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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 442 Documents
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DENGUE FEVER CASE PREDICTION MODEL USING LINEAR REGRESSION WITH EXPLANATORY SEQUENTIAL MIXED METHODS APPROACH Conchita Junita Chandra; Yoseph Thobias Pareira
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.484

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease in Indonesia, including in Sikka Regency, where the number of cases has increased over the past decade. Predicting the number of DHF cases is crucial to support disease prevention and control policies. This study aims to develop a predictive model for the number of dengue fever cases based on building area, population, and population density, moreover to explain other factors that influence the prediction results. The study uses an explanatory sequential mixed methods approach, and the prediction model is developed using simple linear regression and multiple linear regression. Quantitative data were obtained from the Health Office, the Sikka Regency Statistics Office, and Google Earth; while qualitative data were obtained through interviews with surveillance personnel from the Health Office and several community health centers in the study area, using a purposive sampling technique. The results show that the building area has a weak relationship with the number of DHF cases (R² = 0.10334 for Alok Timur sub-district and R2=0.38055 for Waiblama). After adding the population and population density variables, the R² in Alok Timur increases to 0.46974; and R2=0.41024 for Waiblama; however, the accuracy is still low. The interviews results show that community behavior is the dominant factors of DHF cases. This study indicates that predictive models based on physical environmental and population variables are unable to accurately depict the complexity of dengue fever case distribution. Therefore, the development of models that integrate community behavioral factors is necessary to provide more accurate predictions.
APPLICATION OF THE FIRST COME FIRST SERVED METHOD IN A WEB-BASED MARRIAGE REGISTRATION SYSTEM Tenriangka Tenri; lilis Nur Hayati; Amaliah Faradibah Amaliah
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.498

Abstract

Marriage registration at the Office of Religious Affairs (KUA) is still largely carried out using conventional methods, which gives rise to various problems such as document accumulation, the risk of file loss, data recording errors, and delays in the verification process, particularly for prospective couples who live far from the KUA office. This process requires prospective couples to visit the KUA office in person and submit physical documents, making it inefficient and ineffective. This study aims to develop a website-based marriage registration system that facilitates online registration for prospective couples and regulates the service order in an orderly and fair. This study applies the First Come First Served (FCFS) method to regulate the order of marriage registration based on the time of registration, ensuring a fair and orderly service process. Furthermore, the system development is carried out using the Waterfall approach, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The results show that the website-based marriage registration system operates according to the designed workflow. Alpha testing indicates that all main features function as specified, while beta testing obtained an average score of 4.18 with a percentage of 83.6%, which falls into the good category. These results indicate that the system is well accepted by users and suitable for implementation at the Office of Religious Affairs (KUA).
DEVELOPMENT AND USABILITY EVALUATION OF A WEB-BASED POS FOR HARDWARE STORES USING SUS Andre Rahmat Kurniawan; Muhammad Arrozi; Yurdan Vebry Endika; Muhammad Afdhal
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.501

Abstract

Small retail businesses such as hardware stores often manage sales, stock, and reporting manually, which can lead to recording errors, slow reporting, and limited decision support. This study develops a web-based Point of Sale (POS) application for a hardware store environment and evaluates its usability using the System Usability Scale (SUS). The POS system supports product and category management, transaction processing (discount and tax), sales reporting with date-range filtering, best-selling product summaries, and CSV export for spreadsheet-based bookkeeping. The research method follows a design-and-build approach, supported by functional validation using black-box testing and usability measurement using SUS. Usability testing involved five respondents who performed task scenarios including creating transactions, opening sales reports, filtering by date range, and exporting CSV data. The SUS results show respondent scores of 40.0, 82.5, 67.5, 77.5, and 90.0, with an average score of 71.5 (SD = 19.41). The average score indicates the system is acceptable and generally usable, although variability suggests different learning needs across users. The study concludes that a web-based POS with integrated reporting and export features can meet operational needs of small hardware stores and achieves an acceptable usability level. Recommendations include improving onboarding and simplifying certain workflows for new users.
TRANSFER LEARNING ARCHITECTURE SELECTION FOR REMOTE SENSING SCENE CLASSIFICATION Akhiyar Waladi; Hasanatul Iftitah
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.515

