ikhsan Amar Jusman Indah Kusuma Dewi, Indah Kusuma Jenuri Kurnia Mustafani Larisang Laurensius Setyabudhi, Albertus M. Daud Yahya Muhamad Yari Hanzalah Muhammad Aris Fadilla Muhammad Bobbi Kurniawan Nasution Muhammad Rizki Reza Saputra Muhammad Ropianto Muskhir, Mukhlidi Mutiara Ayu Mawaddah Nanda Jarti Nandar Cundara Abdurahman Nofri Yudi Arifin Novi Hendri Adi Nurlinda Nurul Maghfiroh Radillah, Teuku Revi Illya Badri Ririt Dwiputri Permatasari Sabtu Sabtu Sayuti sherly agustini Simatupang, Wakhinuddin Sumijan Sumijan Wahyudi Wahyudi