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Yaddarabullah
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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year) This Journal accepts research articles in these following fields: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 187 Documents
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Application of the Simple Additive Weighting Method in the Decision Support System for Determining the Best Village Officials Qubro, Baiq Ainurrahmi Imanda; Saikin, Saikin; Fahmi, Hairul
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2555

Abstract

Performance evaluation of village officials in Bunut Baok Village is still carried out manually using assessment sheets, which often leads to subjectivity, unclear assessment aspects, and slow decision-making. These issues indicate the need for a Decision Support System (DSS) capable of providing objective and transparent evaluations based on measurable criteria. This study aims to develop a DSS using the Simple Additive Weighting (SAW) method to determine the best-performing village officials. Data were collected through observation and interviews with the Village Head and Village Secretary, involving 13 village officials as evaluation subjects. The dataset consists of five assessment criteria attendance, daily activity reports, output of activities, discipline, and service each represented through a linguistic scale (excellent, good, fairly good, poor) which was then converted into numerical weights for SAW processing. The results show that alternative A1 (Head of Gelogor Mapong Region) achieved the highest preference score of 0.938, indicating superior performance based on all evaluated criteria. The findings demonstrate that the SAW method effectively supports structured and transparent decision-making at the village governance level and can serve as a reference framework for future DSS implementations in local government environments.
Comparative Analysis of Machine Learning Methods in Predicting Diabetes Risk Based on Genetic Data Kusumaningrum, Sekar Ayu Wijaya; Soleh, Oleh; Yusup, Muhamad
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2486

Abstract

Type 2 Diabetes Mellitus (T2DM) is a global chronic disease caused by the interaction of genetic and environmental factors. The use of genetic data offers great potential for early detection and personalized intervention. However, the complex analysis of genetic data requires sophisticated approaches like machine learning. This study aims to compare the performance of three machine learning algorithms Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN) in predicting T2DM risk based on genetic data. By using a Systematic Literature Review of studies published between 2019 and 2024, the accuracy data from each algorithm was compared. The analysis results show that Random Forest has the best performance with an accuracy of 99.3%. This algorithm excels due to its ability to handle high-dimensional datasets and reduce overfitting. In comparison, KNN achieved an accuracy of 87% and Logistic Regression 82%. These findings support the integration of machine learning into early detection systems and more precise and efficient clinical decision-making for T2DM management.
The DeLone and McLean Model for Measuring Success Hospital Management Information System Case Study: Praya Regional Hospital Kartini, Ajeng Mastuti; Fadli, Sofiansyah; Fahmi, Hairul; saikin, saikin
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2203

Abstract

The advancement of information technology in the health sector encourages hospitals to implement Hospital Management Information Systems (SIMRS) to improve efficiency, effectiveness, and service quality. This study aims to measure the success rate of SIMRS implementation at Praya Regional Hospital using the DeLone and McLean model, which includes six variables: system quality, information quality, service quality, usage, user satisfaction, and net benefits. Data was collected through distributing questionnaires to 101 respondents in all service units of RSUD Praya. The analysis was conducted using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method. The results showed that only three of the nine hypotheses proposed proved significant, namely the effect of user satisfaction on net benefits, service quality on usage, and usage on net benefits. These findings indicate that technical aspects and system services need to be improved to achieve optimal SIMRS implementation. This research contributes to the evaluation of hospital information systems and can serve as a reference in making future system development decisions.
Design of a Medical Guide and Healthy Lifestyle Application for Pregnant Women Based on Flutter Web Mobile Br. Panggabean, Chaterine Laura; Anggara, Afwan
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2541

Abstract

Pregnancy is a period women that requires careful health monitoring and access to accurate medical information, yet pregnant women still face limitations in accessing digital platforms that provide structured health guidance. This study focuses on developing a web-mobile application using the Flutter framework to deliver medical guidelines and healthy lifestyle recommendations for pregnant women at Puskesmas Mlati 1 Yogyakarta. The development process applies the Waterfall model, which includes requirement analysis, system design, implementation, testing, and maintenance. The main features of the application include medical guidelines, a medical terminology dictionary, healthy food recommendations, examination schedule checking, and daily healthy lifestyle tips. The user interface is designed to be responsive and easy to use, enabling users to access the provided information conveniently. The testing results show that the application runs well across various devices and is capable of supplying the information needed by pregnant women throughout their pregnancy. This application is expected to serve as a practical and educational information tool that supports the improvement of maternal health literacy through digital technology.
UI/UX Design Using Design Thinking Method Based on Website Unload Repair Bonari, Anggiet Harjo Baskoro; Putra, Fajar Ariya
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2305

