Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JSAI (Journal Scientific and Applied Informatics)
Hadiguna Setiawan
Universitas Dian Nusantara
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Algoritma LSTM Untuk Klasifikasi Opini Terhadap Perkembangan Perkebunan Kelapa Sawit di Indonesia Hadiguna Setiawan; Handrie Noprisson; Abraham Cornelius Dachi; Ilim Hilimudin
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.10007

Abstract

This study aims to analyze public opinion on the development of oil palm plantations in Indonesia through sentiment classification using the Long Short-Term Memory (LSTM) algorithm. The data used in this study were taken from Twitter by collecting 750 tweets consisting of three sentiment categories: positive, negative, and neutral. The pre-processing stage includes filtering, tokenization, stemming, and word-embedding to prepare the data for further analysis. The LSTM model was applied to classify the sentiment of the processed tweets, and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the LSTM model produced an accuracy of 70.81%, with precision, recall, and F1-score varying between classes, namely 0.92, 0.71, and 0.80 for the negative class, 0.48, 0.63, and 0.55 for the neutral class, and 0.77, 0.77, and 0.77 for the positive class. This study shows that LSTM can be used to analyze public opinion on the issue of oil palm plantations, despite challenges in classifying neutral tweets.
Penerapan ResNet50 untuk Klasifikasi Citra Buah Kelapa Sawit Berdasarkan Tingkat Kematangan Hadiguna Setiawan; Handrie Noprisson; Abraham Cornelius Dachi; Ilim Hilimudin
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.10009

Abstract

Manual ripeness assessment still has limitations as it is subjective and highly dependent on human expertise. Therefore, this study aims to apply a deep learning approach based on the ResNet50 architecture to classify oil palm fruit ripeness into three categories, namely unripe, ripe, and overripe. The dataset used in this study consists of 1,350 RGB images of oil palm fruits, which are divided into training, validation, and testing sets with a ratio of 70:10:20. All images are preprocessed by resizing them to 224 × 224 pixels and normalizing pixel values, while data augmentation is applied to the training set to improve model generalization. A pre-trained ResNet50 model on the ImageNet dataset is employed as a feature extractor and trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ for 50 epochs. Experimental results show that the model achieves an accuracy of 89.7% on the training data and 84.1% on the validation data. Evaluation on the testing data yields an accuracy of 84.07%, with average precision, recall, and F1-score values of 84.71%, 84.07%, and 84.32%, respectively. These results indicate that the proposed ResNet50-based model demonstrates good and stable performance in classifying oil palm fruit ripeness levels.
Co-Authors Abraham Cornelius Dachi Ilim Hilimudin Ilim Hilimudin Noprisson, Handrie