Scientific Journal of Informatics最新文献

{"title":"Smart System on Two-dimensional Shapes Recognition Application for Kindergarten Students","authors":"Teja Endra Eng Tju, Elizabeth Nurmiyati Tamatjita","doi":"10.15294/sji.v11i1.47494","DOIUrl":"https://doi.org/10.15294/sji.v11i1.47494","url":null,"abstract":"Abstract. Kindergarten-aged children are going through an important period of cognitive development, such as the ability to think concretely, including recognizing simple geometric shapes such as circles, triangles, and squares. However, many children find it difficult to understand the basic concepts of two-dimensional shapes.Purpose: It is necessary to develop prototype learning aids in the form of intelligent systems in two-dimensional shapes applications for kindergarten students, which utilize information technology and object visualization directly through cameras on smartphones. This is expected to increase children's learning motivation and help strengthen their understanding of two-dimensional shapes.Methods: The research combines Waterfall and Agile methodologies, tailoring them to four stages: plan and discovery, analysis and design, application development, and testing. Testing gathers accuracy with 120 smartphone-collected data points for square, triangle, circle, and pentagon shapes. Also, usability testing based on learnability, efficiency, memorability, error handling, and satisfaction, was obtained from six kindergarten teacher questionnaires and quantitatively processed.Results: The application achieves an accuracy rate of approximately 79%. Notably, accuracy decreases with fewer corners, mainly due to low resolution or lack of focus, resulting in simplified detected shapes. Regarding usability, it is evident that the application has received positive feedback from users, particularly kindergarten teachers, who have given it an average score of 78.83.Novelty: Distinguished from previous research, the novelty of this study resides in its ability to capture objects through a camera, eliminating the need for predefined shapes within the application, and innovating by creating an educational tool aligned with the kindergarten curriculum to recognize two-dimensional shapes. The research contribution is the creation of an innovative learning tool for kindergarteners, merging smartphone technology with real-world objects to teach two-dimensional shapes, thus integrating technology into early childhood education effectively, which has urgency in efforts to improve the quality of learning.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies","authors":"Gunawan Gunawan, Wresti Andriani, Sawaviyya Anandianskha, Aang Alim Murtopo, Bangkit Indarmawan Nugroho, Naella Nabila Putri Wahyuning Naja","doi":"10.15294/sji.v11i1.47935","DOIUrl":"https://doi.org/10.15294/sji.v11i1.47935","url":null,"abstract":"Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"10 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19","authors":"A. Susanto, C. A. Sari, E. H. Rachmawanto, I. U. W. Mulyono, Noorayisahbe Mohd Yaacob","doi":"10.15294/sji.v11i1.47305","DOIUrl":"https://doi.org/10.15294/sji.v11i1.47305","url":null,"abstract":"Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"45 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140509486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Smart Reward for Serious Game Activity Design","authors":"Hanny Haryanto, Umi Rosyidah, Acun Kardianawati, E. Z. Astuti, Erlin Dolphina, Ronny Haryanto","doi":"10.15294/sji.v10i3.44051","DOIUrl":"https://doi.org/10.15294/sji.v10i3.44051","url":null,"abstract":"Purpose:  Serious game has been widely considered to be a potential learning tool, due to its main advantage to provide a fun experience in learning. The experience is supported mainly by in-game activities, where feedback is given in the form of rewards. However, rewards often don't work well due to various factors, for example, rewards are always the same, so they are monotonous. We use Appreciative Learning as underlying concept for activity design and fuzzy logic to create the reward behavior, called Fuzzy Smart Reward.Methods: We use Appreciative Learning as underlying concept for activity design and fuzzy logic to create the reward behavior. Appreciative Learning activities consists of Discovery, Dream, Design and Destiny. We propose fuzzy-based smart reward for those activities. The smart reward takes player achievement in each activity as input for the fuzzy inference system and give the dynamic reward as output.Result: A game prototype is developed as a test subject. The result shows that the smart reward could dynamically adjust the reward based on game conditions and player performance. Test conducted using Game Experience Questionnaire get the score 3.3 out of 4.Novelty:  There aren't many studies on dynamic rewards in structured reward systems; the majority of studies remove dynamic rewards from reward systems. In our research, a \"smart reward\" is a dynamic reward in a structured reward system that is created using artificial intelligence and is based on activities for appreciative learning. The use of Fuzzy Logic for structured reward behavior is also very rare. ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44487659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter","authors":"E. Z. Astuti, C. A. Sari, Mutiara Syabilla, Hendra Sutrisno, E. H. Rachmawanto, Mohamed Doheir","doi":"10.15294/sji.v10i3.43438","DOIUrl":"https://doi.org/10.15294/sji.v10i3.43438","url":null,"abstract":"Purpose: As digital technology advances, society needs to convert physical text into digital text. There are now many methods available for doing this. One of them is OCR (Optical Character Recognition), which can scan images [1]–[4] containing writing and turn them into digital text, making it easier to copy written text from an image. Text recognition in images is complex due to variations in text size, color, font, orientation, background, and lighting conditions.Methods: The technique of text recognition or optical character recognition (OCR) in images can be done using several methods, one of which is a neural network or artificial neural network. The artificial neural network method can help a computer make intelligent decisions with limited human assistance. Intelligent decisions can be made because the neural network can learn and model the relationship between nonlinear and complex input and output data. In this research, the scaled conjugated gradient is applied for optimization. SCG is very effective in finding the minimum value of a complex function, but it takes longer than some other optimization algorithms.Result/Findings: The dataset used is an image with a size of 28 x 28 which is changed in dimension to 784 x 1. This research uses 4000 epochs and obtained the best validation result at epoch 3506 with a value of 0.0087446. Results: From the statistical test results, the effect of perceived usefulness on ease of use has the highest level of influence, obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtained a test value of 1.2.Novelty:  In this article, Gaussian filter is used as feature extraction to improve yield. Character detection results using a Gaussian filter are known to be almost 10% higher than those using only a neural network. The result with the Neural Network alone is 82.2%, while the Neural Network-Gaussian Filter produces 92.1%.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46262058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coastal Sentiment Review Using Naïve Bayes with Feature Selection Genetic Algorithm","authors":"O. Somantri, R. Maharrani, Santi Purwaningrum","doi":"10.15294/sji.v10i3.43988","DOIUrl":"https://doi.org/10.15294/sji.v10i3.43988","url":null,"abstract":"Purpose: The tourism potential in the maritime sector can be Indonesia's mainstay at this time, especially in enjoying the charm of the natural beauty of the coast as people know Indonesia is an archipelagic country. The purpose of this study is to find the best model by applying the feature selection genetic algorithm (GA) and Information Gain (IG) to get the best Naïve Bayes (NB) model and the best features to produce the best level of sentiment classification accuracy.Methods: The stages of the research were carried out by going through the process of searching, pre-processing, analyzing research data using the Naïve Bayes model and optimizing genetic algorithms, validating data, and model evaluation.Result: The experimental results show that the best model is naïve Bayes based on information gain and the genetic algorithm yields an accuracy rate of 86.34%.Novelty: The main contribution to this research is proposing a new model of the best NB optimization model by applying an optimization algorithm in the search for feature selection to increase sentiment classification accuracy.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44723829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection","authors":"Ahmad Solikhin Gayuh Raharjo, E. Sugiharti","doi":"10.15294/sji.v10i3.44137","DOIUrl":"https://doi.org/10.15294/sji.v10i3.44137","url":null,"abstract":"Purpose: There are deaf people who have problems in communicating orally because they do not have the ability to speak and hear. The sign system is used as a solution to this problem, but not everyone understands the use and meaning of the sign system, even in terms of the alphabet. Therefore, it is necessary to classify a sign system in the form of American Sign Language (ASL) using Artificial Intelligence technology to get good results.Methods: This research focuses on improving the accuracy of ASL alphabet classification using the VGG-19 and ResNet50 architecture of the Convolutional Neural Network (CNN) method combined with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the detail quality of images and Canny Edge Detection to produce images that focus on the objects in it. The focused result is the accuracy value. This study uses the ASL alphabet dataset from Kaggle.Result: Based on the test results, there are three best accuracy results. The first is using the ResNet50 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 99.9%, followed by the ResNet50 architecture, CLAHE + Canny Edge Detection, and an image size of 128 x 128 pixels with an accuracy of 99.82 %, and in third place are the VGG-19 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 98.93%.Novelty: The novelty of this study is the increase in the accuracy value of ASL image classification from previous studies.