Abstract

Unload Repair is a PC repair service that can solve problems with computers or laptops. However, the current problem is that marketing through social media tends to be a little bit about information about service services, making it difficult for customers to receive the information. This study aims to create a User Interface (UI) and User Experience (UX) design that can make it easier for customers to see what services are provided by Unload Repair and help customers in ordering repair services using the Design Thinking method. Design Thinking is a method that involves users in every stage of design, starting from a deep understanding of their needs, to producing a prototype that can be tested to measure the effectiveness of the design. Design thinking includes the Empathize, Define, Ideate, Prototype and Testing stages. The Design Thinking method used is to create solutions based on problems in order to create designs that are easy to use and understand for users. The design was tested using Usability testing on 50 respondents, thus it was concluded that the UI/UX design on the Unload Repair website in the city of Jakarta can be used easily by users and has a positive experience based on the results of the respondents obtained and gets good satisfaction from Unload Repair website users.
Overview of Vehicle-to-Everything Communication: Technological Advancements, Applications, and Future Prospects in Intelligent Transportation Systems Bintoro, Ketut Bayu Yogha
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2187

Abstract

 Vehicle-to-Everything (V2X) communication is a cornerstone technology in modern Intelligent Transportation Systems (ITS), enabling real-time data exchange between vehicles, infrastructure, pedestrians, and networks. V2X enhances road safety, traffic management, and driving experience, contributing to the global vision of connected and autonomous transportation. This paper presents a comprehensive overview of V2X communication, detailing its key components, types, and technological standards, including Dedicated Short-Range Communication (DSRC), LTE-V2X, and 5G. We explore current applications of V2X, such as safety enhancements, traffic optimization, infotainment, and environmental sustainability, and discuss significant challenges in deployment, including technical limitations, security and privacy concerns, and infrastructure requirements. Furthermore, this study examines future directions in V2X, emphasizing the role of 5G, edge computing, AI integration, and the implications for electric and autonomous vehicles. Realizing V2X's full potential requires robust policy support, international standardization, and infrastructure investments, particularly in countries with varying levels of technological maturity. This paper provides insights into the evolution of V2X and outlines critical considerations for advancing toward a safer, more efficient, and sustainable global transportation ecosystem
Development of a Risk Analysis Application for Higher Education Institutions Using the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) Methodology Putra, Rizaldi; Sanjaya, Memet; Adelia, Kinanti Dwi; Fathimah, Neng Siti
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2556

Abstract

In general, higher education institutions in Indonesia continue to face challenges in accurately mapping risks identified through Internal Quality Audits (IQA), resulting in limited collective management awareness of risk-based operations. Specifically, this study highlights that similar conditions persist in Cikarang, West Java, where risk identification and control processes remain insufficiently integrated into institutional quality improvement strategies. Although routine audit findings are successfully collected, the subsequent follow-up process is often unstructured and fails to prioritize the most crucial improvements. This research addresses these challenges by developing an application. Digital system adapted from the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) methodology. That enables managers to collaboratively determine the risk level associated with each finding. The system also facilitates the categorization of findings based on the urgency of required corrective actions and prioritization for subsequent mitigation efforts. This application is designed to facilitate the conversion of every evaluation finding into a measurable risk score. The primary objective of this system is to deliver comprehensive visualization and mapping of risks through a collaborative process, enabling groups to identify the impact of each finding, conduct analysis and discussion to determine probability, exposure, and consequence, and classify the results into categories of very high risk, high risk, substantial risk, moderate risk, or low risk.
1D-CNN-Based Childhood Stunting Prediction through Socio-Economic Data Integration and Community Participation Bahtiar, Agus; Mulyawan, Mulyawan; Faqih, Ahmad; Lidina, Lidina; Fitria, Ananda Rizki
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2490

