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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 8 Documents
 1 
Search results for , issue "Vol 8 No 3 (2025): November" : 8 Documents clear
Redesain UI/UX Sistem Pemesanan Menu Menggunakan Metode Design Thinking Falaq, Zilva Nur Fajrul; Sharazita Dyah Anggita; Wulandari, Irma Rofni; Baita, Anna
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.v8i3.9457

Abstract

This study aims to design and develop the user interface (UI) and user experience (UX) of a web-based menu ordering system using the Design Thinking method. The development process includes the stages of empathize, define, ideate, prototype, and testing to ensure that the final solution aligns with user needs. The prototype was further refined into a frontend design emphasizing ease of navigation, clarity of information, and efficiency of the ordering flow. Usability evaluation was conducted using the System Usability Scale (SUS). The assessment results show a SUS score of 70, in the Good category in the adjective rating. This indicates that the system design is acceptable to users and can be further developed into a complete frontend implementation. Overall, the Design Thinking approach proved effective in producing an intuitive, functional, and user-centered interface design.
Penerapan Metode Gamma Correction dan MobileNet Untuk Klasifikasi Citra Daun Purba, Mariana; Ayumi, Vina; Rahayu, Sarwati; Salamah, Umniy; Handriani, Inge; Ani, Nur
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.v8i3.9459

Abstract

This study proposed an enhanced leaf image classification model by integrating gamma correction as a preprocessing technique with the MobileNet (MNET) architecture to improve visual feature extraction. The dataset consisted of 750 images representing five classes of medicinal plants, namely Psidium guajava, Syzygium polyanthum, Piper betle, Annona muricata, and Andrographis paniculata, obtained from personal documentation, online sources, and public datasets. Gamma correction was applied to adjust illumination and enhance leaf texture clarity, followed by resizing and normalization processes. Data augmentation was performed using rotation, contrast adjustment, horizontal and vertical flipping, brightness adjustment, and channel shifting to increase training data variation. The MobileNet architecture was expanded with additional layers, including global average pooling, flatten, Dense–ReLU, and Dense–softmax, enabling it to function as an efficient feature extractor and classifier. Experiments were conducted using a batch size of 32, 50 epochs, the Adam optimizer, and a learning rate of 0.0001. The combined MNET and gamma correction model achieved a training accuracy of 99.00%, a validation accuracy of 87.50%, and a testing accuracy of 84.16%.
Dashboard Analitik Human Capital Development Plan Jabatan Fungsional Manajemen ASN Cahyaningsih, Elin; Amani Rizki Ananda, Inarotul; Jihan Ramadhani, Putri; Saputro, Budi; Hidayati, Rosanti
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.v8i3.9618

Abstract

The human capital development plan analytics dashboard is a business intelligence tool designed to assist in policy-making and decision-making in the development of competencies and careers of functional officials in ASN management, aiming to improve the quality of ASN management services. The value proposition canvas approach is used to identify problems and analyze user needs to enhance the success of user-based technology implementation. Business intelligence visualization is implemented using the Laravel and Node.js framework with JavaScript programming. Research results show that there are 4 thematic HCDP analytics dashboards, namely HCDP statistics, competency values, competency achievements, and recommendations for the development of functional management ASN position competencies. Future research challenges include improving the availability, accuracy, and integration of technical competency mapping data. However, this dashboard provides a strategic basis for formulating business intelligence recommendation models that will support the sustainable development of JFMASN competencies.
Implementasi Aplikasi EIGHT untuk Pengembangan Kompetensi Aparatur Sipil Negara Nurmadewi, Dita; Cahyaningsih, Elin; Agrandis, Nanda; Ardiansyah, Munif; Saputro, Budi
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.v8i3.9619

Abstract

EIGHT (Academy for Government Human Capital Management Specialist) is a technology-based knowledge management system (KMS) designed to improve the management of the competency of the State Civil Apparatus (ASN). In an effort to support bureaucratic reform and improve public service quality, EIGHT integrates the Learning Management System (LMS) and Knowledge Management System (KMS), allowing ASN to access training materials, regulations, and important documents through an easily accessible digital platform. The Agile Software Development Life Cycle (SDLC) approach was chosen in the development of this system to ensure flexibility, rapid iteration, and responsiveness to user feedback. The research findings indicate that the EIGHT platform has successfully provided an efficient solution for managing ASN competencies, with easy access through the Single Sign-On (SSO) feature. Although some challenges remain in terms of integration and continuous updates, this system shows great potential in supporting the improvement of ASN professionalism and strengthening the culture of continuous learning within government organizations. This system is expected to become a model for the development of technology-based systems in the public sector in the future.
Strategi Profiling ASN: Peningkatan Kualitas Data ASN menuju Data-Driven Manajemen Talenta Ibrahim, Andi; Cahyaningsih, Elin; Berly, Alfonsius
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.v8i3.9634

