Article Per Year (5 Year)
p-Index From 2021 - 2026
1.94
P-Index
This Author published in this journals
All Journal JSAI (Journal Scientific and Applied Informatics) JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) JCOSIS (Journal Computer Science and Information Systems)
Hadiguna Setiawan
Universitas AMIKOM Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab Marissa Utami; Sarwati Rahayu; Sulis Sandiwarno; Erwin Dwika Putra; Marissa Utami; Hadiguna Setiawan
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5379

Abstract

Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.
Komparasi Hasil Color Feature Extraction HSV, LAB dan YCrCb pda Algoritma SVM untuk Klasifikasi Spesies Burung Sarwati Rahayu; Andi Nugroho; Erwin Dwika Putra; Mariana Purba; Hadiguna Setiawan; Sulis Sandiwarno
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.5920

Abstract

The classification of bird species is a problem often faced by ornithologists, and has been considered scientific research since antiquity. This study aims to evaluate the results of color feature extraction including HSV, LAB and YCrCb against the results of the SVM classification. In addition, the results of this study are useful to determine the performance of color feature extraction that is suitable for bird species classification. The dataset used was 22,617 bird species images. Based on experimental results, the effect of HSV on the SVM classification caused a decrease in accuracy by -0.33% while LAB and YCrCb on the SVM classification caused an increase in accuracy of 0.44% and 0.21%. However, the accuracy of the SVM classification does not yet have good performance so that further research will be carried out using other classifications, including convolutional neural networks and others.
Analisis Performa Metode Klasifikasi Dataset Multi-Class Kanker Kulit Menggunakan KNN dan HOG Rahayu, Sarwati; Sandiwarno, Sulis; Dwika Putra, Erwin; Utami, Marissa; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6423

Abstract

Detection of skin cancer in its early phase is a challenge even for dermatologists. This study aims to analyze the performance of classification methods on multiclass skin cancer datasets using K-nearest neighbor (KNN) and histogram of oriented gradients (HOG). The dataset is taken publicly under the name Skin Cancer MNIST dataset: HAM10000 dataset totaling 10,015 data. The first experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The second experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The last experiment using the pixels per cell parameter of 8.8 and cells per block of 2.2 got the best accuracy of 61.43%.
Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Sentimen Analisis Teks Umpan Balik Tentang Evaluasi Pengajaran Dosen Setiawan, Hadiguna; Ariatmanto, Dhani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6572

Abstract

Evaluation of lecturer performance is very important because it helps in monitoring and ensuring that lecturers fulfill their duties effectively in maintaining integrity and teaching lecture material. By assessing lecturer performance based on criteria such as teaching, it can identify areas for improvement and provide support if needed. This study aims to determine the accuracy level of machine learning and deep learning combined with word-embedding for text analysis of lecturer teaching performance evaluation using preprocess techniques.The dataset consisted of 663 positive data, 552 negative data, and 465 neutral data. Successful in the results of the experiment, the training accuracy value for each classification method included KNN of 74.75%, SVM of 65.78%, RF of 98.58%, LSTM of 95.64% and Bi-LSTM of 95.91%. The test accuracy value for each classification method includes KNN of 59.82%, SVM of 62.88%, RF of 69.37%, LSTM of 70.81% and Bi-LSTM of 72.25%. The most superior method in processing data of 663 positive data, 552 negative data, and 465 neutral data by applying the word-embedding method, namely BiLSTM with a training accuracy of 95.91% and a testing accuracy of 72.25%.
Analisis Usabilitas Sistem Informasi Akademik Berdasarkan Usability Scale (Studi Kasus: Universitas Mercu Buana) Rahayu, Sarwati; Nugroho, Andi; Sandiwarno, Sulis; Salamah, Umniy; Dwika Putra, Erwin; Purba, Mariana; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7478

Abstract

The usability analysis on the website of Mercu Buana University (UMB) is an important research carried out to ensure that the site effectively supports the university's goals, especially in terms of the user's experience in completing academic and administrative goals with ethical and professional standards. This research was carried out during the period January 2024 to May 2024. The main purpose of this study is to measure the usability of the UMB website using a questionnaire method. The questionnaire used for the research adapted the System Usability Scale (SUS) which consisted of a total of 10 questions. Based on the calculation of each statement item having a minimum score of 0 and a maximum score of 2.5, the final score of each respondent ranged from 0 to l00. The average score obtained was 63,125. Based on the results of the score of 63,125, the UMB website has a score in the range of 50 to 70. This shows that the UMB website is in the "quite good" category but there is still a need for a little improvement. Some icons or layouts on the UMB website are not familiar to respondents. In addition, there needs to be guidelines developed to provide information on how to use the website for users who are using the UMB website for the first time.
Analisis Algoritma LSTM Untuk Klasifikasi Opini Terhadap Perkembangan Perkebunan Kelapa Sawit di Indonesia Setiawan, Hadiguna; Noprisson, Handrie; Dachi, Abraham Cornelius; Hilimudin, Ilim
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 Setiawan, Hadiguna; Noprisson, Handrie; Dachi, Abraham Cornelius; Hilimudin, Ilim
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 Andi Nugroho Andi Nugroho, Andi Ayumi, Vina Dachi, Abraham Cornelius DHANI ARIATMANTO Hilimudin, Ilim Mariana Purba Mariana Purba Marissa Utami Noprisson, Handrie Purba, Mariana Putra, Erwin Dwika Salamah, Umniy Sarwati Rahayu Sulis Sandiwarno Wachyu Hari Haji