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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 432 Documents
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APPLICATION OF TRANSFER LEARNING ON EFFICIENTNET-B0 ARCHITECTURE FOR AUTOMATIC ROOF TILE DAMAG CLASSIFICATION Ady, Rayhan Prasetya; Pramudwiatmoko, Arif
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.v8i1.450

Abstract

Subjectivity in manual quality control for traditional roof tiles poses a significant challenge, as the current process relies on manual, visual inspection and subjective judgment. This research proposes an automatic system to classify tile quality from images using a Convolutional Neural Network (CNN), specifically the EfficientNet-B0 model enhanced with transfer learning. The study utilized a primary dataset comprising 616 local roof tile images collected directly from producers in Berjo Kidul, Godean, Yogyakarta. These images were manually labeled based on producer criteria into three distinct classes: 'Finished' (203 images), 'Underbaked' (213 images), and 'Broken/Cracked' (200 images). The methodology involved resizing all images to 224x224 pixels and applying data augmentation, including random rotation, horizontal flipping, and color jitter, to mitigate overfitting. The EfficientNet-B0 model, pre-trained on ImageNet, was implemented in PyTorch and trained for 10 epochs using an 80/20 train/validation split with the Adam optimizer. The model demonstrated outstanding performance, reaching 99.70% accuracy in validation. Further evaluation confirmed this robustness; the model perfectly identified the 'Underbaked' class and recorded only a single misclassification error on the test set. Qualitative analysis via a Flutter mobile application showed the system is resilient to changes in background and viewing angles, although its accuracy is compromised by poor lighting and strong shadows. This study validates the proposed system as a highly efficient and objective tool for a more reliable quality control process.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Rahmawati, Ami; Yulianti, Ita; Oktarini Sari, Ani; Nurajizah, Siti; Hikmatulloh
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.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
ANALYSIS OF CLASSIFICATION ALGORITHM IN UNBALANCED DIABETES DATASET Ahmad Rifa'i; Herin Dwibima Aprianto; Lubna
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.v8i1.458

Abstract

Diabetes mellitus is a metabolic disease that is spreading rapidly and has the potential to be life-threatening worldwide. This condition occurs when the body experiences a decline in its ability to process glucose, triggering metabolic disorders. The use of machine learning algorithms is one effective approach to predicting or detecting diabetes based on the severity of a patient's symptoms. This study uses the Diabetes dataset from Kaggle and compares the performance of several classification algorithms in unbalanced data conditions and after data balancing using the SMOTE, Random Under Sampling, Random Over Sampling, and Near Miss resampling techniques. The results show that model performance is greatly influenced by data balance conditions and the resampling method used. In the original unbalanced data condition, Artificial Neural Network (ANN) provided the best results with the highest accuracy of 96.98%, indicating that ANN is the most adaptive to class imbalance. After resampling, the performance pattern changed: with SMOTE, Random Under Sampling, and Random Over Sampling, the Random Forest algorithm consistently produced the highest accuracy of 96.52%, 89.84%, and 96.26%, respectively, demonstrating its superiority in utilizing balanced data. Meanwhile, in the Near Miss method, the best performance was achieved by Logistic Regression with an accuracy of 94.41%, indicating that minority sample selection based on proximity is more suitable for linear models. Therefore, selecting the right combination of resampling methods and machine learning algorithms is an important factor in obtaining optimal diabetes predictions.
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.
MODELING THE IMPACT OF RECOMMENDATION ALGORITHMS ON GEN Z E-COMMERCE CONSUMPTION BEHAVIOR karina, Ritzqy; Joko Sutopo
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.v8i1.462

Abstract

The consumptive behavior of Generation Z (Gen Z) in e-commerce platforms is strongly influenced by recommendation algorithms, which often drive impulsive purchasing decisions. This issue is further exacerbated by low levels of financial literacy and the widespread availability of Buy Now Pay Later (BNPL) services, which increase the risk of a recurring debt cycle. This study aims to model and quantitatively estimate the level of impulsive behavior using a deep learning approach. Two neural network architectures were tested and compared. The first architecture, an Artificial Neural Network (ANN), was employed as a preliminary analytical model to map the nonlinear relationships between preprocessed static variables and impulsivity levels. The second architecture, a hybrid model combining a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), was specifically designed to capture temporal patterns and the dynamic evolution of impulsive behavior over time. Quantitative evaluation results demonstrate that the RNN-LSTM hybrid model achieved superior performance with exceptionally high estimation accuracy, as indicated by a Mean Absolute Error (MAE) of 0.0821 and a coefficient of determination (R²) of 0.9767. In comparison, the static ANN model achieved only an MAE of 0.2078 and an R² of 0.8924. These findings explicitly confirm that impulsive behavior is a dynamic phenomenon, and thus, the hybrid RNN-LSTM architecture proves significantly more effective in analyzing sequential behavioral patterns.
SHAPE AND TEXTURE INTEGRATION FOR JAVA SEA FISH CLASSIFICATION USING K-NEAREST NEIGHBORS ALGORITHM Nazarina, Pingkan Putri; 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 Terampe, Glanes Cindy; 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.
DESIGN OF A DIGITAL CORRESPONDENCE AND DISPOSITION SYSTEM WITH INTEGRATED DIGITAL SIGNATURE Putra, Kadek Rolavito Andrianto; Hidayati, Ajeng
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.v8i1.468

