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Apriani Apriani
FKIP UNTAD
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Journal : Jurnal Teknik Informatika (JUTIF)

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Data Augmentation-Driven Predictive Performance Refinement in Multi-Model Convolutional Neural Network for Cocoa Ripeness Prediction Apriani, Apriani; Switrayana, I Nyoman; Hammad, Rifqi; Irfan, Pahrul; Pratama, Gede Yogi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5298

Abstract

Timely and accurate prediction of cocoa fruit ripeness is critical for optimizing harvest schedules, improving yield quality, and supporting post-harvest processing. Conventional visual inspection methods are prone to subjectivity and inconsistencies, especially when distinguishing among multiple ripeness levels based on fruit age. This study proposes a deep learning approach that leverages multi-model convolutional neural network transfer learning combined with image data augmentation to classify cocoa fruit into four maturity stages derived from fruit age. An augmented dataset of cocoa fruit images was used to fine-tune five well-established pre-trained models: MobileNetV2, Xception, ResNet50, DenseNet121, and DenseNet169. Data augmentation techniques were employed to increase variability and improve model generalization. Model evaluation was conducted using a standard 80:20 training-to-testing split to ensure sufficient data for learning while preserving a representative test set across all ripeness classes. The results demonstrate that DenseNet169 consistently outperformed other models, achieving the highest average accuracy of 85,05%, followed by DenseNet121 84,06%. Across all models, the use of data augmentation led to notable performance gains, highlighting its importance in enhancing predictive capability and reducing overfitting. The proposed framework shows promising potential for automating ripeness classification in agricultural contexts, offering a robust, scalable, and accurate solution for intelligent cocoa harvest management. This work contributes to the growing application of deep learning in precision agriculture, particularly in addressing fine-grained classification problems using limited but enriched visual data.
Co-Authors Abdul Kadir Abdul Muhid, Abdul Adelia Febriyossa Adlika, Nur Meily Afiyah, Ida Antoni Antoni Anwar, Najih Anwari . Aprilianti Aprilianti Arjudin Arjudin Aryati Alitu Azhar Bafadal, Azhar Azlina, Fitri Ayatul Balqis, Ranne Basuki Rachmad Chanakan Sojayapan Darma Asasih, Widani Desliyanah, Sherly Desvitasari, Helsy Dwi Ratnasari Eka Martiningsih, Ni Gst.Ag. Gde Entin Daningsih Fatriansari, Asih Hammad, Rifqi Hwa, Fenny Ong Lian I Ketut Arnawa I Ketut Sumantra I Made Suryana I Nyoman Switrayana Ibrahim, Isra Dewi Kuntary Ihdiani Abubakar Ilhidda, Inul Kartini Ruth Maduma Manalu Khairan marzuki Kormil Saputra Kurniadin Abd Latif Kurniawan, Ahmad Lalu Danu Prima Arzani Lestari, Adhe Utami Mardedi, Lalu Zazuli Azhar Masliana Masliana, Masliana Muhammad Basri Muhammad Yunus Munirwan Zani, Munirwan Murti, Evivana Pranika Musrifah, Ainun Ni Gst. Ag. Gde Eka Martiningsih Ningrum, Desyana Nuroh Najmi Oris Krianto Sulaiman Pahrul Irfan Pahrul, Dedi Pebriani, Ela Perdana Jendra Dewi, Putri Prasetya, Fikki Pratama, Gede Yogi Puthi Dwi Untari Putinah, Putinah Qur’ani, Dindari Bela Rachman, Azis Rahmadani, Nur Asmi Suci Rahmalia Afriyani RAHMAZANIATI, LINDA Rosa Rita, Titian Rachmawati Rusmilawati, Nila Ruswati, Henny Meilani Sahtiani Jahrir, Andi Sari, Henni Maya Sarma Panggabean Setyawati, Rina Sirojul Hadi Sri Buwono . Sudi Prayitno Sulatri, Sulatri Syafei, Abdul Syahrir, Moch. Syam, Nurlaily Tomy, John Wati, Henny Yokhebed Yokhebed Yulis Tyaningsih, Ratna Yunawati, Irma Zakiyudin, Hizbu Zulfikri, Muhammad