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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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UI/UX Designing of an Indonesian Language Writing Educational Game for Elementary School Students Using a Human-Centred Design Method Luthfi Syukriansyah Fitra; Chanifah Indah Ratnasari
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.v5i3.229

Abstract

Based on previous research conducted on elementary school students, it was found that there are still many errors in the use of EYD or KBBI rules, such as errors in the use of capital letters and the use of words. The lack of learning media outside of school triggers students' lack of understanding. Using technology-based learning media in games is exciting and can help improve students' understanding outside school. Using educational games as out-of-school learning media has several advantages, namely ease of access and higher student interest in playing. In developing games, an attractive appearance is needed and is the need of elementary school students. This is adequate and can increase students' interest in using the game as an out-of-school learning media. Therefore, an appropriate design method is needed to design the appearance to be attractive and by the needs of students, one of which is Human-Centered Design (HCD). This method involves users directly during the design process. By applying the HCD method, this research aims to design the interface of the "CerdasEYD" educational game that suits the needs and answers the problems that students have. Testing is carried out using the System Usability Scale to measure the success of the interface that has been designed. Through this test, the results obtained in the form of an average score of 95 indicate that the design has met the needs and answered the problems that students' problems.
Application Mobile-Based Augmented Reality for Endemic Animals of Central Kalimantan Herdy Andriksen; Donny Avianto
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.v5i3.230

Abstract

The existence of endemic animals typical of Central Kalimantan, such as orangutans and hornbills, is included in the protected species situation because the population numbers have increased slightly along with the clearing of land for the plantation sector, making orangutan and hornbill areas that should be beautiful and natural disappear. Because of this, a medium for conveying information and a unique introduction to the public is needed to know how important it is to know and know the various endangered species in Central Kalimantan to preserve animals. Therefore, this research aims to create an application for Central Kalimantan endemic animals using Mobile-based Augmented Reality to introduce Central Kalimantan's rare animals, starting from the area of residence, characteristics, leading food, and information about animal habitats. The application development process uses the Markerless Augmented Reality (AR) method, which displays 3D objects without using unique markers such as photos or images. The application development stage includes the planning, design, data collection, 3D object creation, application development and application testing using Blackbox testing with the Text Case method, which produces application testing descriptively explaining the application work process. The application for displaying 3D objects was tested in 5 trials, with an average of 5 seconds, and the marker appeared to display 3D objects. This application can have an impact on progress in the field of informatics as a medium for delivering information and learning media using Augmented Reality Markerless.
Implementation of Hybrid Method in Tourism Place Recommendation System Based on Image Features Steven Christ Pinantyo Arwidarasto; Desti Fitriati
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.v5i3.235

Abstract

In the industrial 4.0 era, there is an explosion of unstructured and structured data that produces broad and varied knowledge information that humans cannot process quickly. This issue makes the existence of recommendation systems meaningful. This system studies the existing information and provides suggestions according to the user's will. In the past, many recommendation systems have focused more on content-based filtering methods where recommendation results are similar based on the features of the Content that match the user's personality. This method limits the variety of information that is relevant to users. In addition, in the context of tourist attractions, many studies have not used image data that can contain many objects in one frame as a determining factor in providing recommendations. Therefore, in this study, the authors propose to add image features as one of the parameters of the recommendation system to determine the impact of using image features on the model performance. The best performance obtained is 0.364 RMSE metric using the Hybrid Image method.
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
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.v5i3.239

Abstract

AbstractFloods can occur at any time if the amount of river water discharge and rainfall intensity tends to be high, so preparations and ways of handling are needed to anticipate flooding quickly, precisely, and accurately for the Surabaya City Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is to calculate predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict using a time series dataset of rainfall and river water discharge in the Jagir River, Surabaya. This data is used to make predictions with the proportion of 70% training data and 30% testing data. Data normalization is performed in intervals of 0 and 1 using a min-max scaler and activated using ReLU (Rectified Linear Unit) and Adam Optimizer. The process continues by repeating the process to enter iterations, or epochs until it reaches the specified epoch (n). The data is then normalized to their original values and visualized. The model was evaluated and produced acceptable performance evaluation results for the rainfall variable, namely at epoch (n) = 75 for training data, namely a score of 0.054 for MAE and 0.099 for RMSE. In contrast, data testing was given a score of 0.041 for MAE and 0.091 for RMSE. As for the water discharge variable, the performance evaluation shows the difference between the training and testing data. Results of training data MAE = 11.10 and RMSE=18RMSE =18.61.61 at epoch (n) = 150. Results of data testing MAE = 11.37 and RMSE = 21.08 at epoch (n) = 100. These results indicate an anomaly that needs to be discussed in further research.
Ultra-Micro Lending Eligibility Support System With Exponential Comparison Method (MPE) Ninuk Wiliani; Mulyana Adi Supatra; Herry Wahyono
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.v5i3.240

