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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 417 Documents
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Divorce Factor Classification Uses The C4.5 Algorithm Based On Particle Swarm Optimization Palupi, Endang
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.307

Abstract

Cases of household divorce increased in the West Java area during the Covid-19 pandemic. The pandemic has increased personal relationships and interactions between family members, and some families are using this opportunity to strengthen their relationships. However, increased family interaction can also result in increased conflict, leading to divorce. The author classifies divorce factors that have increased during the pandemic using the C4.5 Algorithm based on Particle Swarm Optimization (PSO). The main factors for divorce are economic factors that have hit during the pandemic coupled with unstable mental conditions resulting in poor communication and continuous fighting. So that the husband/wife leaves one of the parties, infidelity, and adultery, then domestic violence and ending in divorce. The dataset was taken from the West Java BPS website, and the author split the data, namely 80% training data and 20% testing data, to avoid overfitting. Research results on the classification of divorce factors during the pandemic using the C4.5 algorithm based on particle swarm optimization are an accuracy value of 87.50% and an AUC (Area Under Curve) value of 0.807, which is included in the good classification category.
Enhancing Ulos Batik Pattern Recognition through Machine Learning: A Study with KNN and SVM Chusna, Nuke L.; Wiliani, Ninuk; Abdillah, Achmad Feri
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.311

Abstract

This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology and texture analysis using the Gray-Level Co-occurrence Matrix (GLCM). The dataset consists of 1,800 images of Ulos fabric categorized into six main motif classes. The preprocessing process involves converting images to grayscale and extracting features with GLCM. Two classification algorithms, K-NN and SVM, were used for modeling, with evaluation using confusion matrix metrics and Area Under Curve (AUC). Evaluation results show that the K-NN model has an accuracy of 82%, while SVM has an accuracy of 57%. The analysis also highlights the superiority of K-NN in distinguishing Ulos fabric patterns. This research contributes to cultural preservation and the development of the creative industry by introducing an effective automated classification system for Ulos fabric patterns.
Machine Learning for Stroke Prediction: Evaluating the Effectiveness of Data Balancing Approaches Muhamad Indra; Siti Ernawati; Ilham Maulana
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.344

Abstract

Stroke occurs due to disrupted blood flow to the brain, either from a blood clot (ischemic) or a ruptured blood vessel (hemorrhagic), leading to brain tissue damage and neurological dysfunction. It remains a leading cause of death and disability worldwide, making early prediction crucial for timely intervention. This study evaluates the impact of data balancing techniques on stroke prediction performance across different machine learning models. Random Forest (RF) consistently achieves the highest accuracy (98%) but struggles with precision and recall variations depending on the balancing method. Decision Tree (DT) and K-Nearest Neighbors (KNN) benefit most from SMOTE and SMOTETomek, improving their F1-scores (11.21% and 9.18%), indicating better balance between precision and recall. Random Under Sampling enhances recall across all models but reduces precision, leading to lower overall predictive reliability. SMOTE and SMOTETomek emerge as the most effective balancing techniques, particularly for DT and KNN, while RF remains the most accurate but requires further optimization to improve precision and recall balance.
IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET Maulana, Ilham; Ernawati, Siti; Indra, Muhammad
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.348

Abstract

The issue of data imbalance in image classification often hinders deep learning models from making accurate predictions, especially for minority classes. This study introduces AugOS-CNN (Augmentation and Over Sampling with CNN), a novel approach that combines oversampling and data augmentation techniques to address data imbalance. The The Simpsons Characters dataset is used in this study, featuring five main character classes: Bart, Homer, Agnes, Carl, and Apu. The number of samples in each class is balanced to 2,067 using an augmentation method based on Augmentor. The proposed model integrates oversampling and augmentation steps with a Convolutional Neural Network (CNN) architecture to improve classification accuracy. Evaluation results show that the AugOS-CNN model achieves the highest accuracy of 96%, outperforming the baseline CNN approach without data balancing techniques, which only reaches 91%. These findings demonstrate that the AugOS-CNN model effectively enhances image classification performance on datasets with imbalanced class distributions, contributing to the development of more robust deep learning methods for addressing data imbalance issues.
Optimizing Deep Learning with Dimensionality Reduction for Analyzing the CuMiDa Brain Cancer Gene Expression Dataset Duwi Lufita Marfiana; Asmita Rani, Fatimah
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.350

Abstract

In the digital era, machine learning and deep learning have become indispensable tools for bioinformatics, particularly in analyzing high-dimensional gene expression data for cancer diagnosis and classification. This study leverages the CuMiDa brain cancer dataset, a curated microarray database with 54,676 genes and 130 samples, to evaluate the effectiveness of deep learning models integrated with dimensionality reduction techniques. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (TruncatedSVD) were employed to address the challenges of high-dimensional data, reducing noise and computational complexity. Three deep learning models—DNN, MLP, and TabNet—were implemented with various optimizers, including ADAM, RMSprop, and SGD. Results showed that TruncatedSVD outperformed PCA in minimizing loss, especially for MLP with LBFGS optimizers, achieving near-zero loss. TabNet demonstrated the highest classification accuracy (96%) with ADAM and RMSprop. Conversely, SGD exhibited suboptimal performance across models. These findings highlight the critical role of dimensionality reduction and optimizer selection in enhancing the efficiency and accuracy of deep learning models for cancer classification. This research provides a robust framework for improving diagnostic tools in computational oncology.
PREDICTION OF PIP RECIPIENTS USING K-NEAREST NEIGHBOR AT MI NURUL QOLBI Ningrum, Dea Fitra; Desti Fitriati
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.321