Abstract

The State Civil Service Agency (BKN) implements the ASN data quality index (IKD) as an integrated instrument to ensure the quality of national ASN personnel data. The dimensions of ASN data quality are completeness, timeliness, accuracy, and consistency. These dimensions provide a comprehensive picture of the quality of ASN Profile Data and serve as the basis for coaching, standardization, and improvement of data governance. The implementation of IKD maintains the integrity of civil service data and enables more accurate and evidence-based ASN talent planning and management processes. This study uses a gap analysis approach to formulate ASN profiling strategies through improving the quality of ASN data. Gap analysis is able to map the differences between actual and ideal conditions objectively so that organizations can set targeted and strategic improvement priorities. The results of the study show that there are four strategic recommendations, namely the formulation of ASN data quality regulations, the structuring of ASN data quality development services and business processes, ASN data quality assistance and development, and the development of services in a shared digital platform.
Model Implementasi Firewall MikroTik dalam Pengelolaan Trafik dan Keamanan Jaringan Dachi, Abraham Cornelius; Noprisson, Handrie
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.v8i3.9777

Abstract

This study aims to analyze the role of firewall implementation on MikroTik RouterOS in improving network security and traffic management at CV. Prima Dinamika Mandiri. The company’s network infrastructure consists of an internet service provider router (ISP router), a MikroTik router functioning as the main firewall and gateway, network switches for LAN distribution, multiple access points across work areas, client devices such as laptops, PCs, and servers, as well as shared printers in each office room. The firewall was implemented through several configurations, including filtering rules, brute-force protection, Layer 7 filtering, Network Address Translation (NAT), and Quality of Service (QoS), with the objective of minimizing security threats and optimizing network traffic distribution. The evaluation results demonstrate a significant improvement in network performance, as indicated by the increase in throughput from 90 Mbps to 105 Mbps, the reduction of latency from 20 ms to 15 ms, and the decrease in packet loss from 3% to 0.5%. These findings confirm that the implementation of a MikroTik-based firewall enhances network security, stability, and reliability in supporting the company’s operational activities. Further development opportunities include the integration of Intrusion Detection and Prevention Systems (IDS/IPS) and VLAN segmentation.
Efek Hyperparameter Tuning pada VGG16 Untuk Klasifikasi Citra Batik Basurek Bengkulu Ayumi, Vina
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.v8i3.9778

Abstract

This study aimed to analyze the effect of combining hyperparameters, namely optimizer and batch size, on the performance of the VGG16 model in classifying Batik Basurek images. The dataset consisted of 250 images divided into five motif classes, with 50 images in each class. The data were split into training, validation, and testing sets with proportions of 70%, 15%, and 15%, respectively. The study employed a transfer learning approach using the VGG16 model, with hyperparameter variations including the RMSProp, Adam, and SGD optimizers, as well as batch sizes of 16, 32, and 64. The results showed that the Adam optimizer consistently delivered the best accuracy performance across all testing scenarios. The optimal performance was achieved using the combination of Adam and a batch size of 32, yielding a training accuracy of 97.55%, validation accuracy of 93.25%, and testing accuracy of 92.80%. Meanwhile, RMSProp demonstrated reasonably good performance but remained below Adam, and SGD produced the lowest accuracy across all evaluation stages. In terms of batch size, a batch size of 32 provided the most stable and accurate performance, whereas a batch size of 64 tended to reduce the model’s generalization capability. Therefore, the combination of Adam and a batch size of 32 was identified as the most optimal hyperparameter configuration for Batik Basurek image classification using the VGG16 model.
Klasifikasi Citra Aksara Tradisional Kaganga Bengkulu Menggunakan Optimasi Arsitektur ResNet50 Ayumi, Vina
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.v8i3.9780

Abstract

This study aimed to analyze the performance of the ResNet50 model based on transfer learning in classifying 19 classes of Kaganga script, while also evaluating the effect of applying L1, L2, and dropout regularization techniques on the model’s generalization ability in minimizing overfitting. In addition, the study examined the impact of varying batch sizes (16, 32, and 64) on training stability and overall model performance. The experiments were conducted by freezing the initial layers of ResNet50 as a feature extractor and modifying the final layers for the classification task. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics on the test dataset. The results showed that all model configurations achieved high training and validation accuracy. However, the combination of L2 regularization with a batch size of 32 yielded the best performance with a testing accuracy of 86.10%, indicating the most optimal generalization capability compared to other configurations. Meanwhile, the use of batch size 64 resulted in a more noticeable decrease in accuracy, making it less effective for this dataset. These findings indicated that the appropriate selection of regularization techniques and batch size played an important role in improving training stability and classification accuracy for traditional script image recognition.

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