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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 561 Documents
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Rancang Bangun Sistem Informasi Pemetaan dan Pemantauan Daerah Rawan Banjir di Bengkulu Dengan Metode Overlay Yusa Virginiawan Guntara; Yulia Darmi; Muntahanah; Rojali Toyib; Anita Septiani Putri
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10687

Abstract

Setiap dalam musim penghujan kota Bengkulu merupakan salah satu wilayah yang memiliki tingkat kerawanan tinggi terhadap bencana banjir dengan intensitas curah hujan yang tinggi. Tujuan dari penelitian untuk memetakan daerah rawan banjir di Kota Bengkulu menggunakan sistem informasi geografis dengan metode overlay yang dilakukan dengan menggabungkan beberapa parameter yang berpengaruh terhadap banjir, yaitu kemiringan lereng, curah hujan, penggunaan lahan, tekstur tanah, kemiringan lereng. Parameter yang digunakan tingkat kontribusinya yang sesuai terhadap risiko banjir. Tahapan proses yang dilakukan seperti melalui tahapan pengolahan data ,pengumpulan data, implementasi, sampai peta akhir yang disusun untuk menggambarkan dari zona yang dianggap rawan banjir. Penelitian ini menghasilkan pemetaan dan pemantauan daerah yang rawan banjir sehingga penulis mengharapkan dapat menjadi acuan atau pedoman untuk masyarakat dan pemerintah daerah dalam menyusun kebijakan mengurai resiko, dampak dari suatu bencana banjir dan perencanaan tata ruang yang untuk menyesuaikan diri secara cepat, efektif, dan proaktif terhadap perubahan bencana banjir.
Analisis Robustness Model Deep Learning pada Klasifikasi Sampah Non-Organik terhadap Variasi Pencahayaan dan Noise Citra Erwin Dwika Putra; Marissa Utami
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10791

Abstract

The increasing amount of non-organic waste presents challenges in effective waste management and sorting processes. Deep learning technology has been widely used for image-based waste classification; however, most previous studies mainly focused on improving model accuracy under ideal conditions without considering model robustness against visual disturbances in real-world environments. This study aims to analyze the robustness of deep learning models for non-organic waste classification under illumination variations and image noise using the public TACO (Trash Annotations in Context) dataset. Three deep learning models were employed, namely EfficientNet-B0, ResNet50, and MobileNetV2. The experiments were conducted by applying multiple brightness levels and Gaussian noise disturbances. The results showed that EfficientNet-B0 achieved the best performance with an accuracy of 87.84%, precision of 87.17%, recall of 85.35%, and F1-score of 85.39%. Furthermore, EfficientNet-B0 obtained the highest robustness score of 0.823 compared to ResNet50 and MobileNetV2. The findings indicate that illumination variations and image noise significantly affect model performance, especially under severe visual disturbances.
Pengembangan Metode Deep Metric Learning Berbasis Attention untuk Verifikasi Tanda Tangan Offline pada Lingkungan Data Terbatas Marissa Utami; Erwin Dwika Putra
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10792

Abstract

This study aims to develop an attention-based Deep Metric Learning method for offline signature verification in environments with limited data availability. The main challenge in offline signature verification lies in the high intra-class variation and the similarity between genuine and forged signatures, particularly when the amount of training data is limited. The proposed method employs a Siamese Convolutional Neural Network architecture combined with an attention mechanism to enhance discriminative feature extraction capabilities. The dataset used in this study was obtained from offline sources and Kaggle, consisting of genuine and forged signature images. The research process includes preprocessing, signature pair generation, feature extraction, embedding generation using Deep Metric Learning, and optimization using Contrastive Loss. Experimental results demonstrate that the proposed method achieved an Accuracy of 91.12%, Precision of 92.27%, Recall of 92.43%, F1-score of 90.75%, and an Equal Error Rate (EER) of 4.88%. These results indicate that the integration of the attention mechanism and Deep Metric Learning effectively improves the system's capability to recognize signature patterns under limited data conditions.
Perbandingan K-Means, Hierarchical Clustering Dan K-Medoids Untuk Segmentasi Pasar Berdasarkan Evaluasi Silhouette Score Denny Ganjar Purnama; Safrizal; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10849

