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All Journal Jurnal Teknologi Terpadu
Theresia Kurniati Seran
Universitas Sari Mulia
Computer Science & IT Engineering

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Klasifikasi Buah Kelapa Sawit dengan Convolutional Neural Network Arsitektur Inception-v4 Theresia Kurniati Seran; Septyan Eka Prastya; Muhammad Zulfadhilah; Rudy Ansari
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2597

Abstract

The palm oil industry plays a crucial role in Indonesia’s economy, making fruit classification by ripeness levels essential to ensuring the quality of palm oil production. This study aims to develop a classification system for oil palm fruits into two categories: ripe and unripe, using a Convolutional Neural Network with the Inception-v4 architecture. The dataset consists of 2,900 images, divided into training (2,000), validation (500), and testing (400) sets. The research stages include data collection, pre-processing (duplicate detection, augmentation, and normalization), model training with Inception-v4, evaluation, and result interpretation. Model performance was evaluated using accuracy, precision, recall, f1-score, and confusion matrix. Results indicate that Inception-v4 achieved the highest validation accuracy of 95% in classifying oil palm fruit. Further experiments were conducted using various optimizers (SGD, Adam, RMSprop, Adagrad, Adadelta) to enhance performance. This study confirms that Inception-v4 is highly effective for oil palm fruit classification and can be applied in plantation industries to improve harvest efficiency and production quality.
Co-Authors Muhammad Zulfadhilah Rudy Ansari Septyan Eka Prastya