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INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Teknokrat Indonesia
ISSN : 27973492     EISSN : 27972011     DOI : https://doi.org/10.33365/jatika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 41 Documents
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Comparison of Modern NLP with Classical Machine Learning Algorithms in Evaluating Food Security Programs Sinaga, Anita Sindar; Sijabat, Dameria Esterlina; Saputri, Bella; Aulia, Nadia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 4 (2025): Volume 6 Number 4 Desember 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i4.1395

Abstract

The success of food security programs faces various challenges. Most of the available data is in the form of unstructured text reports, news, and policy documents. The BERT (Bidirectional Encoder Representations from Transformers) model allows the system to read reports and news by considering the relationship between words in sentences. Compared to Support Vector Machines (SVMs) that rely on numerical data. The dataset is expanded to improve the generalization of the IndoBERT Classifier. There are 6 commodity data and 3 labels used in IndoBERT Modeling, represented by a 768-dimensional feature vector resulting in Accuracy 0.8333 (83.33%) indicating 5 correct predictions, with one misclassification. Tuned Min-Max on Support Vector Machines (SVM) is used in each dimension to find the optimal hyperplane contributing. The feature matrix x with size (39,10) and the target variable y with size (39) show Accuracy 0.92 (92.0%) that the data division process maintains the class proportion consistently. SVM performed better than IndoBERT. Classification evaluation of the models showed IndoBERT with Accuracy 83% and SVM Sccuracy 87%.
Synthetic Data Pattern Simulation of Patient Care Journey Using K-Means Clustering Sitio, Arjon Samuel; Parlindungan, Richard; Sinaga, Anita Sindar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 4 (2025): Volume 6 Number 4 Desember 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i4.1498

Abstract

Heterogeneous synthetic data is artificial data that can include many types of features (demographics, examinations, therapies). Complex patients (many procedures & medications) but fast service process and low complications. All patients are divided into 4 clusters, patient segmentation includes cluster 1 including mild patients, Cluster 2 including complex patients, Cluster 3 including high costs, Cluster 4 including high readmission risk. The highest silhouette score is 0.2187, which is obtained when the number of clusters (k) is 2. Based on previous calculations, the Davies-Bouldin Index result for the current clustering solution is 2.33. The Calinski-Harabasz index for the clustering solution with k=4 is 367.72. Clustering results are simply groups, without labels. Further analysis is needed to assign clinical meaning to each cluster.
Advancing Smart City Infrastructure: A Deep Learning-Based Framework for Real-Time Traffic Monitoring and Violation Detection Using YOLOv11 Rama Putra, Gustian; Jaleco Forca, Adrian; Jun Gepayo Alminaza, Reiner; Delli Wihartiko, Fajar; Rafif, Raid
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1354

Abstract

Urban traffic congestion and violations of dedicated bus lanes in metropolitan cities, such as Jakarta, are significant challenges affecting the efficiency of public transportation systems. Traditional traffic monitoring methods are insufficient to address these issues, particularly in real-time violation detection. This research proposes an AI-based smart traffic monitoring framework using YOLOv11 for real-time detection of vehicle violations in TransJakarta’s Bus Rapid Transit (BRT) lanes. The study aims to improve urban mobility by enhancing the detection accuracy and speed of traffic monitoring systems. The methodology involves data collection from surveillance cameras, data annotation using Roboflow, and model training with YOLOv11, utilizing transfer learning and hyperparameter optimization. The system's performance is evaluated through precision, recall, F1-score, and mean Average Precision (mAP@0.5), as well as real-time inference speed. The results show that YOLOv11 achieves a mAP@0.5 of 0.946 and an F1-score of 0.898, demonstrating the model's high accuracy in detecting vehicle violations across different lighting conditions. Real-time inference is achieved at a rate of 35-40 FPS, making it suitable for deployment in real-world urban environments. This research concludes that the YOLOv11-based framework is an effective solution for automated traffic monitoring, offering significant implications for smart city development and intelligent transportation systems. Further research is needed to address lighting challenges and improve the system's scalability across various urban settings.
Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks Bagastian, Bagastian; Putro, Dimas Eko; Fudholi, Muhammad Fahmi; Suryono, Ryan Randy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1437

Abstract

The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
Clustering of Provincial Health Vulnerability Levels in Indonesia Using the K-Means Method Arnilia, Okma; Ishak, Sahrial Ihsani; Widodo, Tri; Nyoman Agung Bisma Tatwa, I Gusti
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1469

