JOIV : International Journal on Informatics Visualization
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The IT Services Management Architecture Design for Large and Medium-sized Companies based on ITIL 4 and TOGAF Framework
Get PDFThe development of information technology occurs rapidly in almost all areas of life. All companies must immediately carry out a business transformation following the development of information technology to survive amid increasingly fierce competition. One of the keys to this business transformation's success is an enterprise architecture that is used as a reference in planning, developing, operating, and monitoring company information technology. Implementation of service management practices in state-owned enterprises needs to be translated into IT Services Management Architecture Design, that match the IT Governance Principles as mandated in PER-03/MBU/02/2018. This research focuses on preparing an enterprise architecture design in IT service management by referring to ITIL 4 best practices. The resulting solution is a target architecture design in the business domain, data, and applications arranged according to the TOGAF framework. It was carried out in four stages: scope identification, which defines practices; preliminary phase, which resulted in 11 architecture principles; architecture vision that produces a value chain for IT service provider organizations; a business architecture which resulted in a business service/function catalog consisting of 13 business functions and 43 business services; and an information system architecture that produces a conceptual data model on the three main priority processes of IT service management and an application use-case diagram that describes the relationship between the four actors (users, service managers, service desks, and support groups) with their roles in applications. The enterprise architecture has been designed following the scope of IT service management practices commonly used as a reference for all large and medium-sized companies
Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System
Get PDFAround 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting
Mining Opinions on a Prominent Health Insurance Provider from Social Media Microblog: Affective Model and Contextual Analysis Approach
Get PDFSocial media plays a significant role in enhancing communication among organizations, communities, and individuals. Besides being a mode of communication, the data generated from these interactions can also be leveraged to assess the performance of an institution or organization. People may evaluate public companies based on the opinions of their users. However, user-supplied information is brief and written in natural language. In addition to being brief, the process of sending messages or engaging in other social media interactions contains a great deal of context information. This multiplicity of context can be utilized to conduct a more in-depth analysis of user opinion. This study presents a new approach to opinion mining for social media microblogging data by applying an affective model and contextual analyses. The affective model is applied for sentiment analysis to measure the degree of each adjective from user opinion by evaluating adjectives according to their varying levels of pleasure and arousal. The contextual analysis in this paper is modeled based on topic, user, adjective, and personal characteristics. The contextual analysis has four main features: (1) Temporal keyword sentiment context, (2) Temporal user sentiment context, (3) User impression context, and (4) Temporal user character context. Our affective model outperformed 75.6% the accuracy and 74.98% of F1-score, rather than SVM. In the experiment, the contextual analysis performed graph visualization of output results for each query feature for future development. Feature one to four successfully processes the query to produce a visualization graph
Predictive AC Control Using Deep Learning: Improving Comfort and Energy Saving
Get PDFThe growing global population and the availability of energy-hungry smart devices are critical factors in today's alarmingly high electricity usage. The majority of energy used in urban areas is consumed by buildings, with heating, ventilation, and air conditioning (AC) systems accounting for a significant amount of energy use. This project proposes an AC controlling algorithm that uses the Internet of Things sensors and a deep learning framework in temperature prediction to control a single AC unit. The algorithm consists of a Long Short-Term Memory (LSTM) model to predict the indoor temperature for the next J minutes. The highlight of this model is its capacity to predict the future temperature based on the predetermined AC status, whether it is switched on or off. The AC unit will be turned off if the J-minute predicted temperature is within the desired thermal comfort range, and it will be turned back on if the sensor readings exceed the upper pre-set threshold. The experiment is performed on the dataset collected by Chulalongkorn University Building Energy Management System (CU-BEMS). The LSTM prediction model developed using CU-BEMS data yields an average Root Mean Squared Error and Mean Absolute Error of 0.08 and 0.03, respectively. A half-day simulation is also performed in controlling the AC unit from 7:39 a.m. to 11:35 a.m. The proposed algorithm shows that 49.00% of the time, the AC unit can be turned off while the thermal range is maintained between 27ºC to 27.9ºC, providing a strategy for managing the AC unit and achieving energy savings
Internet of Things (IoT) Innovation and Application to Intelligent Governance Systems: A Case Study on DISHUB for Transport Vehicles
Get PDFVehicles can be used to facilitate humans in carrying out their daily activities. Motorized vehicles are divided into 3: land, sea, and air. Apart from the many benefits that can be obtained, motorized vehicles have dangers related to high-risk accidents. The highest cause of motorized vehicle accidents on land is road damage due to overload vehicles. Brake failure due to overload vehicles also contributes to vehicle accidents. This research aims to create an Internet of Things (IoT) based application to detect motorized vehicle load conditions. It was combined with several other technologies to produce a tool for detecting motor vehicle load conditions. The Extreme Programming Method is being used in this research. The Extreme Programming method is considered more suitable for completing this research because the communication with stakeholders is quite different. The Extreme Programming Method enables it to go back to the next step if discrepancies are encountered in making the system. The result of this research is an IoT-based tool called e-overload. It can detect vehicle loads, provide information for the drivers, and inform the results to related officers at the same time. E-overload tool will enable the drivers to get real-time information on the load on their vehicles. Officers will get additional evidence and the latest position of the vehicle to carry out actions against motorists who operate their vehicles with excessive loads
