Journal of Computer Science and Informatics Engineering (J-Cosine)

Implementation of Static Routing with Path Redundancy on LoRa SX1276 and ESP32 Based on Graph Theory

Tue, 30 Jun 2026 14:54:30 +0800

The rapid development of the Internet of Things (IoT) has increased the demand for energy-efficient wireless communication systems and others under various environmental conditions. LoRa (Long Range) technology has attracted considerable attention due to its ability to support long-distance transmission with low power consumption. However, most existing LoRa network implementations rely on single-hop or non-redundant statistical routing to the gateway point, making them vulnerable to link degradation or node failure. This study enhances the statistical routing mechanism with path redundancy implemented on an ESP32 microcontroller and an SX1276 LoRa transceiver module to improve communication transmission. The network topology is modeled using graph theory, where each node is represented as a vertex and communication paths are represented as weighted edges based on the Received Signal Strength Indicator (RSSI). The primary and backup routes are derived from the minimum-weighted path in the graph. Experimental results show that incorporating path redundancy improves the packet delivery ratio (PDR) and maintains communication continuity during primary path quality degradation. The proposed approach demonstrates the feasibility of a reliable multi-hop LoRa network using statistical routing with graph-based path redundancy.

Design of Inpatient Module for Hospital Management Information System (Case Study: Mataram University Hospital)

I Gede Restu Astikadeni, Ida Bagus Ketut Widiartha, Dwi Ratnasari — Tue, 30 Jun 2026 14:55:04 +0800

Advances in information technology have encouraged healthcare services to adopt Hospital Management Information Systems (HMIS) to improve operational efficiency. However, the inpatient module at Mataram University Hospital still has issues such as irrelevant form content, a complex interface, and inefficient navigation. This study aims to design a prototype of the inpatient module using the User-Centered Design (UCD) method. UCD places users at the center of the design process and ensures that the system addresses actual needs. The development process followed the UCD stages, including understanding the context of use, specifying user requirements, producing design solutions, and evaluating the design iteratively through wireframes and prototypes. Evaluation was conducted using the User Experience Questionnaire (UEQ), and the system achieved high scores in five out of six aspects: Attractiveness, Clarity, Efficiency, Stimulation, and Novelty. These results indicate that the prototype offers a positive user experience and is suitable for future implementation and development.

Design of Hospital Management Information System Pharmacy Module Using Total Architecture Synthesis Method (Study Case: University of Mataram Hospital)

M. Irfan Ramdhani, Dwi Ratnasari, Santi Ika Murpratiwi — Tue, 30 Jun 2026 14:56:23 +0800

This study aims to optimize the hospital pharmacy process at
the University of Mataram Hospital by designing a user
friendly interface for the pharmacy module within the Hospital
Management Information System (HMIS). In this research, a
combined methodology of Total Architecture Synthesis (TAS)
and User-Centered Design (UCD) was employed to ensure both
a structured system architecture and a design that meets user
needs. For the data collection observations, staff interviews,
and literature reviews, which revealed several limitations and
confusions in the existing system. The redesigned pharmacy
module facilitates structured pharmacy workflow management
and supports accurate data reporting. Usability evaluation
using the User Experience Questionnaire (UEQ) yielded
positive results across all dimensions, with “Stimulation”
scoring highest at 1.68 on scale from -3 to +3. These findings
indicate that the proposed system design is functional,
engaging, and user-friendly, offering improved efficiency and
usability in pharmaceutical service operations.

Key words: Hospital Management Information System,
Total Architecture Synthesis, Pharmacy Module.

Development of Data Bank Application at the Agriculture and Plantation Service of West Nusa Tenggara Using the Prototyping Method

Agus Gunawan, Dwi Ratnasari, Sri Endang Anjarwani — Tue, 30 Jun 2026 14:57:22 +0800

This research aims to develop a Data Bank application that can assist Information and Documentation Management Officers (PPID) in providing public information services more quickly, accurately, and in a structured manner. This application is designed to address the problem of data management spread across various sectors, as well as delays in delivering information to the public. Development was carried out using a prototyping method, with direct user involvement in the design, testing, and evaluation processes of the system. The developed Data Bank application has several key features, including: storing and searching agricultural commodity data by category and year, data visualization in the form of interactive graphs, grouping data by district/city area, and an integrated information management system across sectors. This application is also equipped with data export, graph printing, and dynamic filtering features to facilitate information access by PPID officers and the public. System testing covered six aspects: functionality, performance, security and reliability, user interface (UI), user experience (UX), and its impact on PPID officer performance. The results showed that the Data Bank application successfully met all tested aspects with a user satisfaction level reaching 89%. This application is able to accelerate the process of providing information, improve work efficiency, and facilitate data storage and retrieval. Thus, the Data Bank application has been proven to support improving the quality of public information services within the NTB Agriculture and Plantation Service.