Abstract

This study compares the performance of K-Means, Agglomerative Hierarchical Clustering, and K-Medoids algorithms for market segmentation using PT XYZ sales data. The dataset consists of the Quantity and Expected Revenue attributes and was processed through data cleaning, currency-to-numeric conversion, invalid data removal, logarithmic transformation, and standardization, resulting in 923 valid records. Clustering was performed using three clusters, representing Low Value, Mid Value, and High Value customer segments. Performance was evaluated using the Silhouette Score, where K-Means achieved 0.4805, Agglomerative Hierarchical Clustering 0.4808, and K-Medoids 0.4840. Although the performance differences among the algorithms were relatively small, K-Medoids achieved the highest score and was therefore selected as the final model. The resulting segmentation consisted of 188 Low Value customers (20.37%), 469 Mid Value customers (50.81%), and 266 High Value customers (28.82%). These findings indicate that K-Medoids provides the best clustering quality while offering greater interpretability through medoid-based cluster centers representing actual data objects. The proposed segmentation can support companies in developing differentiated marketing strategies for low-, medium-, and high-value customer segments.
Penerapan Metode Ekstraksi Fitur Geometris, Hog, dan Hu Moment Pada Citra Tanda Tangan Digital Menggunakan Support Vector Machine (SVM) Muhammad Hikmal Febrian; Erwin Dwika Putra; Ardi Wijaya; Muntahanah
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10910

Abstract

The increasing use of electronic documents has heightened the need for fast, accurate, and objective digital signature verification systems. This study proposes a digital signature recognition system by combining Geometric Features, Histogram of Oriented Gradients (HOG), and Hu Moment feature extraction with a Support Vector Machine (SVM) classifier using the Radial Basis Function (RBF) kernel. A dataset of 500 signature images from 50 individuals was divided into training, validation, and testing sets using an 80:10:10 ratio. The proposed workflow includes image preprocessing, feature extraction, feature vector construction, model training, and evaluation using a confusion matrix. Experimental results show that the combined feature extraction methods effectively represent both global and local signature characteristics. The proposed model correctly classified 46 of 50 testing samples, achieving 92.00% accuracy, 88.00% precision, 92.00% recall, and an 89.33% F1-score, demonstrating its effectiveness for automatic digital signature recognition and electronic document authentication.
Pemodelan Isu Dan Sikap Publik Terhadap Program Makan Bergizi Gratis Menggunakan Arsitektur Hibrida BERTopic Dan IndoBERT Bayu Tri Nugroho; Hermawan Arief; Avianto Donny; Risnanto Ari
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10555

Abstract

The Free Nutritious Meal (MBG) program is a large-scale social policy that has generated extensive public discussion on YouTube. However, existing public opinion analyses primarily rely on sentiment analysis, which often fails to distinguish operational criticism from fundamental opposition to the policy. This study proposes a hybrid machine learning pipeline to analyze discussion topics and public stance using 5,509 preprocessed YouTube comments. Topic modeling was performed using BERTopic with the paraphrase-multilingual-MiniLM-L12-v2 embedding model, UMAP, and HDBSCAN, while stance classification was conducted by fine-tuning the IndoBERT-base-p1 model. The results identified 19 coherent topics with a C_v coherence score of 0.4918. The fine-tuned IndoBERT achieved an accuracy of 70.00% and a Macro F1-score of 0.7002. Oppositional stances dominated the discussions (50.0%), particularly on food safety concerns (91.1%) and allegations of project corruption (86.1%). In contrast, supportive opinions (22.2%) primarily focused on the program's nationwide equity and social welfare objectives. These findings suggest that strengthening kitchen hygiene standard operating procedures (SOPs), enhancing budget transparency, and improving public communication are critical to increasing public trust and supporting the effective implementation of the MBG program.
Analisis Sentimen Ulasan Instagram Menggunakan Algoritma Support Vector Machine dan Random Forest (Studi Kasus: Universitas Dian Nusantara) Giri Purnama
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.11045