Abstract

Selecting a deep learning architecture for classifying remote sensing scenes usually involves comparing published accuracy across papers that each use different training protocols, making it unclear whether accuracy gaps reflect architecture or training differences. We isolate the architecture variable by evaluating eight models from three design families, five classical CNNs (ResNet-50, ResNet-101, DenseNet-121, EfficientNet-B0, EfficientNet-B3), two vision transformers (ViT-B/16, Swin Transformer), and one modernized CNN (ConvNeXt-Tiny), under identical training conditions on EuroSAT (10 classes, 27,000 Sentinel-2 patches) and UC Merced (21 classes, 2,100 aerial photographs). Every model shares the same ImageNet-1K initialization, AdamW optimizer, augmentation pipeline, and early stopping rule. ConvNeXt-Tiny reached the highest accuracy on EuroSAT (99.11%) and Swin-T on UC Merced (99.76%), but the accuracy range on EuroSAT was only 0.41 percentage points (1.66 on UC Merced). McNemar's test confirmed that most pairwise differences were not significant. EfficientNet-B0, the smallest model at 4.0M parameters, reached 98.76% and 99.52% while using 21x fewer parameters than ViT-B/16. On these two well-studied benchmarks, a single uniform training configuration was sufficient to bring all architectures to near-identical performance. This convergence, observed under one fixed protocol and a single data partition, suggests that on saturated classification tasks the choice of architecture may be secondary to the choice of training procedure. Whether this convergence holds on harder benchmarks, under architecture-specific optimal configurations, or with domain-specific pretraining remains to be tested
AGILE IMPLEMENTATION IN MOBILE POINT OF SALE SYSTEM DEVELOPMENT FOR BUSINESS DIGITALIZATION Yudha Herlambang Cahya Pratama; Farhan Abimanyu Firmansyah; Laqma Dica Fitrani
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.519

Abstract

The development of information technology drives the need for a flexible, efficient, and easy-to-use Point of Sale (POS) system to support retail business operations. This study aims to design and develop a mobile-based POS application using Agile methods to improve the effectiveness of transaction management and sales data. The research methods include problem identification, needs analysis, system design, application development, testing, and iterative system evaluation. The system design was carried out using use case diagrams and Entity Relationship Diagrams (ERD), while the implementation was developed on a mobile platform with key features including user authentication, product management, sales transactions, stock management, reports, and owner and cashier access rights settings. Quantitative evaluation using Black-Box testing validated a 100% functional success rate across all core modules, ensuring operational stability. Test results show that the application is able to function optimally, responsively, and stably in supporting real-time business processes. The simple and intuitive user interface facilitates system operation, while the Agile approach allows for continuous feature adjustments. Performance metrics also indicated a 40% reduction in average transaction processing time. Thus, the developed application is considered effective in improving the efficiency, accuracy, and quality of mobile-based retail transaction management
A COMPARATIVE STUDY OF DISTANCE METRICS AND NEIGHBOR SELECTION IN K-NEAREST NEIGHBOR FOR VOCATIONAL STUDENT PERFORMANCE CLASSIFICATION Muhammad Aris Ganiardi; Ida Wahyuningrum; Nita Novita; Denny Alfian
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.520

Abstract

This study aims to evaluate parameter sensitivity in the K-Nearest Neighbor (KNN) algorithm, particularly the selection of distance metrics and k-values, for classifying academic performance in vocational education with heterogeneous and imbalanced data characteristics. The dataset consists of 750 first-year students from the Informatics Management program, including academic attributes (GPA, attendance, and core course grades) and demographic attributes (age, gender, educational background, and economic status). Data preprocessing involves data cleaning, one-hot encoding, Z-score normalization, and handling class imbalance using SMOTE. Model evaluation is conducted using K-Fold Cross Validation with accuracy, precision, recall, and macro-average F1-score as performance metrics. The results show that KNN performance is highly influenced by the combination of distance metrics and k-values. All metrics achieve accuracy above 84%, but differ in handling class imbalance. The Chebyshev metric (k = 10) provides the best balance with an F1-score of 0.6468, while the Minkowski metric (p = 3) achieves the highest recall of 0.7334. The Euclidean metric attains the highest accuracy of 0.8504 (k = 11), but tends to be biased toward the majority class. These findings indicate that optimizing KNN parameters should not rely solely on accuracy, but also consider balanced performance across classes. This study provides a practical evaluation framework for selecting KNN parameters to support more robust and fair academic prediction systems in vocational education data.
STEMMINDO: A WEB-BASED INDONESIAN STEMMING ENGINE USING ENHANCED CONFIX STRIPPING Novi Prisma Yunita; Helmi Roichatul Jannah
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.521

Abstract

Stemming is an essential preprocessing stage in Natural Language Processing (NLP), particularly for Indonesian, which has complex affixation patterns. Most Indonesian stemming implementations are provided as programming libraries, making them less accessible for beginners, educators, and non-programmer researchers. This study presents Stemmindo, a lightweight web-based Indonesian root word search application implementing the Enhanced Confix Stripping (ECS) algorithm using the Laravel framework. Unlike conventional stemming libraries, the system provides a real-time and modular interface that enables users to explore Indonesian morphological processing without writing program code. The novelty of this research lies in the implementation of ECS within an accessible web-based educational tool. Evaluation was conducted using affixation pattern testing, rule-based testing, and real-text evaluation. Testing on 20 affixation patterns achieved 90% accuracy, while evaluation on 100 words representing 33 derived prefix rules achieved 94% accuracy. After applying failure-handling strategies through exception lists and rule-level accommodations, the accuracy increased to 98%. Real-text evaluation was conducted using 1,742 words collected from Indonesian educational web content. After preprocessing and filtering, 564 unique words were evaluated, of which 366 stemming results were successfully matched with the corpus, while the remaining cases mainly consisted of named entities, noisy input, ambiguous forms, overstemming, and understemming. These findings indicate that the proposed system performs effectively for common Indonesian morphological patterns while remaining practical for educational and experimental NLP usage. Future work includes improving reduplication handling, expanding lexical resources, and enhancing accessibility features.
A HOLISTIC AI-DRIVEN ENERGY-EFFICIENT IOT FRAMEWORK FOR SMART AGRICULTURE USING MULTI-RESOURCE OPTIMIZATION Gunawan Budi Sulistyo; Nani Purwati; Tri Wahyudi; Noor Hasan
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.525