Abstract

The process of providing credit can now be done quickly and closely through the presence of BRILink agents with additional facilities in addition to payment points, namely as partners of ultra-micro loans, which are now popularly called UMi Partners, where BRILink agents can distribute microloans with a loan range of 1 to 5 million. This is done by management as a financial inclusion program and as a revitalization of work in all operational work units (UKO). This research uses the Exponential Comparison Method (MPE) to determine credit granting decisions to optimize all existing information systems by implementing a system that can be used and run by UMi partners to improve the process of providing creditworthiness to their partners. The results of the calculations carried out by the system are manual calculations that have been carried out so that the results of this study can be applied correctly to produce creditworthiness that helps the credit-granting process.
Performance Improvement of K-Nearest Neighbor Algorithm in KIP Scholarship Recipient Selection Manzilur Rahman Romadhon; M. Faisal; M. Imamudin
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.242

Abstract

Abstract Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
Combination of Profile Matching and SAW Methods for College KIP Admission Riya Majalista; M. Izman Herdiansyah; Zaid Amin
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.243

Abstract

The KIP College program at Baturaja University has been running since 2020. The large number of people interested in this program has made the university that runs this program have difficulty making decisions about recipients of the KIP college program. The data is on interested participants in the KIP program studying at Baturaja University (UNBARA). The gap between the quota determined by the Ministry of Education, Culture, Research, and Technology and the number of registrants triggers difficulties for management in making decisions. This research aims to analyze the KIP Kuliah program selection results using the combination of Profile Matching and SAW methods. From the analysis of determining criteria and rankings using the Combination Method of Profile Matching and SAW, the results show the names of students who will occupy the UNBARA KIP program quota. The result of data calculations already obtained a value of 1,96667 with alternative data A208 in the name of Randi. Alternative A208 can be recommended as the recipient of the College KIP because it has the profile most appropriate to the specified criteria. So, it can be concluded that SPK, using the combination of Profile Matching and SAW methods, can be applied as a form of recommendation in decision-making in determining UNBARA KIP college program recipients.
Measuring the Level of Readiness in SDI Al-Hasaniah Students for Computer-Based Exams Using Technology Readiness Index Method Anggi Oktaviani; Deny Novianti; Dahlia Sarkawi; Muhamad Zul Fahmi
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.127

Abstract

The context of this research is that erratic rainfall can disrupt community activities, especially for traders who want to make sales. In addition, information about rainfall is also needed by farmers in determining planting patterns to get maximum yields. The purpose of this research is to be able to help farmers predict rainfall to get maximum crop yields. One method to be able to predict rainfall is Fuzzy Logic. This research will use the Tsukamoto Fuzzy method. In the research conducted this time, the author conducted monthly rainfall forecasting in Sleman Regency. Rainfall data in Sleman Regency from 2015 to 2022 will be used in this research. This research succeeded in getting a MAPE value of 49.31%. The result of this research is the highest monthly rainfall prediction in November, with a rainfall of 713.78 mm. At the same time, the lowest occurred in August, which amounted to 36.47 mm. This research only gets a MAPE value of 49.31%. So, it can be concluded that the Tsukamoto fuzzy method cannot predict rainfall well.
The Expert System for Diagnosing Respiratory Diseases for Cats Fitria Adyati Mardha; Ria Astriratma; Muslim, Muhammad Panji
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.v6i2.249

Abstract

Cats are the most common pet in Indonesia with a cat ownership rate of 47% (Rakuten Insight, 2021). Cat owners need to know and recognize the signs and symptoms of the diseases that often occur in cats, especially respiratory problems and diseases. Although vaccines in cats can significantly reduce the incidence of respiratory diseases, they do not eliminate infectious disease pathogens. During the COVID-19 pandemic, it was necessary to adjust health consultations that could reduce the transmission of COVID-19, which is the contactless method. In animal health, there is an online consultation through the WhatsApp platform between veterinarians and cat owners. Cat owners manually type every symptom experienced by the cat. However, there are several shortcomings in the online consultation, including that the symptoms described by the cat owner are unclear, so the diagnosis data is lacking and the consultation fee is quite expensive. Based on the problems that have been mentioned, the purpose of this study is to create an expert system for diagnosing respiratory diseases in cats using the Certainty Factor method. The result of this study is the availability of an expert system that can be used to diagnose respiratory diseases in cats.
Application of XGB Classifier for Obesity Rate Prediction Cahya Putri Buani, Duwi; Nuraeni, Nia
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.v6i1.260

Abstract

According to the Ministry of Health, the percentage of the population in Indonesia who are overweight is 13.5% for adults aged 18 years and over, while 28.7% are obese with BMI>=25 and obese with BMI>=27 as much as 15.4%. Meanwhile, at the age of children 5-12 years, 18.8% were overweight and 10.8% were obese. From these data, early detection of obesity levels is needed. From these data, prevention is needed so that the percentage of the population who experience obsediness can decrease, one of the efforts that can be done is to do early detection of obesity, to do early detection of obesity can be done using Machine Learning. In this study, it was discussed about the prediction of obestias levels using 7 (seven) models, namely Naive Bayes (NB), Random Forest (RF), K-NN, Decision Tree Classifier (DTC), SVM, XGB Classifier (XGB), Logistic Regression (LR) from the seven models used to predict the obesity level of XGB Classifier (XGB) which has the highest accuracy, namely Accurasy 0.96, with an f1-score of 0.96,  Precission and recall 0.96.

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