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INDONESIA
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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Decision Support System for Selection of the Best Fuel for Households Using the Weighted Product (WP) Method Yasmin Khairunnisa; Dewi Primasari; Nurul Kamilah
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.506

Abstract

Fuel is one of the most critical needs for the community in carrying out various household activities dominated by fuel oil. Meanwhile, the availability of fuel is increasingly running low. Most people in the Botumoito area in Gorontalo province work in the agricultural sector with low local income. Availability of fuels such as kerosene and gas there is quite challenging because there is no source or supply of gas either directly from the gas field or terminal. The government needs to make the right policy on fuel selection according to the problems in the region. This research aims to design and build a decision support system for selecting the best fuel and implementing the Weighted Product (WP) method into the system. The method used in designing and manufacturing this system is the waterfall method. This research results in a decision support system that can help fuel ranking. The ranking results are based on the magnitude of the Vector (V) value obtained from testing ten alternative fuels. Bioethanol occupies the top priority with a vector value of 0.152. This research can help consultants, fuel experts, and local governments speed up their work in determining the best household fuel according to their respective regions.
Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment Wargijono Utomo
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.512

Abstract

In post-pandemic recovery efforts, uncertainty arose due to the unresolved conflict between the Russia-Ukraine war. This conflict impacts world security stability and affects the economic, energy, and food sectors. This conflict also impacts humanity by causing death to civilians and military personnel, including children in Ukraine. The clustering analysis results of the impact of the Russian-Ukrainian war show losses and losses in personnel and war equipment, with three cluster optimization methods used through k-means. Of the two methods that can be recommended, namely elbow and Silhouette, both produce K=3. The profiling results show that losses or losses in Ukrainian personnel and war equipment are categorized into three clusters, with cluster one being the lowest category, cluster two being the very high category, and cluster three being the moderate category. This research is helpful for state agencies, international organizations (NGOs), and other stakeholders.
K-Means Binary Search Centroid With Dynamic Cluster for Java Island Health Clustering Muhammad Andryan; Muhammad Faisal; Ririen Kusumawati
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.511

Abstract

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with deficient health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies
Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.522

Abstract

Smallpox syndrome, also known as monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are both used in the data analysis process. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. In comparison to using conventional machine learning algorithms, the adagrad optimizer with learning rate 0.01 and 0.2 dropout has a higher value. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores when compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.
Extreme Programming Method for Integrated Service System Website Development in Rejosari Village Eka Supriyati; Muhamad Azrino Gustalika
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.527

Abstract

The Rejosari Village Hall provides a manual letter submission service which is sometimes problematic, including when residents are about to submit an application letter, they have to come directly to the village hall office while the residents are still out of town. Apart from that, there was no media information which resulted when they were going to submit the requirements for the letters they brought were not in accordance, then from the data collection, and the letters were still in the books. Therefore we need a service system for the submission of letters. This integrated service system for residents of Rejosari Village is a web-based information system, the use of technology in the form of a website makes it easier to receive all forms of existing information. The Extreme Programming (XP) method is applied in developing this system, a software engineering process that refers to an object-oriented approach. The stages of this method start from the planning, design, coding and testing stages using black box testing with descriptive analysis techniques, which produce tests in the form of a proportion value of 96.42% and have a possible interpretation. In addition, this system can have an impact on progress in the field of informatics in the form of information media as well as learning materials
The Best Employee Decision Support System Using the Analytical Hierarchy Process Method at PT ASDP Indonesia Ferry (Persero) Deny Novianti; Anggi Oktaviani; Dahlia Sarkawi; Aldyanto Aldyanto; Ahmad Faren Syahidan
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.454

Abstract

The selection of the best employees aims to spur employee morale in improving employee performance and dedication in the company. The selection of best employees is selected based on company criteria. PT ASDP Indonesia Ferry (PERSERO), the best employee criteria applied by the company are Work Quantity, Work Quality, Attendance, Teamwork, and Initiative. Employee assessment is carried out every month by the assessment team (Vice President (VP) and Manager). The problem faced is determining the best employees with criteria and alternatives that are calculated manually. This system is a Decision Support System (DSS) built using the Analytical Hierarchy Process (AHP) method. Previously, the evaluation process for selecting the best employees had never been done. Some of the problems encountered were the absence of an employee performance appraisal process, no appropriate selection method, and a Decision Support System (DSS) was not available that could make it easier to assess the selection of the best employees.
Application of Fuzzy C Means and TOPSIS in Warehouse Selection at PT Warung Islami Bogor Dewi Primasari; Khidir Zahid Muchtadiabillah; Freza Riana
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.517