Abstract

Education is a key foundation in the development of quality human resources. However, the rising cost of education makes some children unable to attend school due to their parents' financial limitations. To address this problem, the government launched the Indonesia Smart Program (PIP) which provides education funding assistance to eligible students. This research aims to develop an Information System that can predict the eligibility of students to receive PIP assistance using the K-Nearest Neighbors (KNN) algorithm. The data used comes from all students of Madrasah Ibtidaiyah (MI) Nurul Qolbi in the 2022-2023 school year. This research methodology involves testing with a value of k=13 and model evaluation is done using split ratio and cross-validation techniques. The results showed an accuracy of 98.98% from various split ratios (10:90, 20:80, 30:70, 40:60) and an accuracy of 99.24% using the 10-fold cross-validation technique. The accuracy results show excellent performance and provide important significance in the development of prediction systems to help the selection process of aid recipients more efficiently and reduce the administrative burden for schools. However, its application on a wider scale still requires further research, especially to test its consistency and effectiveness in different contexts and with more diverse datasets.
UI/UX DESIGN DEVELOPMENT ON PT GALVA TECHNOLOGIES Tbk WEBSITE SERVICE TRACKING WITH THINKING DESIGN METHOD Oktaviani*, Anggi; Sarkawi, Dahlia; Yusuf, Jevan; Novianti, Deny
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.338

Abstract

This research uses a qualitative approach with a focus on the development of the UI/UX design of the Galva Service Tracking website using the Design Thinking method. The main goal of this research is to improve the user experience by creating a more intuitive and efficient design. Here are the details of the methods used in this study. In addition, another purpose of this study is to develop a UI/UX design on the Service Tracking website of PT Galva Technologies Tbk using the Design Thinking method. With the application of information technology that is increasingly crucial, UI/UX design improvements are expected to increase user satisfaction and the effectiveness of the services provided. Through the Design Thinking approach, this research involves users in every stage of development starting from empathy, problem definition, ideation, prototype making, to testing. The System Usability Scale (SUS) method is used to evaluate the usability of a website. The results of the study show that the application of Design Thinking has succeeded in improving the quality of website design and function, as well as providing a better and more satisfying user experience. Thus, it is hoped that the Service Tracking website of PT Galva Technologies Tbk can meet the needs and expectations of users more optimally.
SENTIMENT ANALYSIS OF USER REVIEWS BRI MOBILE APPLICATION WITH GRADIENT BOOST METHOD Nanang Ruhyana; Salsabila, Kanita; Agung, Andri; Mardiana, Tati
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.342

Abstract

BRI Mobile application is a digital banking service launched in 2019 by Bank Rakyat Indonesia, which provides facilities such as mobile banking, internet banking, and electronic money. The presence of this application aims to facilitate customers in accessing and managing financial services efficiently through mobile devices. Reviews have become a very important source on platforms such as Google Playstore become a very important source of information to evaluate and improve service quality. However, manually identifying sentiment representations from thousands of reviews is a time-consuming and inefficient process. This research aims to perform sentiment analysis automatically on BRI Mobile application user reviews by utilizing text mining methods. The sentiment classification process is carried out using the Gradient Boosting algorithm approach and initial analysis using the VADER Sentiment method to provide initial data labelling. Based on the classification results, 344 data with positive sentiment, 333 data with negative sentiment, and 333 data with neutral sentiment were obtained. The model built was then evaluated using the accuracy metric, and an accuracy value of 97% was obtained. The results of this research are expected to be a strategic input for application developers in understanding user perceptions more objectively and efficiently.
Prediction Of Flight Delays Using Feature Engineering, Catboost, And Bayesian Optimization To Improve Model Performance Maulana, Ilham; Ernawati, Siti; Wati, Risa
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.346

Abstract

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.
Digitalization Of Survey And Mapping Service Processes Through The Development Of A Web-Based System Ernawati, Siti; Hermawan, Deni
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.352

Abstract

PT. Cakrawala Pilar Nusantara is a private company engaged in survey and mapping consultancy services. Several challenges have been identified in its business processes, one of which is that service delivery for collaboration is still conducted manually. This study adopts a Design Science Research (DSR) approach, focusing on the development of an artifact in the form of a web-based service system for PT. Cakrawala Pilar Nusantara, in accordance with the objectives of the research. The DSR methodology consists of the following stages: Problem Identification and Research Motivation, Definition of Solution Objectives, Design and Development of the Artifact, Demonstration, Evaluation, and Communication. Data collection was carried out through observation and interviews with relevant parties. System design visualization was conducted using UML, represented by use case diagrams and activity diagrams. The programming language used is PHP, implementing the CodeIgniter framework. System testing was performed using the black-box testing method.The result of this research is a web-based information system that facilitates data entry, quotation submissions, reporting, and improves service processes by transforming manual record-keeping into a computerized system. The presence of this information system provides greater convenience for the company in managing its operational activities.

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