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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 538 Documents
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Contrastive Learning pada IndoBERT untuk Analisis Sentimen Kebijakan Makan Bergizi Gratis Dwi Dian Sari Nonibenia Hia; Sunneng Sandino Berutu; Jatmika
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.v9i1.9963

Abstract

Transformer-based language models such as IndoBERT still face limitations in topic and sentiment analysis of short social media texts, particularly due to embedding anisotropy, semantic overlap between topics, and limited sensitivity to implicit sentiment intensity. This study aims to evaluate the effectiveness of integrating SimCSE-based contrastive learning to optimize IndoBERT vector representations for sentiment analysis of the “Free Nutritious Meals” public policy. A comparative experimental approach was employed using an equal number of topics (three topics) and evaluated through BERTopic and Aspect-Based Sentiment Analysis (ABSA). The results demonstrate that the contrastive learning–based model substantially improves cluster separability, indicated by an increase of more than 1000% in the Silhouette Score compared to the baseline model, along with a reduction in topic overlap of approximately 40–50%. In addition, topic keyword diversity increased by more than 75%, yielding more informative and interpretable topic representations. In aspect-based sentiment analysis, the contrastive model exhibited approximately a 50% improvement in sensitivity to sentiment intensity and achieved perfect classification of implicit high-confidence sentiments that were previously misclassified as neutral by the baseline model. These findings confirm that contrastive learning–based embedding optimization effectively addresses the limitations of conventional embeddings and enhances the quality of topic modeling and aspect-based sentiment analysis for Indonesian social media texts.
Penerapan Optimasi Convolutional Neural Network untuk Klasifikasi Multi-Kelas Tumor Otak pada Citra MRI Vina Ayumi
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.v9i1.9986

Abstract

Brain tumors are among the most critical neurological diseases and require early and accurate diagnosis to support appropriate medical treatment. Magnetic Resonance Imaging (MRI) is widely used for brain tumor detection due to its high-resolution imaging capability; however, manual analysis of MRI images is time-consuming and highly dependent on the expertise of radiologists. Therefore, this study aims to apply an optimized Convolutional Neural Network (CNN) for multi-class brain tumor classification using MRI images. The dataset used in this study consists of 7,023 MRI images, categorized into four classes: glioma, meningioma, pituitary, and healthy, and divided into training, validation, and testing subsets. The research stages include image preprocessing, CNN architecture design, hyperparameter optimization, model training for 50 epochs, and performance evaluation. The training process achieved an accuracy of 87.44%, while the validation accuracy reached 85%, indicating good model generalization. Model evaluation on the test dataset using a confusion matrix, precision, recall, F1-score, and accuracy resulted in an overall accuracy of 77.8%.
Klasifikasi Penyakit dan Hama Daun Padi Menggunakan Model ResNet50 pada Dataset AgroGuard AI Mariana Purba
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.v9i1.9987

Abstract

Rice leaf diseases and pests are one of the main factors causing decreased rice productivity. Manual disease identification still relies on the experience of farmers and extension workers, potentially leading to delayed diagnosis and mishandling. This study aims to develop an image-based rice leaf disease and pest classification model using the ResNet50 deep learning architecture. The dataset used comes from AgroGuard AI and consists of seven classes: blast disease, healthy leaves, insect attacks, leaf roller pests, leaf scald disease, brown spot disease, and tungro disease. The dataset is divided into training, validation, and test data with a ratio of 70%:15%:15%, where the test data is balanced with 400 images in each class. The ResNet50 model was trained from scratch without pre-training weights with a batch size of 32, a learning rate of 0.001, and 50 epochs. The evaluation results showed that the model achieved an accuracy of 77.86% on the test data, with a training accuracy of 80.52% and a validation accuracy of 89.38%. Evaluation using a confusion matrix and precision, recall, and F1-score metrics indicated that the model performed quite well and stably across all classes.
Pengenalan Penyakit Tanaman Berdasarkan Citra Daun Menggunakan Arsitektur DenseNet Berbasis Sekuensial Mariana Purba
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.v9i1.9988

