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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 8 Documents
 1 
Search results for , issue "Vol. 6 No. 3 (2024): June 2024" : 8 Documents clear
Integration of Adasyn Method with Decision Tree Algorithm in Handling Imbalance Class for Loan Status Prediction Ami Rahmawati; Yulianti, Ita; Mardiana, Tati; Pribadi, Denny
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.285 KB) | DOI: 10.34288/jri.v6i3.299

Abstract

Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which might influence the final results. This research uses data mining techniques by developing modeling on loan status prediction datasets. The stages in this research include data preprocessing, modeling, and evaluation using accuracy metrics and ROC graphs. In this analysis, it is known that there is a class imbalance in the processed dataset, so an oversampling technique must be carried out. This research uses the ADASYN (Adaptive Synthetic) Oversampling technique to ensure the class distribution is more balanced. Then, the ADASYN technique is integrated with the Decision Tree Algorithm to build a prediction model. The research results show that the two methods can increase prediction accuracy by 12.22%, from 73,91% to 85.22%. This improvement was obtained by comparing the accuracy results before and after using the ADASYN Oversampling technique. This finding is important because it proves that implementing such integration modeling can significantly improve the performance of classification models and provide strong potential for practical application in helping more effective loan status predictions.
Comparison of Decision Tree, Naive Bayes and Random Forest Algorithm to get the Best Performance of Algorithm for Customer Credit Classification Suryani, Indah
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (818.138 KB)

Abstract

Credit is a potential income and the most significant business operation risk for a bank. Bad credit has become an ingrained problem in the banking world. Therefore, this research aims to classify customer data profiles who have the opportunity to be able to apply for a loan or not to reduce the risk of bad credit in the future by classifying using three commonly used data mining algorithms, namely the Decision Tree algorithm, Naïve Bayes and Random forest. The research was conducted using an experimental, descriptive method by testing the accuracy of the three methods to get the best performance. Based on the experiments' results, the accuracy performance with the confusion matrix was 73.20% for the Decision Tree algorithm, then the accuracy for the Naive Bayes algorithm was 74.4% and Random Forest was 77.4%. Meanwhile, performance evaluation is based on the Receiver Operating Characteristics (ROC) curve by looking at the resulting Area Under Curve (AUC) value of 0.717 for the Decision Tree algorithm, while Naive Bayes produces an AUC value of 0.741 and the largest is Random Forest at 0.796. So it can be concluded that the best performance of the classification carried out is the one that uses the Random Forest algorithm. Then, from the validation results using the T-Test of the three methods being compared, Random Forest produces a significant difference in the level of accuracy compared to the accuracy produced by the Decision Tree, namely with an alpha value of 0.031.
Implementation of the FP-Growth Algorithm on Spare Parts Supply Requests Amsury, Fachri; Nanang Ruhyana; Riyadi, Andri Agung; Bayhaqy, Achmad
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (995.929 KB) | DOI: 10.34288/jri.v6i3.302

Abstract

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.
Comparison of the Application of Linear Regression with Sliding Window Validation and K-Fold Cross-Validation for Forecasting Covid-19 Recovered Cases Setiyorini, Tyas; Frieyadie, Frieyadie
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.v6i3.288

Abstract

The increase in confirmed cases and deaths due to Covid-10 continues to spread and increase day by day throughout the world. This has resulted in a world health crisis that impacts all sectors of life. The government declared a movement to suppress the spread of Covid-19, so it is necessary to understand the pattern of Covid-19 problems. Researchers contribute scientifically to finding patterns of death or recovery due to COVID-19 by applying Machine Learning methods. The Linear Regression and Sliding Window preprocessing methods are appropriate for forecasting time series data. This research obtained RMSE results at 0.320 with linear regression with sliding window validation and RMSE at 0.320 with linear regression with K-Fold cross-validation. This proves that Linear Regression with Sliding Window Validation can improve performance much better than k-fold cross-validation in forecasting COVID-19 recovery cases in China. The sliding window validation method has been proven to increase accuracy for forecasting with time series data compared to other standard preprocessing methods, namely K-Fold cross-validation. In the future, further research is needed to test different types of time series data by comparing the application of sliding window validation and K-Fold cross-validation or developing other validation models.
User Experience Using the Planes Method on the BUKUERP Application Bety Wulan Sari; Donni Prabowo
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.v6i3.291

Abstract

This study applies the five planes method to comprehensively investigate a particular enterprise resource planning (ERP) application. To improve overall usability and user satisfaction, the organizational requirements component is the specific focus of this study. This research utilizes the five planes method, which consists of five UX design elements: strategy, scope, structure, skeleton, and surface. A review of the methodology, processes, and frameworks of similar research within user experience and user experience analysis is conducted. Each component makes The addressed problems more definite, understandable, and explicit. The System Usability Scale (SUS) is used in this study to examine and assess the procedure for raising user satisfaction. This study explains the significance of a structured approach emphasizing users in the application development, particularly in digitizing an organization's business.
Sentiment Analysis of E-Grocery Application Reviews Using Lexicon-Based and Support Vector Machine Aryanti, Riska; Fitriani, Eka; Royadi, Royadi; Ardiansyah, Dian; Saepudin, Atang
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.v6i3.301

Abstract

This research aims to conduct sentiment analysis of e-grocery application reviews using the Support Vector Machine (SVM) algorithm. Sentiment analysis is used to distinguish between positive and negative reviews by users who have provided reviews so that an evaluation of the services offered can be made. This research uses scraping techniques to obtain all the needed review data, focusing only on reviews of the Segari and Sayurbox applications. Datasets were collected from reviews using a library in Python, namely, google-play-scraper, obtained by the sayurbox application 4235 reviews and the segari application 5575. The dataset collected does not yet have a label, and the labeling process is impossible to perform manually by looking at the reviews one by one because it takes a long time and requires an expert in the field of language who can interpret the reviews and group them into positive and negative sentiments. Therefore, the sentiment-labeling process applies a lexicon-based method that works based on the inset lexicon dictionary by calculating each review's polarity value. The analysis process of this research uses the SVM algorithm because the SVM method has been proven to provide consistent and accurate results in various classification tasks, including sentiment analysis. The results show that the lexicon-based method and SVM produce good accuracy in determining the sentiment of e-grocery reviews, with a vegetable box application accuracy rate of 94%. In comparison, the segari application accuracy rate reached 97%.
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.

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