Machine Learning Algorithms Based on Sampling Techniques for Raisin Grains Classification
Get PDFRaisin grains are among the agricultural commodities that can benefit health. The production of raisin grains needs to be classified to achieve optimal results. In this case, the classification is carried out on two types of grains, namely Kecimen and Besni. However, inaccurate sample data can affect the performance of the model. In this study, two sampling techniques are proposed: stratified and shuffled. The proposed classification model is RF, GBT, NB, LR, and NN. This study aims to identify the performance of classification models based on sampling techniques. Classification models are applied to the seven-features dataset, and modeling is done by cross-validation. The results of the models were tested with a different amount of test data. The performance of the models was evaluated related to accuracy and AUC. The best outcomes of all models based on stratified sampling were founded on tested data of 40 percent with a mean accuracy of 85.50% and an AUC of 0.921. In comparison, models based on shuffled sampling were founded on test data of 20 percent with a mean accuracy of 88.11% and an AUC of 0.935. On the other hand, classification models based on a stratified sampling of all data splits do not all models generate an excellent category. Whereas, based on shuffled sampling, all models resulted in the excellent category. Therefore, models based on shuffled sampling are superior to stratified sampling. The result of the significant test, RF, significantly differs based on sampling techniques
Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology
Get PDFThis study describes the optimization of hand gesture recognition on Raspberry Pi 4 technology has advanced over the past years, some computers are now able to compute much more complex problems like real-time object detection. But for small devices, optimization is required to run in real-time with acceptable performance in terms of latency and low-cost effect on accuracy. Low latency is a requirement for most technology, especially when integrating real-time object detection as input into Self-Service Technology on Raspberry Pi for the store. This research was conducted on 288 pictures with six types of chosen hand gestures for command inputs that have been configured in the Self-Service Technology as a training dataset. In the experiment carried out with 5 CNN object detection models were used, namely YOLOv3-Tiny-PRN, YOLOv4-Tiny, MobileNetV2-Yolov3-NANO, YOLO-Fastest-1.1, and YOLO-Fastest-1.1-XL. Based on the experiment after optimization, the FPS and inference time metrics have improved performance. The performance improves due to a gained average value of FPS by 3 FPS and a reduced average value of inference time by 119,260 ms. But such an improvement also comes with a reduction in overall accuracy. The rest of the parameters have a reduced score on Precision, Recall, F1-Score, and some for IoU. Only YOLO-Fastest-1.1-XL have an improved value of IoU by about 0.58%. Some improvements in the CNN and dataset might improve the performance even more without sacrificing too much on the accuracy, but it's most likely suitable for another research as a continuation of this topic
The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices
Get PDFAn otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early
Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR
Get PDFPrevention of the effects caused by natural disasters such as earthquakes and landslides requires analysis of vibration propagation. In outdoor applications, internet sources such as WIFI are not always available, so it requires alternative data communications such as nRF24l01. The system also requires a portable power source such as solar power. This research aims to develop a vibration propagation analysis system based on the nRF24l01 wireless sensor network and solar power by implementing the fuzzy rule-based regression (FRBR) algorithm. The system consists of two piezoelectric and nrf24l01 vibration sensors. The system also uses a third node equipped with temperature and soil moisture sensors, air temperature and humidity, and light intensity as environmental variables. The evaluation results show the Quality of Services (QoS) results with a throughput of 99.564%, PDR 99.675%, and a delay of 0.0073s. The Fuzzy Association Rule (FAR) extraction results yield nine rules with average support of 0.319 and confidence of 1 for vibration propagation. The availability of solar power was evaluated with an average current value of 0.250A and a voltage of 3.266V. The results of FRBR are based on the propagation of the vibration that propagated and produced a mean square error (MSE) of 0.141 and a mean absolute error (MAE) of 0.165. The correlation matrix and FAR results show that only soil moisture has a major effect on the magnitude and duration of propagation. However, other variables can regress soil moisture with MSE 0.232 and MAE 0.287
Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data
Get PDFThe availability of hydrological data is one of the challenges associated with developing water infrastructure in different areas. This led to the TRMM (Tropical Precipitation Measurement Mission) design by NASA, which involves using satellite weather monitoring technology to monitor and analyze tropical precipitation in different parts of the world. Therefore, this validation study was conducted to compare TRMM precipitation data with observed precipitation to determine its application as an alternate source of hydrological data. The Kuranji watershed was selected as the study site due to the availability of suitable data. Moreover, the validation analyses applied include the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Coefficient Correlation (R), and Relative Error (RE). These used two calculation forms: one for the uncorrected data and another for the corrected data. The results showed that the best-adjusted data validation from the Gunung Nago station in 2016 was recorded to be RMSE = 62,298, NSE = 0.044, R = 0.902, and RE = 11,328. The closeness of the R-value to one implies that the corrected TRMM data outperforms the uncorrected ones. Therefore, it was generally concluded that the TRMM data matches the observed precipitation data and can be used for hydrological study in the Kuranji watershe