Abstract

The administrative workflow at the Army Communication and Electronics Center (PUSKOMLEKAD) faces significant challenges due to its reliance on manual, paper-based correspondence processes. This manual system causes operational inefficiencies, difficulties in real-time disposition tracking, and critical workflow bottlenecks, particularly the dependency on the physical presence of leadership for signatures. Data for this study were collected through direct observation of the manual administrative workflow and interviews with personnel regarding user requirements. The research method used is Research and Development (R&D), applying the Rapid Application Development (RAD) model for the system's lifecycle using the PHP Laravel framework and MySQL database. The research resulted in a functional prototype that features an integrated digital archive, a multi-level disposition system for real-time tracking, and a secure PIN-based digital signature. In conclusion, the integration of digital signatures effectively solves the primary bottleneck by eliminating the need for physical presence, thus significantly enhancing operational efficiency, transparency, and accountability at PUSKOMLEKAD.
TOPIC MODELING OF PUBLIC DISCOURSE ON TWITTER ABOUT THE ASSET CONFISCATION BILL USING LATENT DIRICHLET ALLOCATION (LDA) Azka Bima Aditya; Ahmad Abdul Chamid; Rizkysari Mei Maharani
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.v8i2.477

Abstract

This study examines the structure of public discourse on Twitter regarding the Indonesian Asset Confiscation Bill, a policy initiative aimed at strengthening anti corruption enforcement and ensuring legal certainty. Moving beyond conventional sentiment classification, this research identifies how substantive public concerns are thematically organized within digital debate. A total of 14,319 cleaned and deduplicated tweets collected between January and September 2025 were analyzed using Latent Dirichlet Allocation with the optimal model configuration of nine topics selected based on coherence evaluation to ensure semantic interpretability. The findings reveal nine dominant thematic clusters, with law enforcement and regulatory enactment emerging as the primary focus, followed by legislative process dynamics, protest mobilization, party politics, and institutional accountability. These results indicate that online discourse is structured around normative concerns, particularly procedural clarity, fairness, and institutional legitimacy, rather than driven solely by emotional polarity. Scientifically, this study contributes by shifting the analytical emphasis from sentiment polarity toward systematic thematic mapping of digital political discourse using an optimized LDA framework tailored to Indonesian Twitter data characteristics. Practically, the findings provide policymakers with an evidence based monitoring instrument to identify priority public concerns, strengthen legislative communication strategies, and reduce interpretive ambiguity in sensitive regulatory deliberations.
APPLICATION OF THE ENTROPY–MARCOS METHOD IN A DECISION SUPPORT SYSTEM FOR SELECTING TOURIST DESTINATIONS IN SEMARANG Ekayulianti, Eka Yulianti; Saifur Rohman Cholil
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.v8i2.509

Abstract

Tourism is an important sector that supports regional economic growth. In the Semarang area, the increasing number of tourist destinations provides many options for visitors, but it can also make it difficult for tourists to determine the most appropriate destination based on several considerations. Therefore, this study aims to develop a decision support system for selecting tourist destinations using a multi-criteria decision-making approach. The criteria used in this study include travel time, ticket price, tourist facilities, and destination rating. The data were obtained from digital tourism information and questionnaire responses from 64 respondents. The Entropy method was used to determine objective criteria weights based on data variation, while the MARCOS method was applied to rank tourist destination alternatives based on their proximity to ideal and anti-ideal solutions. The results show that Kota Lama is the most recommended tourist destination with the highest final score, followed by Lawang Sewu and Pagoda Avalokitesvara. The validation results using Spearman correlation analysis produced a coefficient value of 0.9879, indicating a very strong agreement between the ranking results generated by the Entropy–MARCOS model and tourist preference rankings. This study contributes by integrating objective weighting and ranking methods using digital tourism data to provide a structured approach for tourism decision support systems.

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