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JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Published by Universitas Lambung Mangkurat
ISSN : 25275399     EISSN : 25282514     DOI : http://dx.doi.org/10.20527
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) is intended as a media for scientific studies on the results of research, thinking and analytical-critical studies regarding research in Systems Engineering, Informatics / Information Technology, Information Management and Information Systems. As part of the spirit of disseminating knowledge from the results of research and thought for service to the wider community and as a reference source for academics in the field of Technology and Information.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 10 Documents
 1 
Search results for , issue "Vol. 11 No. 1 (2026)" : 10 Documents clear
Predictive Analysis of Student Academic Performance Using Ensemble Learning Methods: A Case Study on the Portuguese Student Performance Dataset Hakim, Mujibul; Zuliarso, Eri; Hidayat, Husni; Imam, Muhammad Nurul; Sholehudin, Mukti Ahmad
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.492

Abstract

The ability to predict student academic performance at an early stage is crucial for educational institutions to provide timely interventions. This research aims to apply and evaluate the effectiveness of ensemble learning methods in predicting the final grades (G3) of secondary school students using the UCI "Student Performance" public dataset. To prevent data leakage, the models were executed without incorporating historical grade variables (G1 and G2), ensuring the system functions strictly as an Early Warning System. The methodological training process was enhanced by integrating k-fold cross-validation,hyperparameter optimization, and a direct comparison against a baseline model (Linear Regression) to guarantee model robustness and validity. Evaluation results indicate that the XGBoost model achieved the highest performance, yielding an Rsquared ($R^2$) of 0.28. Furthermore, feature importance analysis revealed that accumulated absences and prior class failures are the most significant predictors. As a practical implication, these findings recommend that schools develop proactive early warning dashboards and improve the overall school climate to address the root causes of absenteeism at an early stage.
Classification Of Anxiety Levels Based On General Anxiety Disorder Data Using The XGBoost Method Rahman, Rovi Royyan; Octariadi, Barry Ceasar; Sucipto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.495

Abstract

Anxiety is a common psychological disorder experienced by individuals and has the potential to reduce quality of life if not treated properly. This study aims to classify anxiety levels into four categories, namely normal, mild, moderate, and severe, using the Extreme Gradient Boosting (XGBoost) algorithm. The data used came from the Kaggle platform, consisting of 671 entries with 11 anxiety symptom features and one target label. The research process involved data exploration (EDA), handling missing values, data balancing using the Synthetic Minority Oversampling Technique (SMOTE), and feature selection based on multivariate correlation. Two models were built with training and test split ratios of 70:30 and 80:20. The evaluation results showed that the XGBoost model achieved good classification performance, with accuracy, precision, recall, and F1-score reaching 93% after optimization. The best model was then implemented as a Streamlit web application to facilitate interactive prediction of anxiety levels. This research is expected to be a tool for initial screening of anxiety disorders and a reference in the development of machine learning-based classification systems in the field of mental health.
Design of UI/UX Website Using the Design Thinking Method for Sukabumi Coffee Center Muhammad Rahmat Faizal; Lattu, Arny; Permana, M. Anton
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.496

Abstract

The rapid development of digital technology encourages businesses to provide accessible and informative digital information media. Sukabumi Coffee Center, as a coffee shop business, still faces challenges in delivering information to customers, as existing information distributed through social media is limited and unstructured. This study aims to design the User Interface (UI) and User Experience (UX) of an informative website for Sukabumi Coffee Center using the Design Thinking method. This method consists of five stages: Empathize, Define, Ideate, Prototype, and Test, which emphasize a user-centered approach. The design process was conducted based on observation, interviews, and literature study to identify user needs and problems. The research results include an informative website design that presents coffee product information, facilities, and shop identity in a structured and easily accessible manner. Usability evaluation was carried out using the System Usability Scale (SUS) involving 10 respondents and resulted in an average score of 90.75. This score indicates that the website falls into the acceptable category, achieves a Grade A usability level, and is classified as best imaginable. The findings demonstrate that the application of the Design Thinking method is effective in producing a user-centered UI/UX website design that enhances user experience and information delivery quality.
Early Detection Decision Support System for Diabetes at Nediva Husada Pagelaran Clinic Using Certainty Factor Method Firdaus, Mukhamad; Wahyudi, Farid
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.502

