{"title":"SMARIoT: Augmented Reality for Monitoring System of Internet of Things using EasyAR","authors":"Muhamad Aldy Bintang, R. Harwahyu, R. F. Sari","doi":"10.1109/ICICoS51170.2020.9299088","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299088","url":null,"abstract":"Technology development delivers the Internet of Things (IoT) to control and monitor the digital system. Augmented Reality (AR) has the ability to display 3D virtual objects into real environments. In general, the interface on IoT monitoring uses a static computer screen, in a dashboard model. This research focuses on integrating interfaces in the IoT monitoring system using augmented reality. Our work includes making the applications for mobile devices using EasyAR and building the connectivity to an online database. We also create the prototype of an IoT device to send data. We measured the system speed displaying the object with AR. Although the data stored online, we find out how effective this system is compared to its predecessor, which is using the static dashboard. We studied and measured the convenience of the use and the user experience.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128501017","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":"Balinese Carving Recognition using Pre-Trained Convolutional Neural Network","authors":"I. W. A. S. Darma, N. Suciati, D. Siahaan","doi":"10.1109/ICICoS51170.2020.9299021","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299021","url":null,"abstract":"The preservation of Balinese carvings in traditional buildings is needed to preserve by collecting Balinese carving data. Balinese carving data collection is an attempt to save important patterns in Balinese carvings to become a reference for repair Balinese carvings that are beginning to erode by age. Balinese carving recognition is the first step to preserve cultural heritage by collecting Balinese carving motifs on traditional sacred buildings. In this study, we compare the performance of Convolutional Neural Network pre-trained models for Balinese carving recognition. We use transfer learning using four pre-trained models, i.e., MobileNet, Inception-v3, VGG16, and VGG19, to train the recognition model. In the model training process, we fine-tuned the number of parameters trained on each pre-trained model to produce the best performing model. Based on eight experimental scenarios, the VGG19 can produce the best performance with a recognition accuracy of 87.50%.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129592802","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":"Meta-Analytical Considerations for Gamification in Higher Education: Existing Approaches and Future Research Agenda","authors":"Flora Poecze, A. Tjoa","doi":"10.1109/ICICoS51170.2020.9299055","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299055","url":null,"abstract":"Gamification is one of the most trending scientific topics in recent times, with higher education receiving the most concentrated focus of interest. Due to the relative infancy of this domain, three meta-analytical approaches were published in this domain, despite the accelerated speed of new manuscripts seeing light, indicating the need for more contributions in this regard. The present article explores this research area, concentrating on the relevance of publication bias tests in meta-analytical approaches, discussing the results of existing meta-analytical contributions in this sector. The heterogeneity of interpretation, analyzed constructs and relationships makes it, however, rather challenging to conduct a successful meta-analysis. The paper therefore deals with the comparison of existing methods for correction of publication bias, leading to an enhanced focus on evaluated constructs, and the effective exclusion of threats, such as population effect overestimation, selective reporting, and p-hacking.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115829170","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":"The Performance of Face Recognition Using the Combination of Viola-Jones, Local Binary Pattern Histogram and Euclidean Distance","authors":"Suherwin, Z. Zainuddin, A. A. Ilham","doi":"10.1109/ICICoS51170.2020.9299073","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299073","url":null,"abstract":"Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582747","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}
A. Darmawan, D. Siahaan, T. D. Susanto, Hoiriyah Hoiriyah, B. Umam, A. Hidayanto, A'ang. Subiyakto, Miftahul Walid, I. Santosa
