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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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Transformasi Digital, Media Sosial, dan Teknologi Informasi terhadap Daya Saing UMKM Mobil Bekas Kota Batam DESI RENATA SINURAT DESI; SURYA TJAHYADI; SYAEFUL ANAS AKLANI
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9268

Abstract

This study aims to analyze the influence of digital transformation, social media, and information technology on the competitiveness of used car MSMEs in Batam City. A quantitative research approach was employed with a sample of 89 respondents. The results of the classical assumption tests indicate that the data are normally distributed, as shown by the Kolmogorov–Smirnov Z value of 0.068 and a significance level of 0.200 (>0.05). The multicollinearity test results show no indication of multicollinearity, with Tolerance values greater than 0.10 and VIF values below 10 (X₁ = 1.685; X₂ = 1.062; X₃ = 1.614). The heteroscedasticity test results also confirm that the model is free from heteroscedasticity, as all independent variables have significance values greater than 0.05. The simultaneous F-test shows that digital transformation, social media, and information technology collectively have a significant effect on MSME competitiveness, with an F-value of 3.264 and a significance of 0.000 (<0.05). Partial t-tests indicate that all three variables have a positive and significant influence, with a significance value of 0.000. Digital transformation has the most dominant effect (B = 0.406, t = 2.908), followed by information technology (B = 0.306, t = 2.438) and social media (B = 0.066, t = 2.518). These findings demonstrate that implementing digital transformation supported by effective utilization of social media and information technology can enhance the competitiveness of used car MSMEs in Batam City by improving operational efficiency, service innovation, and digital marketing effectiveness.
Implementasi Metode Agile Development pada Pengembangan Aplikasi Pemantauan Tingkat Stres Kerja Berbasis Mobile Putra Afiqi; Sri Wulandari
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9299

Abstract

This study aims to develop the WARASPADA (Work Stress Awareness and Prevention Application Dashboard) as a mobile-based work stress monitoring tool using the Agile Development methodology, with the Flutter framework for the frontend and Supabase as the backend. The instrument used in this research is the Work Stress Diagnosis Survey (SDS) issued by the Indonesian Ministry of Manpower. System evaluation was conducted using the Technology Acceptance Model (TAM), which measures four key variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioral Intention to Use (BIU). The results show that the overall user acceptance level reached 85.4%, categorized as very good. This finding indicates that users perceive the application as easy to use, functional, and effective for detecting and monitoring work stress levels. Therefore, the WARASPADA application is considered feasible and has strong potential to be implemented in workplace environments as a data-driven decision-support tool for human resource management.
Implementasi Push Notification pada Aplikasi Antrean Pasien Berbasis Android Mohamad Styvani Hendi S; Tri Widodo, S.T., M.Kom
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9316

Abstract

This study aims to implement and evaluate the push notification feature in an Android-based patient queue application to improve service efficiency and user satisfaction. The research process includes several stages: needs analysis, system design, feature implementation, functional testing, and user satisfaction testing using the User Satisfaction method. Data were collected through a Likert-scale questionnaire covering five aspects: ease of use, interface design, response speed, information relevance, and overall satisfaction. The results show that the application successfully provides real-time queue information through push notifications, enhancing user convenience. The average user satisfaction rate exceeded 80%, with the highest score of 88% achieved in the information relevance aspect. Reliability testing using Cronbach’s Alpha obtained a coefficient value of 0.87, indicating that the questionnaire instrument was reliable and internally consistent. Therefore, the implementation of push notifications has proven effective in improving user experience and service efficiency in digital patient queue management.
Implementasi Token-Based Access Control dan Proteksi Konten pada Manajemen Pembelajaran Berbasis Web untuk Meningkatkan Keamanan Data di Institusi Pendidikan Medis Ahmad Sulaeman; Joko Aryanto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9374