Design Of Web-Based Electronic Medical Record System Using User-Centered Design Method (Case Study: University of Mataram Hospital)

Khairunnisa Fitriani, Santi Ika Murpratiwi, Moh. Ali Albar — Tue, 30 Jun 2026 14:57:57 +0800

The growing demand for integrated and secure medical documentation has driven hospitals to adopt Electronic Medical Record (EMR) systems. This study focuses on the design of a web-based EMR system for the University of Mataram Hospital using the User-Centered Design (UCD) approach. The design aims to overcome the limitations of existing systems, particularly those related to hospital workflows that hinder usability and adaptability. The EMR module is envisioned as part of a microservice-based Hospital Information System, integrating key functionalities such as patient data management, medical history tracking, referral and control letter generation, and dashboard summaries. The development process includes user analysis, UML modeling, and UI/UX prototyping. The resulting prototype is evaluated using the User Experience Questionnaire (UEQ) with direct feedback from hospital staff. The final system is expected to be better aligned with the operational workflows of University of Mataram Hospital, offering improved integration, usability, and adaptability compared to the previous system.

Clean Architecture Implementation for Android Application Rinjani Visitor

Tue, 30 Jun 2026 15:01:06 +0800

The growing complexity of mobile applications necessitates scalable architectural solutions to streamline maintenance and feature expansion. This study presents the implementation of Clean Architecture in the Rinjani Visitor Android application, an ecotourism platform for Mount Rinjani National Park, Indonesia. The application integrates local tourism services while addressing challenges in code maintainability. Leveraging Flutter and Dart, the architecture is structured into three decoupled layers (presentation, domain, and data) to isolate business logic from technical implementation. The methodology encompasses requirement analysis, feature-based code structuring, and user acceptance testing via black-box methods. Results demonstrate successful layer integration, with all 21 tested functionalities adhering to specifications. The modular design simplifies collaborative development, reduces code interdependency, and supports agile enhancements. The study advocates periodic architectural reviews to sustain these benefits amid evolving technological demands.

Data Augmentation Optimization for Indonesian Batik Motif Classification Using Transfer Learning on Convolutional Neural Network

Tue, 30 Jun 2026 14:55:45 +0800

Batik is an Indonesian cultural heritage that has various distinctive motifs from various regions. However, manual classification of batik motifs often requires special skills and considerable time.1 To overcome this, the Convolutional Neural Network (CNN)-based classification method with the help of transfer learning is a promising solution. This research uses a dataset of 500 batik images consisting of 10 different motif classes, each class containing 50 images. Data augmentation techniques are applied to expand the variety of training data with transformations such as rotation, zoom, and flipping to reduce overfitting and improve the generalization ability of the model. The MobileNetV2 model was selected as the base model of transfer learning due to its efficiency and ability to extract features from limited data. Experiments show that the MobileNetV2 model with fine-tuning produces the highest classification accuracy of 86%, which is superior to conventional CNNs that achieve accuracies between 59% and 65%. As a result, the combination of data augmentation and transfer learning in MobileNetV2 proved to be effective in improving the accuracy and efficiency of batik motif classification on small data.

Fish Image Classification Based on Feature Extraction Using Convolutional Autoencoder and CNN Classifier

Destia Suhada, Qalbi Ala Dinika, Muhamad Firdaus, I Gede Pasek Suta Wijaya — Tue, 30 Jun 2026 14:58:46 +0800

Automatic fish species classification is crucial for supporting fisheries management, particularly in coastal regions such as Lombok. This study proposes an artificial intelligence-based approach combining a Convolutional Autoencoder (CAE) as a feature extractor and a Convolutional Neural Network (CNN) as a classifier. The dataset consisted of 9,000 colored images from nine fish species, which were converted to grayscale. Experimental results show that the CAE+CNN model achieved a classification accuracy of 95.94% within 0.87 seconds for 1,800 images. In comparison, the pure CNN model achieved a higher accuracy of 99.05% and faster performance, but with a much larger model size and complexity. The CAE+CNN approach is considered more lightweight and efficient, making it suitable for deployment on resource-constrained devices. This technology has strong potential for local implementation to support the digital transformation of the fisheries sector and assist fishing communities in automatically identifying fish species, improving efficiency, and reducing dependency on manual classification by experts.

Vehicle License Plate Number Detection with YOLO11

Lalu Mischa Khalqin Adhiguna, Ario Yudo Husodo, Fitri Bimantoro — Tue, 30 Jun 2026 15:00:00 +0800

Automatic License Plate Recognition (ALPR) systems are crucial for automating vehicle monitoring and access control, especially in parking management. However, many existing ALPR methods rely on earlier detection models and perform poorly under low-light or night-time conditions. This study addresses that issue by developing a license plate detection and recognition system using the You Only Look Once (YOLO11) algorithm and PaddleOCR (Optical Character Recognition) for character recognition. The objective is to evaluate the performance of this approach on Indonesian two-wheeled vehicles under varied lighting conditions. The dataset includes daylight and low-light images, divided into training, validation, and test sets. YOLO11 was trained with several epoch configurations, and the best model achieved 99.8% precision, 100% recall, 99.5% mAP@50, and 80.7% mAP@50–95. The model was then integrated with PaddleOCR to extract license plate text. Results show this combination is effective for real-time vehicle identification and can improve parking queue management.

Provincial Segmentation Based on K-Medoids Clustering for Risk Factor Mapping and Targeted Intervention of Stunting in Indonesia

Dina Eka Putri, Syifa Salsabila Satya Graha, Baiq Fitria Rahmiati — Tue, 30 Jun 2026 14:54:03 +0800