Abstract

This study aims to classify the health vulnerability levels of 38 provinces in Indonesia based on health and socio-economic indicators in 2024, including the number of hospitals, access to adequate sanitation, access to safe drinking water, stunting prevalence, number of health facilities, population size, and the percentage of poor population. The analysis began with data normalization using the z-score method to standardize variable scales and prevent dominance by indicators with larger value ranges. Following normalization, the optimal number of clusters was determined using the Elbow method by examining the decrease in inertia across different k-values. Based on the inertia pattern and cluster stability, the optimal number of clusters was identified as K=4, which adequately represents the variation in health vulnerability. The clustering results were subsequently visualized in a spatial map using Indonesia’s provincial administrative boundaries. The visualization revealed clear geographical variation across regions, with Cluster 1 representing provinces with very good health conditions, Cluster 2 good conditions, Cluster 3 moderate conditions, and Cluster 4 provinces requiring special attention regarding health indicators. These findings provide a comprehensive overview of health vulnerability distribution in Indonesia and are expected to inform policymakers and stakeholders in prioritizing region-based health interventions, strengthening health development strategies, and promoting more equitable national health services.
Comparison Random Forest and Logistic Regression in Predicting Motivation and Learning Outcomes of Junior High School Students Juanta, Palma; Pavithra, Valencia; Hutabarat, Nurija Sri Paska; Simatupang, Yehuda M. P.
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1510

Abstract

Student learning motivation and learning outcomes are important factors that influence educational success, especially at the junior high school level. Previous studies that primarily emphasize academic achievement prediction alone, this study simultaneously evaluates student motivation and learning outcomes as dual prediction targets. Moreover, while earlier research often applied only a single algorithm or focused on higher education datasets, this research specifically conducts a head-to-head comparison between Random Forest and Logistic Regression using junior high school data, thereby filling an important gap in secondary education predictive analytics. This study compares the performance of two machine learning algorithms, namely Random Forest and Logistic Regression, in predicting student motivation and learning outcomes based on data on learning habits, mental condition, attendance, sleep hours, family support, and academic grades. The study process included data pre-processing, normalization, separation of data into training and testing data, model training, and evaluation using accuracy, sensitivity, specificity, and AUC. Based on the study findings, Random Forest performed better with an accuracy of 0.91, sensitivity of 0.91, specificity of 0.94, and AUC of 0.94. Meanwhile, Logistic Regression obtained an accuracy of 0.84, sensitivity of 0.84, specificity of 0.90, and AUC of 0.95. These findings confirm that Random Forest is superior in its overall predictive ability, while Logistic Regression remains relevant due to its interpretability. This study aims to assist in the development of data-driven decision support systems in education to help schools identify students who require early intervention.
Multi-Criteria Decision Model for Ranking the Best Marketplace Using CRISUS Weighting and OPARA Ranking Thalib, Akil; Asistyasari, Ayuni; Nuryaman, Yosep; Oprasto, Raditya Rimbawan; Yudhistira, Aditia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1568

Abstract

The rapid growth of e-commerce marketplaces in Indonesia has increased competition among platforms and created challenges in identifying the most suitable marketplace for users and businesses. Previous studies commonly applied conventional Multi-Criteria Decision-Making (MCDM) approaches, yet many of these methods rely heavily on subjective weighting or limited data-based evaluation, which may lead to inconsistent ranking results. Therefore, this study aims to develop a more objective decision-making model for marketplace evaluation by integrating the CRISUS weighting method with the OPARA ranking approach. The dataset consists of quantitative marketplace performance indicators collected from public digital statistics, including monthly visits, annual visits, application ratings, number of downloads, and the number of active sellers for several major marketplaces operating in Indonesia. The CRISUS method is used to determine criterion weights based on actual data variation to reduce subjective bias, while OPARA evaluates the alternatives through an optimized pairwise ratio mechanism to obtain the final preference values. The experimental results indicate that Shopee achieves the highest score of 0.3078, followed by Lazada with 0.2476 and Tokopedia with 0.2327, demonstrating their stronger performance compared with other marketplace alternatives based on the evaluated criteria. These findings contribute both academically and practically by providing a transparent and data-driven MCDM framework that improves the reliability of marketplace ranking and can support stakeholders in making more informed platform selection decisions.
Web-Based Healthcare Service System Development Using RAD Method for Community Health Center Efficiency Improvement Iroth, Nathanael Eliasher; Montolalu, Chriestie Ellyane Juliet Clara; Takaendengan, Mahardika Inra; Langi, Yohanes Andreas Robert; Kalengkongan, Wisard Widsli; Lapihu, Dodisutarma
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1606