Abstract

Digital content security is a critical aspect in the implementation of online learning systems, especially in medical education institutions that manage sensitive video data. This study aims to develop and evaluate a web-based video content protection system by integrating three main methods: AES-128 encryption, Token-Based Access Control, and Dynamic Watermarking. The AES-128 encryption method is applied to secure video files so that only authorized users can decrypt and access them, while Token-Based Access Control serves as a dynamic session-based authentication mechanism to prevent unauthorized access. In addition, Dynamic Watermarking embeds user identity information into each video playback to trace and deter illegal content distribution. Experimental results show a Token Validation Success Rate of 100% and an Intrusion Detection Rate of 90%, indicating that the system performs well in verifying authentication and detecting unauthorized access. Performance testing of the HTTP Live Streaming (HLS) process achieved an average response time of 1.79 seconds with 1000 successful requests and no failures, demonstrating that the additional security layers did not significantly degrade system performance. Overall, this study concludes that the integration of AES-128 encryption, Token-Based Access Control, and Dynamic Watermarking provides an effective multilayer security approach for protecting video-based learning content and strengthening data security in medical education environments.
Human Resource (HR) Performance Analysis Model in Higher Education Based on Multi-modal Data Using Machine Learning Reni Utami; Ari Hidayatullah; Anita Ratnasari
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9381

Abstract

This research aimed to develop a machine learning-based model for human resource (HR) performance analysis in higher education institutions using a multi-modal approach, combining static data and text data analysis. For static data analysis, the random forest (RF) algorithm was employed to assess HR performance based on attributes such as years of service, training attended, and performance evaluations. The dataset for this experiment consisted of 250 data points, which were divided into 70% for training, 10% for validation, and 20% for testing. The experimental results with the RF model showed high accuracy in training (90.4%), although there was a performance drop during validation and testing, with accuracies of 85.7% and 82.5%, respectively. For the text data, which contained feedback with negative, positive, and neutral sentiments, the CNN-BiLSTM model achieved an accuracy of 92.6% in training, despite a decrease in validation (87.4%) and testing (84.4%) accuracies. The text dataset comprised 1,000 data points, divided into 70% for training, 10% for validation, and 20% for testing. The study recommends the application of a multi-model approach to assess HR performance using the RF algorithm for static data and the CNN-BiLSTM model for more complex data in future research.
Sistem Blockchain Berbasis Authentication dan Traceability Untuk Produk Batik dan Tenun Lokal Kreatif Desi Ramayanti; Giri Purnama; Eriklex Donald; Eugene Feilian Putra Rangga; M. Julius Saputra; Prayoga Ade Pangestu
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9382

Abstract

This research aimed to develop a blockchain-based system to ensure product authentication and traceability for creative textile products, with a case study on local Batik and Weaving SMEs in Bandung City. The main issue addressed was the widespread counterfeiting of products and the lack of transparency in the supply chain, which harmed both artisans and consumers. To address this, a system architecture was designed that utilized Hyperledger Fabric as the private blockchain platform, Node.js for the backend server, and IPFS for off-chain data storage. So far, the research has reached a technology readiness level (TRL) of 4, where the system prototype was validated in a laboratory environment. Key components such as the registration and verification module for SMEs by administrators, the product data input system, and the mechanism for generating a unique QR code for each product were successfully implemented. The system was developed to enhance consumer trust, protect product authenticity, and increase the market value of local creative textile products.
Model Deteksi Kecurangan Ujian Menggunakan Pendekatan SURF-CNN Berdasarkan Data Citra Digital Uus Rusmawan; Imam Mulya; Muchamad Sandy; Abd Rahman; Pupu Ramadhan
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9383

Abstract

Detecting cheating during examinations was one of the main challenges in maintaining academic integrity in educational environments. This study developed an approach to detect cheating behavior by utilizing a combination of Speeded Up Robust Features (SURF) and Convolutional Neural Networks (CNN) based on digital images. The novelty of this research lay in the application of the SURF method as a feature extraction technique to detect suspicious objects and movements, which were then further analyzed using CNN for student behavior classification. The main objective of this study was to design, develop, and test a digital image–based exam cheating detection model capable of recognizing various types of behavior, such as looking around, glancing suspiciously, as well as non-cheating behaviors like focusing and boredom. The dataset used in this study consisted of 1,200 digital images categorized into six different behavior classes. The dataset was divided into three parts: 70% for training (840 images), 10% for validation (120 images), and 20% for testing (240 images). The experimental results showed that the SURF-CNN approach achieved better performance compared to the standard CNN. The SURF-CNN model achieved an accuracy of 91.80% on training data, 88.65% on validation, and 86.15% on testing, while CNN only achieved 88.20% on training, 85.25% on validation, and 83.15% on testing
Model Deteksi Berita Palsu Menggunakan BERT dan Bi-LSTM Berbasis Discriminative Approach Dwi Fitri Brianna; Paisal Paisal; M. Apreza Saputra; Muhammad Al Hapiz
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9384