Abstract

The low public awareness regarding early symptoms of Diabetes Mellitus at Nediva Husada Pagelaran Clinic poses a significant challenge to early detection and disease prevention efforts. Late detection often worsens the patient's condition due to untimely treatment. This study aims to develop a web-based Decision Support System (DSS) as an early screening tool for diabetes risk using the Certainty Factor (CF) method. This method was selected for its ability to handle uncertainty in symptom data experienced by patients. The research began with data collection through observation and expert interviews to determine symptom weights. System development utilized the Rapid Application Development (RAD) method, encompassing planning, design, and implementation phases. Test results indicate that the system is capable of providing early detection recommendations that are mathematically consistent with CF calculations when compared to manual calculations. Furthermore, black box testing demonstrated that all system functions operated correctly, achieving a 100% success rate in accordance with the defined functional requirements. It is important to note that this system serves as an initial screening tool, not a medical diagnosis. This system is expected to assist the community in performing self-detection before undergoing further medical examination, thereby minimizing the risk of complications.
Analysis of Pieces in The Application of Extreme Programming (XP) Methods in Laravel-Based Website Development Luthfiana, Tias Hafidzotul; Iskandar, Joko; Sari, Yayak Kartika
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.507

Abstract

This research focuses on developing a website that can facilitate students, lecturers, heads of study programs, and study program staff in accessing relevant and up-to-date information. The problem experienced by the Informatics Study Program is the unavailability of a functional and informative website to support academic needs in the Informatics Study Program. The method used in this research uses the Extreme Programming (XP) approach with the stages of planning, designing, coding, testing which involves data collection through interviews, observations, and literature studies. The results of the analysis using the PIECES (Performance, Information, Economy, Control, Efficiency, and Service) method show the need for a website that meets user needs and is responsive to requests. The results of website development using the extreme programming method show that the website built is able to increase efficiency and effectiveness in providing services on demand. The development was carried out through four iterations, each of which included system analysis, system creation, and system testing. In each iteration process, improvements and enhancements were made to the website in accordance with the needs identified in the testing process and feedback from users. This truly reflects that the website has successfully met user needs and is responsive.
Digital Innovation Capability in MSME Information Systems and Its Impact on Digital Service Quality and Consumer Loyalty in Indonesia Hardini, Inkreswari Retno; Fadlullah, Fauzan; Shabrina, Fildzah; Atmadja, Ferry Setyadi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.513

Abstract

This research looks at the digital innovation capability as part of the structural element of the Micro, Small, and Medium Enterprise (MSME) information systems and its impacts on the quality of digital services and consumer loyalty in the Indonesian Smart Economy environment. Digital innovation is theorized as the system-level potential, which entails the alignment of online ordering frameworks, online payment gateways, platform interoperability, and responsiveness of online services to MSME digital platforms. This study is partaking that instead of digitalization being a marketing program, digitalization is an information systems capability within operational architecture. The quantitative survey methodology was used in this study, where 50 consumers that had completed transactions with technology-based MSMEs participated in the survey. The relationship between digital innovation capability and consumer loyalty was assessed by simple linear regression analysis. The results show that the impact of digital innovation capability on loyalty behavior is highly significant (R² = 0.536, p < 0.05) and that it explains 53.6 percent of the loyalty behavior. These findings indicate that improved system integration leads to perceived quality of digital service, which subsequently intensifies user retention and repeat purchase intentions. This study contributes to information systems research by empirically validating digital innovation capability as a measurable construct in MSME digital platforms and by supporting the development of capability-based digital transformation models within emerging digital ecosystems.
Implementation of the Best Teacher Performance Evaluation Decision Support System to Optimize Teacher Quality Using TOPSIS and SAW Methods Based on a Website (Case Study: SMP Putra Bangsa, Depok City) Rio Mukti Setyawan; Sri Rama Putri
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.514

Abstract

The process of selecting the best teachers at SMP Putra Bangsa in Depok is currently still conventional and subjective, which poses a risk of assessment errors and a lack of transparency. This study aims to develop a web-based Decision Support System (DSS) capable of optimizing efficiency and objectivity in identifying high-performing teachers. The scope of the study focuses on processing teacher performance data using five main criteria: administration, work discipline, attendance, collaboration, and professional development. The methods implemented were the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW). The results of the study indicate that the developed system successfully integrates both algorithms to generate an automatic and accurate ranking of teachers. The implementation of this system facilitates data processing for schools and enables the presentation of transparent evaluation reports. The main conclusion of this study is that the use of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) methods in a web-based system is effective in supporting more objective decision-making, while minimizing subjectivity in the process of evaluating teacher performance in a school setting.
Mobile-Based Decision Support System For Sunday School Student Categorization Using Rule-Based Method And RAD Model Tan, Andreas; Putri, Sri Rama
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.515