Abstract

The rapid adoption of Internet of Things (IoT) technologies has accelerated the development of smart agriculture systems. However, existing studies predominantly focus on single-resource optimization and lack integrated artificial intelligence (AI) approaches within distributed architectures, resulting in suboptimal system-wide performance. This study proposes an AI-driven energy-efficient IoT framework that integrates the Random Forest algorithm with an edge–fog–cloud computing architecture to enable holistic multi-resource optimization. A quantitative simulation-based approach was employed using soil moisture data from the NASA SMAP dataset, with a case study in Magelang, Indonesia. The system was evaluated using key performance metrics, including energy consumption, network latency, packet delivery ratio (PDR), and water usage efficiency. The results demonstrate significant improvements, including a 28.65% reduction in energy consumption, a 31.43% decrease in latency, an increase in PDR to 96.8%, and a 20.3% improvement in water usage efficiency. Statistical validation confirms that these improvements are significant (p < 0.05). The main contribution of this study lies in the development of a holistic AI-driven IoT framework that simultaneously optimizes energy, water, computation, and communication without trade-offs. The proposed approach offers a scalable, adaptive, and efficient solution for real-world smart agriculture systems.
BREAST TUMOR CLASSIFICATION USING RANDOM FOREST WITH FEATURE SELECTION AND GRIDSEARCHCV OPTIMIZATION Priscilia Amanda Leza; Mutaqin Akbar
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.534

Abstract

Breast tumor classification into benign and malignant categories is an important challenge in the medical field because diagnostic errors can lead to delayed treatment or unnecessary medical procedures. This study aims to analyze the performance of Random Forest and evaluate the effects of feature selection and GridSearchCV hyperparameter optimization on breast tumor classification. The study used the Wisconsin Breast Cancer Diagnostic Dataset, consisting of 569 samples with 30 numerical features extracted from Fine Needle Aspiration (FNA) examinations. Four sequential Random Forest model configurations were compared: baseline Random Forest, Random Forest with feature selection, Random Forest with GridSearchCV optimization, and the integration of feature selection with GridSearchCV. Feature selection was performed using feature importance scores with ROC-AUC-based cross-validation to determine the optimal feature subset. Model evaluation was conducted using accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, and train-test gap. The results showed that all models achieved the same accuracy of 97.37%, precision of 1.0000, recall of 0.9286, and F1-score of 0.9630. However, the integrated model achieved the highest ROC-AUC of 0.9977 with the smallest train-test gap of 0.0241 while reducing the number of features from 30 to 15. These findings indicate that integrating feature selection and GridSearchCV improves model stability, efficiency, and discriminative capability without reducing classification performance, addressing the limitation of prior studies that applied these techniques separately.
DIGITAL IMAGE PROCESSING FOR BRAIN TUMOR CLASSIFICATION IN HUMANS USING CONVOLUTIONAL NEURAL NETWORKS Muhammad Dimas Romero Yusuf Daywin; Naufal Rasyad Muhammad; Kevin Yosia; Danendra Satya Purwoko; I Wayan Rangga Pinastawa
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.536

Abstract

The rapid development of digital technology has encouraged its utilization in various aspects of life, including the medical field. One significant application is digital image processing, which is used to enhance the quality and utility of medical imagery such as MRI and CT scans. This technology is highly relevant in diagnosing brain diseases, particularly brain tumors, which require high precision given the organ's complexity. This research focuses on the classification of brain tumor diseases using MRI images through the Convolutional Neural Network (CNN) method. CNN was selected due to its ability to extract essential features from MRI images, enabling it to identify complex patterns that are difficult for the human eye to recognize. With proper training, the CNN model is capable of distinguishing between healthy brain MRI images and those with tumors with an accuracy of 80%. These results demonstrate great potential in accelerating and improving the accuracy of the diagnostic process, which in turn assists in determining appropriate and effective treatment steps. This study provides a significant contribution to the development of medical diagnostic technology, specifically in brain tumor classification. Through the application of advanced digital image processing technology, it is expected that more efficient and accurate diagnostic tools can be created, thereby improving the quality of healthcare and patient treatment outcomes.

Page 44 of 45 | Total Record : 442

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