Abstract

This study aimed to develop a text classification model for detecting hoaxes using a deep learning approach and text representation methods. The text data that had undergone preprocessing were then extracted using three approaches: Word2Vec, Doc2Vec, and Bidirectional Encoder Representations from Transformers (BERT). The research dataset consisted of 2,325 genuine news articles (label 0) and 2,287 fake news articles (label 1). In this study, BERT feature vectors with a dimension of 768 were combined with the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm to capture sequential dependencies in the text, along with the Support Vector Machine (SVM) algorithm as the final classifier. The training process was carried out on Dell Precision 7750 hardware using parameters of embedding dimension 128, 64 hidden units, a dropout rate of 0.3, and a learning rate of 0.001. Training and testing were conducted for 10 epochs with a batch size of 32. The results indicated that the Word2Vec and Bi-LSTM model achieved an accuracy of 87.4% with an F1-Score of 87.0%, while the Doc2Vec and Bi- LSTM model performed slightly lower with an accuracy of 85.6% and an F1- Score of 85.4%. The best performance was obtained by the BERT, Bi-LSTM, and SVM model, which achieved an accuracy of 93.8%, precision of 94.1%, recall of 93.5%, and an F1-Score of 93.7%.
Klasifikasi Penyakit Tanaman Sawit Berbasis Hybrid Deep Learning Menggunakan U-Net dan ResNet- Fakhri Lambardo; Putri Maharani; Putri Andromeda; Firga Abel Astiawan
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9385

Abstract

Oil palm (Elaeis guineensis) productivity was frequently constrained by foliar diseases, which were often difficult to detect at an early stage using conventional visual inspection methods. To address this challenge, the present study proposed a hybrid deep learning framework for automated oil palm leaf disease detection. A dataset comprising 1,200 oil palm leaf images, equally distributed across three disease classes (400 images per class), was utilized. The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets, with training and validation data obtained from public repositories, while testing data were collected directly to ensure model generalizability. The proposed hybrid architecture combined U-Net for precise leaf lesion segmentation, ResNet-50 as a deep feature extractor to capture high-level discriminative representations. U-Net segmentation enabled isolation of infected regions, while ResNet-50 provided robust feature embeddings that enhanced separability between visually similar disease classes. Experimental evaluation demonstrated that the baseline U-Net + SVM approach achieved an accuracy of 84.2%, precision of 82.5%, recall of 83.1%, and F1-score of 82.8%. In contrast, the hybrid U-Net + ResNet-50 + SVM method yielded superior results with 91.6% accuracy, 90.8% precision, 91.2% recall, and 91.0% F1-score, reflecting an improvement of approximately 7.4%.
Pemodelan Topik Berdasarkan Dokumen Penelitian Bidang Ilmu Komputer Menggunakan Text Mining Bakhtiar Bakhtiar; Azhar Andika Putra; Muhammad Al Hapiz; Firga Abel Astiawan
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9387

Abstract

This study aimed to develop a document clustering model using a combination of the IndoBERT model and the K-Means algorithm to group research abstracts in the field of computer science and technology. The data used consisted of 1000 research abstracts, divided into two parts: 80% for training data (800 abstracts) and 20% for testing data (200 abstracts). The IndoBERT model was used to represent the abstracts as embedding vectors, which were then processed with the K-Means algorithm to form 10 topic clusters, including artificial intelligence, computer systems and networks, programming, cybersecurity, and others. The training experiment used the training data to generate clusters and centroids for mapping new documents into the appropriate clusters. Evaluation was carried out using several metrics, including accuracy, cluster homogeneity, Davies-Bouldin Index, and Silhouette Score. The testing results showed that the developed model achieved an accuracy of 85%, indicating good performance in clustering the test data. The cluster homogeneity value of 0.90 indicated that documents that should belong to the same cluster were grouped together effectively. The Davies-Bouldin Index value was 0.34, while the Silhouette Score was 0.76.

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