Abstract

The manual determination of student categories at Sunday School based on attendance records is prone to human error, time-consuming, and lacks consistency in evaluation. This study develops a mobile-based Decision Support System (DSS) to automate student categorization using attendance percentages. The system employs a Rule-Based approach with explicit logical thresholds and is developed using the Rapid Application Development (RAD) model to ensure rapid prototyping, iterative refinement, and active user involvement. Key features include QR-based attendance tracking, real-time percentage calculation, automated categorization (Very Diligent, Diligent, Fairly Diligent, Needs Attention), and PDF report generation. Black-box testing confirms that all functional modules perform as expected, achieving a 100% validation rate across core scenarios. The system successfully improves data accuracy, reduces administrative workload, and provides objective, transparent recommendations for student recognition and follow-up coaching. This solution demonstrates the practical applicability of rule-based DSS in religious educational institutions seeking efficient, data-driven student evaluation.
Edge AI Using MobileNet Architecture for Driver Drowsiness Detection Rafie, Rafi e
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.517

Abstract

Driving safety is a crucial issue significantly influenced by the driver's physical condition, where fatigue and drowsiness are major factors causing traffic accidents. This study aims to develop a real-time drowsiness detection system utilizing Edge AI technology based on the MobileNet architecture. This architecture was selected due to its efficiency in performing image classification on resource-constrained devices. The dataset used consists of 4,000 digital images balanced into open-eye and closed-eye classes. The model was trained using the TensorFlow framework and optimized through post-training quantization into the TensorFlow Lite format to reduce model size and inference latency. Performance evaluation was conducted by testing 372 new test images. The results indicate that the balanced model achieved an accuracy rate of 94%. Confusion matrix analysis showed a precision value of 1.000 for the closed-eye class and a recall of 1.000 for the open-eye class, indicating that the system is highly reliable in minimizing detection errors. With processing speeds reaching 10 to 22 Frames Per Second (FPS) on edge devices, this system is proven effective for implementation as a responsive driving safety assistant. Drowsiness detection duration indicator “Closed: 0.32s” represents part of the system logic used to trigger an alert. The system does not immediately activate an alarm during normal blinking, it measures the duration of eye closure. If the duration exceeds a predefined threshold (e.g., >0.30 seconds), an alert is triggered in the form of an audible alarm
Hybrid CNN Feature Extraction and Machine Learning Classification for UAV-Based Vegetation Density Land Cover Mapping in Peatlands Maulidiya, Erika; Sari, Yuslena; Gani, Irham Maulani Abdul; Islami, Achmad Mujaddid; Yunita, Helda
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.518

Abstract

Land cover classification from UAV imagery has become increasingly important for environmental monitoring, especially in peatland ecosystems where vegetation density is closely related to land openness, degradation risk, and fire vulnerability. This study proposes a hybrid approach that combines CNN-based feature extraction with machine learning classification for vegetation density-based land cover classification. A total of 3,000 UAV images collected from Block 1 of the Liang Anggang Protected Forest, Banjarbaru, were used in this study and categorized into three classes: bare, moderate, and high vegetation density. The images were preprocessed through cropping, resizing, and labeling prior to feature extraction. ResNet-50 and DenseNet-121 were employed as feature extractors, while ten machine learning classifiers were evaluated, namely CalibratedClassifierCV, SVC, NuSVC, LogisticRegression, PassiveAggressiveClassifier, SGDClassifier, LinearSVC, XGBClassifier, Perceptron, and LGBMClassifier. The results show that ResNet-50 generally outperformed DenseNet-121 as a feature extractor. The best and most balanced performance was achieved by the ResNet-50 + SVC combination, which obtained 84% accuracy, 84% F1-score, 91% precision, 77% recall, and a computation time of 8.58 minutes. Although CalibratedClassifierCV achieved the same accuracy, it required substantially longer processing time. These findings indicate that classification performance is influenced not only by the classifier used, but also by the compatibility between feature representation and classification mechanism. Therefore, the combination of ResNet-50 and SVC is recommended for UAV-based vegetation density land cover classification in peatlands.

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