Abstract

The increasing use of social media, particularly Instagram with over 1 billion active users in 2023, creates opportunities for Universitas Dian Nusantara (UNDIRA) to understand public perception through user review analysis. This study aims to develop a machine learning-based sentiment analysis model to categorize UNDIRA Instagram user reviews into positive, negative, and neutral sentiments. The novelty of this study lies in the combined use of TF-IDF and Word2Vec features together with a systematic comparison of SVM and Random Forest on Indonesian-language review data, a context still rarely examined for higher-education institutions. The research involved data collection through Instagram scraping, data preprocessing including stop word removal and stemming, and the application of three machine learning models: Support Vector Machine (SVM) with TF-IDF feature extraction, Random Forest (RF) with Word2Vec, and RF with TF-IDF. Results indicate that SVM with TF-IDF achieved the best performance with 99.11% accuracy and 99.13% F1-Score, outperforming Random Forest at 96.43% accuracy
Pengaruh Learning Rate dan Regularisasi pada Kinerja ResNet50 untuk Klasifikasi Motif Kain Tenun Palembang Hadiguna Setiawan; Sri Dianing Asri
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.11058

Abstract

Palembang woven fabric is one of Indonesia's cultural heritages, characterized by diverse motifs with distinctive visual patterns that require accurate identification methods to support cultural preservation and digitalization. This study aims to analyze the effect of learning rate and regularization techniques on the performance of a transfer learning-based ResNet50 model for Palembang woven fabric motif classification. The dataset consisted of 800 images representing eight motif classes, divided into 70% training, 15% validation, and 15% testing sets. All images underwent preprocessing, including resizing to 224 × 224 pixels and normalization. The ResNet50 model was trained using the Adam optimizer for 50 epochs with three learning rates (0.01, 0.001, and 0.0001) and two regularization techniques, namely L2 Regularization and Dropout. Model performance was evaluated using Accuracy, Precision, Recall, and F1-Score, while training and validation curves were analyzed to assess model convergence. The experimental results demonstrate that both learning rate and regularization techniques significantly influence classification performance. The best performance was achieved using a learning rate of 0.001 with Dropout, resulting in a training accuracy of 99.11%, validation accuracy of 96.67%, and testing accuracy of 95.83%, outperforming all other configurations.
Analisis Perbandingan Algoritma Machine Learning dan Contrast Enhancement untuk Klasifikasi Motif Batik Besurek Mariana Purba
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.11059

Abstract

Batik Besurek is one of the cultural heritages of Bengkulu Province that has unique motif characteristics, requiring support from digital technology for its preservation and recognition. This study aims to compare the performance of six machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), in classifying five Batik Besurek motifs. Furthermore, this study analyzes the effect of applying contrast enhancement methods, including Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction (GC), on improving the performance of the best-performing algorithm. The dataset consists of 500 Batik Besurek images representing five motif classes, namely Kaligrafi, Rafflesia, Burung Kuau, Relung Paku, and Rembulan. All images undergo preprocessing, are transformed into one-dimensional vectors (flatten), and are divided using the hold-out validation method with an 80% training data and 20% testing data ratio. The experimental results show that SVM achieves the best performance compared to other algorithms, with a training accuracy of 86.75% and a testing accuracy of 81.00%. The application of CLAHE on SVM improves the training accuracy to 87.50% and testing accuracy to 82.00%.
Model Replikasi Data Low-Cost Berbasis Rsync-Syncthing untuk Mitigasi Kehilangan Data pada UMKM Imam Mulya; Desi Ramayanti; Geri Ramadansyah
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.11149

Abstract

Micro, Small, and Medium Enterprises (MSMEs) generally store operational data on a single device without a structured backup mechanism, making them vulnerable to permanent data loss caused by hardware failure, user error, or cyberattacks. This study aimed to design and validate a low-cost data replication model based on the open-source software Rsync and Syncthing as a data loss mitigation solution that MSMEs can implement independently. The study employed a quantitative experimental approach following the Network Development Life Cycle (NDLC) framework. The tests utilized the real-world UCI Online Retail transaction dataset (531,282 rows), restructured into three MSME operational file profiles (retail, culinary, services) and evaluated under four scenarios: initial sync, incremental update, 512 KB/s network limitation, and failure simulation. The results showed that Rsync completed the initial sync in 0.035–0.195 seconds and incremental updates in 0.049–0.139 seconds; the many-small-files overhead was empirically confirmed, with the retail profile throughput 11.6 times lower than the services profile; the delta-transfer mechanism saved up to 90% of bandwidth; under a limited network, both tools converged at the bandwidth limit (±4 Mbps); and the Recovery Time Actual (RTA) reached 0.028–0.366 seconds with 100% data integrity verified by MD5 checksum.

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