{"title":"Hien’s Framework for Examining Information System Quality of Mobile-based Smart Regency Service in Madura Island Districts","authors":"A. Darmawan, D. Siahaan, T. D. Susanto, Hoiriyah Hoiriyah, B. Umam, A. Hidayanto, A'ang. Subiyakto, Miftahul Walid, I. Santosa","doi":"10.1109/ICICoS51170.2020.9299015","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299015","url":null,"abstract":"Today, almost every nation strives to incorporate city governance with the idea of smart cities. Several previous studies have developed various models to assess the efficiency of ISS. During the past, numerous studies evaluated the quality of service and experience of smart city applications. But very few research also explores the Smart District model’s performance of services, which differs substantially from the general concept of a smart city. This study aims to examine the quality of service through the usage of the region or district with mobile devices. The model and approach used is the Hien’s Framework, a development framework of the Technology Acceptance Model and the Delone McLean IS Success Model to measure the quality of information system services. The online survey form, data processing using AMOS 24.0 software, was used to collect data from two hundred and seventeen interviewees. The research findings show that the variables introduced by the Hien Framework have a significant and positive impact on the quality assessment of smart district services. This work helps to explain the performance model of a smart district information system based on mobile devices. This research recommends that local governments and politicians take better concern for critical problems affecting the quality of mobile services","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115916920","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 Comparison of YOLO and Mask R-CNN for Segmenting Head and Tail of Fish","authors":"Eko Prasetyo, N. Suciati, C. Fatichah","doi":"10.1109/ICICoS51170.2020.9299024","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299024","url":null,"abstract":"The visual appearance of the fish’s head and tail can be used to identify its freshness. A segmentation method that can well isolate those certain parts from a fish body is required for further analysis in a system for detecting fish freshness automatically. In this research, we investigated the performance of two CNN-based segmentation methods, namely YOLO and Mask R-CNN, for separating the head and tail of fish. We retrained the YOLO and Mask R-CNN pre-trained models on the Fish-gres dataset consisting of images with high variability in the background, illumination, and overlapping objects. The experiment on 200 images containing 724 heads and 585 tails annotated manually indicated that both models work optimally. YOLO’s performance was slightly better than Mask R-CNN, shown by 98.96% and 96.73% precision, and 80.93% and 75.43% recall, respectively. The experimental result also revealed that YOLO outperforms Mask R-CNN with mAP of 80.12% and 73.39%, respectively.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129217502","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":"Designing Diabetes Mellitus Detection System Based on Iridology with Convolutional Neural Network Modeling","authors":"Dyla Velia, A. H. Saputro","doi":"10.1109/ICICoS51170.2020.9299081","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299081","url":null,"abstract":"Diabetes mellitus is one of the uncontagious diseases with the highest mortality rate in the world. It happens because of the increased risk of complications caused by the disease. One of the preventative ways is to do early detection, one of which is by using the iridology method. The method detects damage to the body’s organs through the signs that appear on the iris. The paper has introduced a Diabetes Mellitus Detection System to classify diabetes using a Convolutional Neural Network (CNN). The proposed method removed the pupil segmentation step that is important in the traditional machine learning classification system. The squared pupil image size 720×360 pixel was trained using Adam’s algorithm with a learning rate of 0.001 to develop the CNN model. The pupil image was collected using Iriscope Iris Analyzer Iridology 9822U camera. The dataset consists of 35 healthy and 14 diabetes subjects that repeat three times of each person. The proposed approach has an accuracy of 96.43% that better performance compared to traditional machine learning.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127312218","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}
R. Saputra, Nauli Isnaini, S. Adhy, N. Bahtiar, Z. Abidin, E. Suharto
{"title":"Factors Influencing Student’s Adoption of ELearning in Indonesian Secondary Schools","authors":"R. Saputra, Nauli Isnaini, S. Adhy, N. Bahtiar, Z. Abidin, E. Suharto","doi":"10.1109/ICICoS51170.2020.9299109","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299109","url":null,"abstract":"E-learning is one type of educational service created to facilitate the learning process for all groups. They can do teaching and learning activities only through the smartphone or laptop/computer they have. The acceptance of this technology is analyzed through suitability between e-learning technology and current technology, and satisfaction of users’ e-learning. To examine correlation between latent variables (LV) or variable indicator, this study uses Partial Least Square (PLS) as a conceptual test equipment. This study uses the integration model of the unified of acceptance and usage of technology 2 (UTAUT 2) and expectation confirmation model (ECM). The results showed that students were less interested in digital learning. The results showed that electronic devices such as computers, laptops, yet stole great interest from Indonesian students to support their learning process. A high e-learning cost is also a factor that reduces students’ interested in using e-learning continuously. The use of appropriate media and equitable distribution of internet use have an important role in the adopting the e-learning systems among students. The factors that exist in the two models used in this study are considered important in explaining the adoption of e-learning technology, but as far as the author’s knowledge there has been no research that integrates the two models to explain technological adaptation to e-learning. Therefore, this study will contribute to the literature on technology adoption for e-learning by integrating the factors of both models and testing models in developing country contexts exemplified in this study by Indonesia.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130264138","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":"Development of The Smart Chicken Eggs Incubator Based on Internet of Things Using The Object Oriented Analysis and Design Method","authors":"S. Santoso, S. Adhy, N. Bahtiar, I. Waspada","doi":"10.1109/ICICoS51170.2020.9299000","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299000","url":null,"abstract":"Smart Chicken Eggs Incubator is one of the applications of the Internet of Things in the field of animal husbandry specifically on the hatching of chicken eggs. Breeders carry out the process of hatching chicken eggs using artificial incubators and in the process of hatching chicken eggs, these problems arise in the process of monitoring the environment inside the incubator such as temperature, humidity, and egg changes which are still done manually. This research explains the development of a prototype Smart Chicken Eggs Incubator System by implementing the Internet of Things which has three subsystems namely embedded systems, web-based applications, and Telegram bot. Web application software on the Smart Chicken Eggs Incubator System was developed using the Object-Oriented Analysis and Design (OOAD) method. The web apps built can be used to monitor the conditions of the incubator based on sensor data that has been sent during the egg hatching process. While in the Telegram bot, real-time conditions of temperature, humidity, egg transfer, and photos of the situation inside the incubator can be monitored, as well as message notifications if there are conditions on the incubator that change beyond the specified limits. Prototype testing was carried out for 21 days, with the amount of data entered as many as 3,402 records with time intervals every 5 minutes and get the optimal time for each entry is 9 minutes to 10 minutes.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508887","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":"An Improved Method of Graph Edit Distance for Business Process Model Similarity Measurement","authors":"I. Waspada, R. Sarno","doi":"10.1109/ICICoS51170.2020.9299037","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299037","url":null,"abstract":"Graph Edit Distance (GED) is widely used in measuring the similarity of the business process model. The GED method is based on a Process Graph abstraction that represents a business process model in graph notation. There are difficulties in representing the gateway semantics in the process graph. One approach is to ignore the gateway, but this method causes the measurement of similarity between process models to be less accurate. The second approach takes into account the gateways as nodes. This approach causes the required edit distance to be too high and can also have an impact on the incorrect similarity measurement. The third approach represents the gateway as an attribute for the edges. It has the advantages of the first and second approaches while avoiding their weakness. However, no method has been proposed that is accurate in adopting the latter approach to GED. This paper proposes three contributions to answer the problem. The first is a modification of the graphical representation of the business process graph by adding together the relation types (sequence, XOR_split, XOR_join, AND_split, AND_join, OR_split, OR_join) and weight (based on execution probability) as edge attributes. The second contribution proposes a novel formula to calculate the weight. The third contribution is to propose GED modification, namely the Enhanced Graph Edit Distance (EGED). Comparisons were made to the state of the art methods, namely GED_Dijkman and GED_Montani, resulting in GED_Dijkman unable to detect gateways and GED_Montani was able to detect them but could not distinguish between XOR, AND, and OR, while our proposed method, EGED, succeeds in recognizing and differentiate XOR, AND, and OR gateways based on their execution probability semantics.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462928","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}