Abstract

Stunting remains a significant global public health challenge, affecting approximately 148 million children under the age of five. This condition leads to long-term cognitive and physical deficits, particularly in low- and middle-income countries. Many existing prediction models fail to capture the complex interdependencies between nutritional, socio-economic, and environmental factors. To address this gap, our study introduces a 1D-Convolutional Neural Network (1D-CNN) model designed to predict childhood stunting using structured datasets collected from community health centers (Puskesmas) and validated by the Cirebon City Health Department (Dinas Kesehatan Kota Cirebon), Indonesia. The dataset includes anonymized records of children under five years old, comprising anthropometric measurements, socio-economic profiles, nutritional intake, and environmental indicators, gathered through household surveys and routine public health reporting. The proposed 1D-CNN architecture is optimized for structured data by integrating convolutional and pooling layers, dropout regularization, and dense classification layers. To enhance interpretability, we employ explainable AI (XAI) methods—SHAP and LIME—to reveal the relative influence of each feature in the model’s decision-making process. Additionally, the study applies a participatory validation approach through focus group discussions (FGDs) with community health workers, ensuring contextual relevance and ethical integrity. Experimental results demonstrate the superior performance of the proposed model, achieving 93.12% accuracy, with a precision of 97% and a recall of 89%, resulting in an F1-score of 93% across both stunted and non-stunted classes. These findings outperform traditional machine learning approaches and highlight the potential of AI-driven predictive frameworks for early stunting detection and policy-oriented health interventions. This research contributes to the advancement of data-driven public health strategies by integrating predictive analytics, community participation, and transparent AI methodologies
Development of Smart Plant Watering System Application Based on Internet of Things Bachtiar, Adnan Nuur; Faisal, Mochammad; Darwis, Muhammad
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2208

Abstract

In 2024, air pollution levels in Jakarta were recorded as the highest in Southeast Asia, based on various air quality monitoring reports. This condition has become an increasingly alarming environmental issue, with pollution levels frequently exceeding the safe threshold set by the World Health Organization (WHO). One of the efforts by the Jakarta Provincial Government to address this problem is a free plant distribution program for the public. Plants play a crucial role in absorbing carbon dioxide, producing oxygen, and reducing airborne pollutant particles. However, plant maintenance—especially watering—poses its own challenges. Inefficient watering can cause plants to experience stress, wilt, or even die. With the advancement of technology, an automatic plant watering system based on the Internet of Things (IoT) presents a potential solution to improve the efficiency and sustainability of plant care. This study aims to develop a smart plant watering system application based on IoT that can automatically control watering based on real-time soil moisture levels. The system was applied to spider plants (Chlorophytum comosum) grown in pots with a diameter of 15 cm. By using an ESP32 microcontroller, a soil moisture sensor (Capacitive Soil Moisture Sensor v1.2), an air temperature and humidity sensor (DHT11), and a water pump, the system automatically activates watering when the soil moisture is below 55% and stops when it exceeds 65%. Sensor data is stored in a database and displayed through a web-based application for remote monitoring.
Literacy Review Study on the Implementation of Convolutional Neural Network Architecture in Segmentation and Classification of Lung Medical Images Riyono, Joko; Supriyadi, Supriyadi; Pujiastuti, Christina Eni; Puspa, Sofia Debi; Putri, Aina Latifa Riyana
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2172

Abstract

Medical image processing has become an essential aspect of healthcare, enabling accurate disease diagnosis and monitoring through advanced technologies. One of the most widely used methods in this domain is the Convolutional Neural Network (CNN), which has demonstrated high effectiveness in segmentation and classification tasks, particularly for chest X-ray images used in diagnosing lung-related diseases. This study aims to evaluate and analyze various CNN architectures implemented in lung X-ray imaging through a Systematic Literature Review (SLR) approach. The research explores the application, accuracy, challenges, and future opportunities of CNN-based models such as VGG, ResNet, AlexNet, and GoogLeNet. A total of 15 relevant studies published between 2019 and 2023 were selected after applying rigorous inclusion and exclusion criteria. The findings indicate that CNN architectures significantly enhance the accuracy of lung disease detection and support both segmentation and classification tasks. However, challenges such as dataset variability, model generalization, and ethical implications remain. This review provides comprehensive insights into CNN applications in medical imaging, emphasizing their potential and highlighting areas for further research.

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