Abstract

This research aims to design and develop a healthcare service website for Puskesmas Modoinding to improve efficiency and accessibility of public health services. Previously, information delivery including doctor schedules, service types, operating hours, and patient registration was conducted manually, often causing delays and limited information distribution. The system was developed using the Rapid Application Development (RAD) method, comprising four phases: requirements planning, system design, development, and implementation. Data collection employed interviews, observation, and literature study with 12 stakeholders (10 patients, 2 administrators). The website was built using HTML, CSS, JavaScript, PHP, and MySQL database. Black Box Testing demonstrated 100% success rate across 28 test scenarios covering admin and patient interfaces. System Usability Scale (SUS) evaluation yielded average scores of 85.5 for patients and 87.5 for administrators, both exceeding the 'Very Good' threshold of 80. Ten core features were successfully implemented including online registration, queue status checking, and administrative data management. The developed website enables community members to access healthcare information remotely without physical visits, reducing average registration time by approximately 40%. This research concludes that web-based healthcare systems using RAD methodology can significantly improve service efficiency at community health centers while meeting user requirements and usability standards.
Design of Health Application Prototype "Medimate" Using Design Thinking And System Usability Scale (SUS) Wuaten, Frank Emmanuel; Hatidja, Djoni; Takaendengan, Mahardika Inra; Tenda, Edwin; Soewoeh, Christian Alderi Jeffta; Kalengkongan, Wisard Widsli
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1607

Abstract

The rapid digitization of healthcare in Indonesia reveals significant usability gaps, with popular applications like Halodoc and Alodokter showing inconsistent System Usability Scale (SUS) scores. Specifically, existing platforms often prioritize feature completeness over user-centered interface design, resulting in navigation ambiguities. Background information highlights that poor UX leads to user abandonment. Therefore, optimizing interface design is crucial. This research aims to design a user-centered health application prototype, "Medimate," to address critical interface and navigation deficiencies. The study employs a mixed-methods approach, utilizing the five-stage Design Thinking framework (Empathize, Define, Ideate, Prototype, Test) for development and SUS for quantitative evaluation. Data collection involved in-depth interviews with two user personas to identify pain points, followed by usability testing with 36 respondents. Responses were collected via Google Forms to facilitate remote data gathering. Results indicate that "Medimate" effectively resolves identified UX issues, achieving a mean SUS score of 80.42, classified as Grade A- (Excellent). Key design improvements include transparent pricing structures, intuitive navigation flows, and integrated mental health features. The study concludes that the Design Thinking approach significantly enhances usability metrics in health applications. "Medimate" demonstrates high acceptability and viability for further functional development. Future research should expand sample sizes to include broader demographics and evaluate backend performance. This study contributes a validated design framework for improving digital health accessibility in emerging markets.
Web-Based Guest Lecture Information System for Committee and Student Users at FMIPA UNSRAT Sumakul, Andrea Emailly; Montolalu, Chriestie Ellyane Juliet Clara; Takaendengan, Mahardika Inra; Pinontoan, Benny; Kalengkongan, Wisard Widsli; Lapihu, Dodisutarma
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2026): Volume 7 Number 2 June 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i2.1608

Abstract

Guest lecture management at FMIPA UNSRAT currently suffers from significant fragmentation across WhatsApp, Google Forms, and paper attendance, leading to data duplication, information inconsistencies, and administrative inefficiency. This condition hinders effective decision-making and stakeholder engagement. This study aims to design and develop a centralized Web-Based Guest Lecture Information System to integrate the entire event lifecycle, including publication, registration, digital attendance, and reporting. The system was developed using the Waterfall model with Laravel framework and MySQL database. Comprehensive evaluation involved Black Box Testing, User Acceptance Testing (UAT) with committee members, and User Perception Testing with students. Results indicate a 100% success rate in Black Box Testing across 42 functional scenarios. UAT yielded a 90.4% acceptance rate among committee members, validating operational feasibility and workflow alignment. Furthermore, User Perception Testing achieved a 91.04% satisfaction score among students, with Behavioral Intention reaching 93.6%. These findings demonstrate that the system significantly reduces data fragmentation and improves administrative efficiency compared to manual processes. The system is deemed feasible for immediate deployment, offering a robust solution for centralized academic event management. However, limitations exist regarding financial module integration. Future work should focus on API integration with the central university portal and automated honorarium processing to further enhance scalability and institutional adoption.

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