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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 432 Documents
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Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods Suherman Achmad; Nazori AZ; Achmad Solichin
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.213

Abstract

Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. Based on the test results, the method can provide accurate and fast facial recognition results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.
An Interactive Medium to Introduce Sasando Traditional Music Using Multimedia Development Life Cycle Method Salam Irianto Nadeak; Yusmar Ali; Djaka Suryadi
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.214

Abstract

ActionScript-based programming is one of the software which includes applications that teachers widely use to create interactive learning media in the world of education. ActionScript-based programming technology is a type of graphic animation software that can create a graphic object that can be animated without using other supporting software. At present, there are many educational circles to expedite the process of learning activities, especially for students. Interactive learning media is one means of delivering subject matter that is very important to apply today. In implementing student learning at school, it is necessary to present a practical and theoretical learning system which is the main point in helping to develop student competence. One form of culture in Indonesia is the traditional musical instrument Sasando. Sasando belongs to the chordophone instrument because it is played by picking it. The form of Sasando itself is in the form of a guitar, violin or harp. The central part of the Sasando is in the form of a long bamboo tube. In the middle, rounded from top to bottom, there is a wedge to stretch the strings. Developing interactive learning media requires a software development method; one of the development methods used is the MDLC (Multimedia Development Life Cycle) method. Making the MDLC has five stages: Concept, Design, Material Collecting, Assembly, and Testing.
Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment Wargijono Utomo
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.215

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.
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.216

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.
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): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.217

Abstract

Smallpox syndrome, or 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 used in data analysis. 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%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores 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%.
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): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.219

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.
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): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.221

Abstract

Selecting the best employees aims to spur employee morale by improving performance and dedication. The selection of the 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, there was 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.
Internal Factor Analysis of Non-Performing Loans Using Multiple Linear Regression Method Muhammad Irfandi; Fitria Fitria
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.222

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.
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): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.223

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.
Analyzing the Level of Anxiety Disorders of Final-Year Students by Applying the Fuzzy Mamdani Method Virdyra Tasril; Muhammad Iqbal; Febby Madonna Yuma
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.226

Abstract

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still relatively high, coupled with the COVID-19 pandemic, which has impacted the economic sector. For this reason, research on stunting is still a critical topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm and build a website-based early detection application for toddler stunting cases. The research results using the k-Nearest Neighbor Algorithm trial obtained a relatively high accuracy of 92.45%. Implementing an early detection system for stunting cases has proven to help health workers classify toddlers as stunted or not. This application is also helpful as an archive and facilitates data reporting. The application has eight main menus: the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, and stunting early warning menu, which contains malnourished and stunted toddlers.

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