Abstract

PT Warung Islami Bogor needs a warehouse to store goods that come from suppliers. Currently, the selection of warehouses is still done manually and is subjective. It is feared that this will lead to inaccuracies in renting the warehouse. So an application is needed to assist companies in choosing a warehouse. The fuzzy C-Means method can be used to classify warehouse data based on the characteristics of each group. After obtaining the next group is to make a rating of each group. One method that can be used is the TOPSIS method. The TOPSIS method can be applied to this application to rank the data warehouses that have been grouped. In the selection of this warehouse, there are several criteria. The criteria used are price, building area, distance from the head office (HO), parking area, and number of floors. The calculation process is done by dividing the warehouse data into several groups and ranking them to obtain the best recommendations. This application uses the PHP programming language with the Laravel framework—testing using a black box. Fuzzy C-Means and TOPSIS calculations show that Warehouse CCC is the best warehouse in Cluster 1 with a value of 0.797, and the Warehouse in Front of Gas Station Villa Bogor Indah is the best in Cluster 2 with a value of 0.613.
Internal Factor Analysis of Non-Performing Loans Using Multiple Linear Regression Method Muhammad Irfandi; Fitria Fitria
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.535

Abstract

Loans are the largest source of income from banks compared to other sources of income. To ensure bank continuity, Bank income must exist from interest on loans, reaching almost 95% of all bank activities. For companies and banks that apply loan differences, loans are receivables which are cash that is delayed in receipt. Having problem loans can weaken a bank's financial condition. In general, two factors cause problems with loans, namely internal and external factors of the bank. Bank internal factors can be controlled by banks, compared to external factors, to prevent problem loans. Therefore, in this study, an analysis of internal factors affecting problem loans was carried out. The internal factors analyzed are the things that become the process and the essential part of the loan process, which includes loan supervision, acceptance procedures, and loan guarantees. This analysis is carried out to minimize the risk of non-performing loans caused by the inner side of the organization. The method used for analysis is using multiple linear regression analysis. Multiple linear regression analysis analyzed the relationship between the three independent variables (loan monitoring, acceptance procedures, and loan guarantees) and one dependent variable (non-performing loans). Multiple linear regression analysis provides predictions of the value of the dependent variable if the value of the independent variable increases or decreases and describes the direction of the relationship between the independent variable and the dependent variable, whether each independent variable is positively or negatively related. Based on the analysis results, the influence of loan monitoring factors, acceptance procedures, and loan guarantees on problem loans can be concluded that there is an influence between loan supervision and acceptance procedures on problem loans. At the same time, there is no effect between loan guarantees on problem loans.
K-Means Clustering Method for Determining Waste Transportation Routes to Landfill Almas Nurfarid Budi Prasetyo; Maimunah Maimunah; Pristi Sukmasetya
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.540

Abstract

Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transportation schedules, etc. In this study, the data used are the name and address of the supplier, sub-district, coordinate point, and distance from the supplier's place to the landfill. After data collection, data preprocessing is done by dividing and selecting data based on sub-districts. Then the data is processed using the kmeans clustering algorithm to divide the route efficiency and the haversine formula algorithm to determine the closest distance between clusters. After the data has been successfully processed, the number of clusters is 4 for north Magelang, where each cluster will become a corridor with four routes. For central Magelang, 2 clusters with two routes, while for south Magelang, the results are 4 clusters with four routes. From these results, the evaluation results using silhouette score for data clustering of 3 sub-districts are 0.632560 for North Magelang, 0.640667 for Central Magelang, and 0.630186 for South Magelang. This method is expected to help in grouping routes and mapping supplier areas effectively and efficiently in the waste transportation process in Magelang City.
Website Evaluation of The Faculty of Industrial Technology Universitas Islam Indonesia Using the System Usability Scale Method Rafi Arribaath Alfaresy; Chanifah Indah Ratnasari
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.542

Abstract

In order to maintain and improve the quality of the website of the Faculty of Industrial Technology (FTI), Universitas Islam Indonesia (UII), usability testing is performed on the website using the System Usability Scale (SUS). This study aims to evaluate usability and analyze the user experience on the FTI UII website so that the faculty can follow up on it. Respondents consisted of 41 active FTI UII students. Respondents were asked to complete scenarios on the FTI website while being watched by examiners. They then filled out the SUS questionnaire, which had 10 statements and a Likert scale for answers. Using the SUS method, the test scores were 69.32. Based on these results, the acceptability of the FTI web is in the MARGINAL HIGH range, the adjective rating is at an OK level close to GOOD, the grade scale is in class C, and the Net Promoter Score (NPS) could be passive on website users. On the basis of these results, it can be concluded that the usability of the FTI UII website is acceptable to users but has not yet attained a maximum score; therefore, a user has not yet recommended the site to other users. This confirms that the FTI website requires additional enhancements.

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