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48586063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method","authors":"Arnita Arnita, F. Marpaung, Z. A. Koemadji, M. Hidayat, Azi Widianto, Fitrahuda Aulia","doi":"10.15294/sji.v10i2.44059","DOIUrl":"https://doi.org/10.15294/sji.v10i2.44059","url":null,"abstract":"Purpose: The identification and selection of food to be consumed are critical in determining the health quality of human life. Our diet and the illnesses we develop are closely linked. Public awareness of the significance of food quality has increased due to the rising prevalence of degenerative diseases such as obesity, heart disease, type 2 diabetes, hypertension, and cancer. This study aims to develop a model for food identification and identify aspects that can aid in food identification.Methods : This study employs the convolutional neural network (CNN) approach, which is used to identify food objects or images based on the detected features. The images of thirty-five different types of traditional, processed, and western foods were gathered as the study’s input data. The image data for each type of food was repeated 100 times to produce a total of 3500 images.. Using the color, shape, and texture information, the food image is retrieved. The hue, saturation, and value (HSV) extraction method for color features, the Canny extraction method for shape features, and the gray level co-occurrence matrix (GLCM) method for texture features, in that sequence, were used to evaluate the data in addition to the CNN classification method.Result:The simulation results show that the classification model’s accuracy and precision are 76% and 78%, respectively, when the CNN approach is used alone without the extraction method. The CNN classification model and HSV color extraction yielded an accuracy and precision of 51% and 55%, respectively. The CNN classification model with the Canny texture extraction method has an accuracy and precision of 20% and 20%, respectively, while the combined CNN and GLCM extraction methods have 67% and 69% success rates, respectively. According to the simulation results, the food classification and identification model that uses the CNN approach without the HSV, Canny, and GLCM feature extraction methods produces better results in terms of accuracy and precision model.Novelty: This research has the potential to be used in a variety of food identification applications, such as food and nutrition service systems, as well as to improve product quality in the food and beverage industry.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46395456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial Least Square Algorithm (PLS) with Technology Acceptance Model (TAM) in User Analysis of Public Health Center Management Information System (SIMPUS) Applications","authors":"Sri Mulyono Mulyono, W. Syafei, Retno Kusumaningrum","doi":"10.15294/sji.v10i2.44148","DOIUrl":"https://doi.org/10.15294/sji.v10i2.44148","url":null,"abstract":"Purpose: The most important information system application at the health center is the health center management information system or can be called SIMPUS. The SIMPUS is an application program specifically designed to help facilitate recording of patient data, processing and presenting data into information in a short time. With the SIMPUS application, it is necessary to examine whether the application is very helpful for users in completing work at the public health center or Puskesmas. Therefore, the purpose of this study is to analyze the SIMPUS application users by combining the Partial Least Square (PLS) algorithm with the Technology Acceptance Model (TAM) method.Methods: SIMPUS application user analysis is carried out using a combination of the Partial Least Square (PLS) algorithm with the Technology Acceptance Model (TAM) method. The variables used are Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using, Behavioral Intention to Use, and Actual System Use. Data collection techniques by distributing closed questionnaires and taking samples with the solvency formula. Sampling was carried out using a multistage random sampling technique, the number of 12 Puskesmas in each Puskesmas from the calculation results determined 40 samples.Result: From the statistical test results, the effect of the perceived usefulness on the ease of use has the highest level of influence, which obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtaining a test value of 1.2.Value: Analysis and testing of variables that influence user acceptance of the SIMPUS application using the Partial Least Square (PLS) algorithm and the Technology Acceptance Model (TAM) approach, that acceptance of the SIMPUS application is influenced by the level of usability and ease of use of the application.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41602594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metadata Modeling of LoRa Based Payload Information for Precision Agriculture Tea Plantation","authors":"E. Nugroho, Taufik Djatna, I. S. Sitanggang, I. Hermadi, A. Mulyana, S. Wahjuni, Heru Sukoco","doi":"10.15294/sji.v10i2.43432","DOIUrl":"https://doi.org/10.15294/sji.v10i2.43432","url":null,"abstract":"Purpose: The purpose of this study is to model the metadata of Payload Information on Agriculture Drones which consists of the results of images computational and the Onboard system of the Drone.Methods: The stages of the research were carried out with the process of forming Payload information metadata from the Agriculture Drone with sensors/actuators based on the architecture and computing with Image Processing or Computer Vision on the camera captures. This study describes the metadata modeling process formed from the Internet of Things system with Drone and GCS communication based on the Long Range or Long-Range Wide Area Network protocols with Payload information consisting of drone data and image computation results. Result: The result obtained is the formation of Payload information from LoRa-based Drones with a frame size of 142 bytes. Novelty: Payload information is formed into a metadata model indicator with the formation scheme being part of the tea plantation dataset. The metadata model will be test expected to obtain field data on Drones and GCS communication in the LoRaWAN Network in tea plantations which are rural environments. ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44099040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}