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All Journal Jurnal Riset Informatika Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Arif Pramudwiatmoko
Universitas Teknologi Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan Metode Fuzzy Tsukamoto Untuk Mendukung Pengambilan Keputusan Berdasarkan Data Jumlah Resi dan Profit Ferryma Arba Apriansyah; Arif Pramudwiatmoko; Muhammad Senoaji Wibowo; Evi Widiyastuti; Tri Agung Jiwandono; Vatma Sari
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 4 (2025): OCTOBER-DECEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i4.3917

Abstract

Data-driven decision-making in the logistics sector often encounters challenges due to fluctuating shipment volumes and unpredictable profit variations. This study implements the Fuzzy Tsukamoto method to process shipment quantity and profit data, enabling a decision-making model that is more responsive to uncertainty. The fuzzification process converts numerical data into fuzzy representations, followed by the application of if-then rules in the inference stage to determine appropriate decisions. The final results are then transformed back into numerical values through the defuzzification process. Evaluation results indicate high accuracy, with a Root Mean Squared Error (RMSE) of 0.07 and a Mean Absolute Error (MAE) of 0.05. These findings suggest that the Fuzzy Tsukamoto method effectively enhances decision-making by accounting for data variations and operational uncertainties. In practical applications, this model can assist logistics companies in optimizing shipment distribution, resource allocation, and delivery planning with greater precision, thereby improving operational efficiency and profitability.
DEVELOPMENT OF AN INFORMATION SYSTEM FOR ATTENDANCE AND STUDENT PROGRESS AT PAUD TUNAS MUDA Muhammad Ghazali; Arif Pramudwiatmoko
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.459

Abstract

The Tunas Muda Early Childhood Education Student Attendance and Progress Recording Information System application is a digital platform designed to help teachers record student attendance and progress in a modern and efficient manner. Currently, the recording process is still done manually, causing various obstacles such as late reporting, data inaccuracy, and difficulties in comprehensively monitoring student progress. This research uses the Research and Development (R&D) method. The purpose of this research is to develop a system that can facilitate teachers in taking attendance and recording student progress and enable school principals to monitor attendance and progress data through graphical displays and statistical analysis. Data collection was conducted through direct interviews with teachers as the main users. The system was developed using Flutter SDK for the interface and Firebase Firestore as the database. The results of the study show that the application is capable of recording student attendance and progress in real time, generating reports in PDF format, and displaying attendance and progress analysis in an informative graphical form.
SHAPE AND TEXTURE INTEGRATION FOR JAVA SEA FISH CLASSIFICATION USING K-NEAREST NEIGHBORS ALGORITHM Pingkan Putri Nazarina; Arif Pramudwiatmoko
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.464

Abstract

Manual identification of fish species at fish auction sites (TPI) was often time-consuming and prone to inconsistencies, which affected economic valuation and data recording accuracy. This study proposed an automated fish classification system to address these challenges using the K-Nearest Neighbors (KNN) method. The system was designed to assist the fish identification process in the Java Sea, with a case study conducted at the Karanganyar Fish Auction Site. The proposed approach employed computer vision techniques, beginning with image pre-processing steps such as segmentation and cropping to isolate fish objects. Subsequently, two complementary feature extraction methods were combined to obtain a robust representation of each fish image: Hu Moments for capturing holistic shape features that are invariant to scale and rotation, and Local Binary Pattern (LBP) for extracting detailed surface texture information. This hybrid feature representation provided a comprehensive descriptor for every fish instance. The dataset consisted of 1,000 images categorized into 10 main fish species (e.g., tongkol, bawal, and others). Model training and hyperparameter optimization were performed using a k-fold cross-validation scheme, followed by an 80:20 train-test evaluation. The experimental results demonstrated that the KNN model with the optimal k value achieved an overall classification accuracy of 98.50% on the unseen test set. These findings indicated that the integration of Hu Moments and LBP features was highly effective in distinguishing fish species and showed strong potential for practical implementation as a fast, objective, and reliable identification tool at fish auction sites such as Karanganyar Fish Auction Site
FACIAL RECOGNITION PERFORMANCE EVALUATION WITH YOLOV8, ARCFACE, AND SVM IN A CONTACTLESS EMPLOYEE ATTENDANCE SYSTEM Glanes Cindy Terampe; Arif Pramudwiatmoko
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.465

Abstract

Manual attendance systems, which continue to be implemented in many institutions, are vulnerable to manipulation and require significant time. This research proposes an automated facial recognition attendance system optimized to address the unique challenges posed by CCTV cameras installed at a height of 3 meters. The system integrates three main components: YOLOv8m for face detection, ArcFace for 512-dimensional feature extraction, and a Support Vector Machine (SVM) with a Polynomial kernel for identity classification. The dataset (5 classes) was augmented using 20 augmentations per image and was split into a 70% training and 30% testing ratio. An image preprocessing pipeline, including CLAHE, denoising, and sharpening, was applied to enhance the input image quality. Experimental results demonstrate high classification performance, achieving 93.7% accuracy, 0.938 precision, 0.937 recall, and an F1-Score of 0.935. Confusion matrix and PCA analysis identified that the primary misclassification occurred between the E005_employee5 and E002_employee2 classes, correlating with feature overlap. Computationally, the system achieved a throughput of 7.2 FPS on the testing hardware. The system is proven to be accurate and functional for the attendance task, although its real-time performance (FPS) is highly dependent on hardware acceleration.
Co-Authors Evi Widiyastuti Ferryma Arba Apriansyah Glanes Cindy Terampe Muhammad Ghazali Muhammad Senoaji Wibowo Pingkan Putri Nazarina Tri Agung Jiwandono Vatma Sari