Abstract

Plant diseases that affect leaves can significantly reduce crop quality and productivity, making accurate and efficient detection methods essential. This study aims to develop a plant disease recognition model based on leaf images using a sequential DenseNet121 architecture. The dataset consists of 1,530 leaf images categorized into three classes: Healthy, Powdery, and Rust, which are divided into training, validation, and testing sets with a relatively balanced distribution. The model employs DenseNet121 as a base model with pre-trained ImageNet weights, where all base layers are frozen to function as a feature extractor. The classification process utilizes GlobalAverage Pooling2D, Dense, Dropout, and Softmax layers. Experimental results show that the model achieves an accuracy of 98.28% on the training data and 96.25% on the validation data. Evaluation on the test dataset yields an accuracy of 93.33%, indicating that the proposed model demonstrates good generalization capability in classifying plant diseases based on leaf images. These results suggest that the sequential DenseNet architecture is effective for plant disease recognition and has potential for further development as a decision support system in agriculture
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.
Evaluasi Keberhasilan Sistem Informasi Absensi dan Penggajian Terintegrasi Berdasarkan Model DeLone dan McLean Imam Tauzy; Sri Dianing Asri
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.v9i1.10013

Abstract

Employee attendance and payroll systems play a crucial role in human resource management as they directly affect payroll accuracy, transparency, and employee satisfaction. However, many previous studies have primarily focused on system development and functional testing, with limited attention to comprehensive system success evaluation. This gap highlights the need for an evaluative approach that measures system quality, information quality, and the benefits perceived by users. This study aims to evaluate the success of an integrated web-based attendance and payroll information system using the DeLone and McLean Information System Success Model. The research employed a quantitative approach with a survey method, using a Likert-scale questionnaire distributed to system users. Data were analyzed using descriptive quantitative analysis and converted into percentage values. The results indicate that the system achieved a success rate of 92%, categorized as very good. These findings demonstrate that the system has high system quality and information quality and provides significant net benefits in improving administrative efficiency and payroll information transparency for employees.
Komparasi Algoritma LightGBM, SVM, dan Logistic Regression dalam Memprediksi Penyakit Stroke Bryant Steven Aritonang; Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7551

Abstract

Stroke is a serious condition that can lead to disability or death due to disrupted blood flow to the brain. This study aims to compare three machine learning algorithms: LightGBM, Support Vector Machine (SVM), and Logistic Regression, in predicting the risk of stroke. The dataset used contains 5110 rows with 12 attributes, including demographic information and health history. The research process began with data preprocessing, followed by splitting the data into training and testing sets. Models were then trained using the three algorithms and evaluated using accuracy, precision, recall, and F1-score metrics. The analysis results indicate that Logistic Regression performed the best overall, providing a balance between detecting stroke cases and identifying healthy individuals. SVM showed stable results with a balance between recall and precision, while LightGBM, despite high accuracy, was less effective in detecting stroke cases. The study concludes that Logistic Regression is the most suitable model for predicting stroke risk, though SVM can be a good alternative.
Perbandingan Performa Algoritma XGBoost, CatBoost Dan GBM Dalam Prediksi Penyakit Kardiovaskular Panwasto Samosir P; Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7552

Abstract

Cardiovascular disease remains the primary cause of mortality globally, encompassing conditions affecting the heart and blood vessels, such as hypertension and coronary artery disease. Risk factors include unhealthy lifestyle habits and immutable factors like age and family history. To tackle the challenges in early detection and prediction of cardiovascular disease, machine learning techniques, especially boosting algorithms, have emerged as promising tools. This study evaluates the performance of three prominent boosting algorithms: XGBoost, CatBoost, and Gradient Boosting—using publicly available datasets to predict cardiovascular disease risk. The findings reveal that CatBoost surpasses the other models with an accuracy of 75%, a Precision of 0.83, and a ROC AUC of 0.81, highlighting its exceptional predictive capabilities. Gradient Boosting achieves 70% accuracy with a well-balanced Recall and Precision, whereas XGBoost records the lowest performance with 63.3% accuracy across all metrics. These results position CatBoost as the most effective model for cardiovascular disease risk prediction.
Pengembangan Cash Management System (CMS) Berbasis Web Berbasis Cash Processing Center (CPC) di PT. XYZ Andriana Suhendi; Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8612

Abstract

This research aims to develop a web-based financial management system to optimize cash management at PT. XYZ. The background of this research is based on the importance of utilizing technology to support the operations of cash management service companies, such as PT. XYZ, which handles Cash In Transit (CIT) and Cash Management. With the rapid advancement of technology, an application is needed that can simplify cash management processes, improve efficiency, and reduce the risk of errors in recording daily transactions. The research methodology used is qualitative, involving active participation, observation, interviews, and documentation. The researcher directly participated in the application development process with the CPC division for 3 months, observing workflows and system requirements, and discussing with employees and company management. Interviews were conducted to understand managerial needs, while observations focused on monitoring existing cash management operations as the basis for developing a new system. The research results show that the development of the web-based financial management system successfully optimized the recording of cash inflows and outflows, facilitated customer data management, and ensured operational efficiency.

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