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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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Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases Ami Rahmawati; Yulianti, Ita; Nurajizah, Siti
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.261

Abstract

In Indonesia, coffee is one of the plantation products with a relatively high level of productivity and is a source of foreign exchange income for the country. However, unfortunately, certain factors can threaten productivity and quality in cultivating coffee plants, one of which is rust leaf disease. This disease causes disturbances in photosynthesis, thereby reducing plant yields. Therefore, to maintain and control productivity in coffee cultivation, this research carried out the process of observing coffee leaf images through segmentation using the Otsu Thresholding and Mean Denoising methods. The entire series of processes in this research was carried out using the Python programming language and succeeded in providing output in the form of image comparisons showing areas affected by Rust Leaf disease using the Otsu thresholding method alone and the Otsu thresholding method combined with a non-local means denoising algorithm. The test results prove that the Otsu thresholding method with the non-local means denoising algorithm has a smaller MSE value. It is the most optimal method for handling coffee leaf disease image segmentation with an accuracy level of 88%. It is hoped that this research can support farmers in providing insight into early detection of coffee plant diseases and increasing productivity through visual analysis.
Comparison of the Application of Neural Networks with K-Fold Cross Validation and Sliding Window Validation for Forecasting Covid-19 Recovered Cases Tyas Setiyorini
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.263

Abstract

The Covid-19 virus first appeared in China resulting in millions of confirmed cases, deaths and recovered cases to date. The spread and increase in the death rate due to Covid-19 is very worrying. Health workers and researchers continue to struggle to improve recovery from Covid-19 cases. There is a need for future forecasting to predict recovery from cases that occur, so that the public or government can understand the spread, take precautions and prepare for action as early as possible. Several previous studies have carried out forecasting the future impact of Covid-19 using Machine Learning methods. Neural Network and Sliding Window are appropriate methods for forecasting time series data. In this research, it has been proven that the application of a Neural Network with a Sliding Window can improve performance which is much better than without using a Sliding Window in forecasting Covid-19 recovery cases in China.
Evaluating the User-Friendliness of a Mobile Application for Outpatient Food Monitoring: A System Usability Scale (SUS) Approach Sulistiyono, Mulia; Habib Dwi Prajoto; Bernadhed
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.264

Abstract

To maintain and enhance the quality of the Mobile Food Intake Control Application for Outpatients in the Hospital, usability testing must be conducted using the System Usability Scale (SUS). This research aims to evaluate usability and analyze user-friendliness for further action by the Hospital. The respondents consist of 138 outpatient patients. Testing is carried out by requesting respondents to perform scenarios on the Mobile Food Intake Control Application, observed directly by the examiner. Subsequently, respondents fill out a questionnaire containing ten statements with Likert scale responses. The average SUS score of 87.0471 indicates excellent acceptance of the application, and the user rating suggests that the application meets user expectations sufficiently. However, user speed in using the application and focusing on its features are still considered normal, and the error rate falls within acceptable limits.
Application of Data Mining Using Methods K-Means Clustering for Clustering Baby Goods Rental Patterns (Case Study: Baby Kha House Store) Roja' Putri Cintani; Shafa Aurelia Putri; Desti Fitriati
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.265

Abstract

A baby item rental business is a practical option for parents who want to fulfill their baby's needs without buying them. Babykhahouse is one of the stores that offer rental services for various kinds of mother, baby, and child equipment. As the volume of data related to rental transactions increases, it is also increasingly difficult to know and understand the rental patterns found at the Babykhahouse store. This research aims to get a rental pattern that can later be a consideration for the store in determining promos and adding stock items. In handling these problems, data mining methods, especially clustering, are applied to group data and classify it based on certain groups. The clustering method used in this research is K-Means Clustering, which generates clusters to find similar rental patterns. In this study, 2 (two) types of clusters were formed, where, based on the 2 (two) clusters, it will be known which products have high and low rental rates. Based on the research, the results are 100 data in cluster 0, or the unsold cluster, and 64 in cluster 1, or the sold cluster. Products included in cluster 1 or in-demand clusters are products with a high level of sales.
Enhancing Financial Technology Operations: A Comprehensive Evaluation Using COBIT 2019 Framework Sherly, Sherly; Fianty, Melissa Indah
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.267

Abstract

This research aims to enhance information technology (IT) governance in a financial technology company by focusing on peer-to-peer lending services. The main challenges faced by the company involve a lack of system design details, leading to post-implementation imperfections and negative impacts on business process performance, including unnecessary delays and adjustments. The lack of transparency in system evaluation is also a hindrance caused by incomplete recording of test results. Therefore, this research aims to address these challenges by utilizing the COBIT 2019 framework. The study employs a qualitative approach, utilizing data obtained through interviews and literature studies supported by the COBIT Tool Kit. The analysis is conducted on three main objectives: security management, solution identification, and IT change management, to identify disparities between the current status and desired targets. The analysis results highlight the need for improvements in specific aspects, including the lack of system design details, more precise information in the change process, and deficiencies in recording test results. Recommendations for improvement involve the development of more detailed guidelines for system design, enhanced documentation of changes, and improvements in testing instructions and result reporting. Additionally, recommendations focus on enhancing capabilities through proactive evaluation, refining security plans, developing more adaptive solution acquisition strategies, and improving testing practices. Thus, this research underscores the importance of strategic improvements within the IT and Information Systems governance framework to shape a more effective and transparent operational environment in Financial Technology companies.
Classification of Patient Satisfaction Level on Health Services Using the C4.5 Algorithm Sriani; Aidil Halim Lubis; Sofiah
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.283

Abstract

Quality health services are related to patient satisfaction. Patient satisfaction can be used as a benchmark for improving the quality of health services. Problems often occur when implementing health techniques, such as service problems at the Restu Clinic. Patients and their families indicated that although the Restu Clinic was established with adequate facilities, it had not yet reached the maximum level of service. These indications include long waiting times for examinations, a lack of thoroughness by medical personnel, and services that are not timely. Service quality cannot be separated from the dimensions that are the core of quality services, which are expected to meet patient needs. Patient satisfaction is considered an important indicator of good quality. This research will only discuss four aspects of service quality, which are reliability, responsiveness, assurance, and empathy, from health workers at the Restu Clinic. The C4.5 algorithm is known to be superior in producing decision trees that efficiently solve discrete and numerical variables and provide satisfactory accuracy. Therefore, the author conducted a study to assess service quality using the C4.5 algorithm. This research aims to determine the factors that influence the quality of health services and to know patient satisfaction with health services at the Restu Clinic. Knowing the intensity of patient satisfaction with services at the Restu Clinic can improve the quality of optimal services and gain patients' trust in government agencies.
Decision Support System for Outstanding Students’ Selection Using TOPSIS Suryani, Irma; Sani, Asrul; Budiyantara, Agus; Pusparini, Nur Nawaningtyas
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.285

Abstract

In the school environment, determining outstanding students holds significant importance. High academic achievement among students and a low failure rate reflect the overall quality of education. Based on the interviews, it is known that the assessment process for outstanding students at school still needs to be revised, and the current decision-making system needs to consider other factors, resulting in suboptimal selection processes. To address this issue, implementing a Decision Support System (DSS) is necessary to assist the school in selecting the best students. DSS is an interactive system providing access to data and modelling information, designed to support decision-making in both structured and unstructured situations. This DSS will be designed using the Technique for Order of Preferences by Similarity to an Ideal Solution (TOPSIS) as the alternative ranking method. The final results indicate that using the TOPSIS method in this decision support system can improve efficiency and accuracy in selecting outstanding students in the school environment.
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%.

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