Bulletin of Electrical Engineering and Informatics

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ISSN / EISSN : 2089-3191 / 2302-9285
Total articles ≅ 1,254
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Pulung Nurtantio Andono, Eko Hari Rachmawanto, Nanna Suryana Herman, Kunio Kondo
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2530-2538; https://doi.org/10.11591/eei.v10i5.3118

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.
Dalia Mohammad Toufiq, ,
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2588-2597; https://doi.org/10.11591/eei.v10i5.3013

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.
Farouk Boumehrez, A. Hakim Sahour, Noureddine Doghmane
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2652-2660; https://doi.org/10.11591/eei.v10i5.2968

Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where internet of things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.
Lanto Ningrayati Amali, Muhammad Rifai Katili, Sitti Suhada, Tri Alfandra Labuga
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2884-2891; https://doi.org/10.11591/eei.v10i5.3147

Information technology (IT) is essential in supporting an organization's business sustainability and growth, making it critically dependent on IT. Therefore, a focus on IT governance, consisting of leadership, organizational structure, and process ensuring that IT organization supports and expands the organizational strategies and goals is required. When the business supports the strategic significance of IT investment, the implementation of an IT strategy will lead to the adoption of an IT governance model. It will support and help the description of the benefit roles and responsibilities from IT systems and infrastructure. This paper aims to develop a business process monitoring system to support IT governance in improving user service and measuring organizational performance. The research method was the system development method with the Waterfall model. To measure the performance of the business process, the self-assessment method with performance matrix tools was applied. The study resulted in a business process monitoring system that can enhance the organization’s primary business process in services, supporting the said organization’s performance.
Pui Mun Lo, Azniza Abd Aziz
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2477-2487; https://doi.org/10.11591/eei.v10i5.3152

Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.
Aarti Bakshi, Sunil Kumar Kopparapu
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2578-2587; https://doi.org/10.11591/eei.v10i5.3173

In spoken language identification (SLID) systems, the test data may be of a sufficiently shorter duration than training data, known as duration mismatch condition. Duration normalized features are used to identify a spoken language for nine Indian languages in duration mismatch conditions. Random forest-based importance vectors of 1582 OpenSMILE features are calculated for each utterance in different duration datasets. The feature importance vectors are normalized across each dataset and later across different duration datasets. The optimal number of duration normalized features is selected to maximize SLID system accuracy. Three classifiers, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), and their fusion, weights optimized using logistic regression, are used. The speech material comprised utterances, each of 30 sec, extracted from the All India Radio dataset with nine Indian languages. Seven new datasets of smaller utterance durations were generated by carefully splitting each utterance. Experimental results showed that 150 most important duration normalized features were optimal with a relative increase in 18-80% accuracy for mismatch conditions. The accuracy decreased with increased duration mismatch.
Moanda Diana Pholo, Yskandar Hamam, Abdel Baset Khalaf, Chunling Du
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2857-2865; https://doi.org/10.11591/eei.v10i5.3132

Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.
Frederick F. Patacsil, Michael Acosta
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2771-2779; https://doi.org/10.11591/eei.v10i5.2590

Online job vacancy sites have become an important source of information about the characteristics of labor market demand. It has become an avenue for job matching by both employers and employees and to study and analyze the labor market. This study proposed a methodology for identifying and analyzing skill-job relationships using frequency word occurrences of skills as a requirement of the job. It employed association rule mining which aims to discover frequent patterns, relationships among a set of items in the database. It collected published job vacancy data to IT job and skills requirements from various job portal websites. The proposed job skill requirements for specific I.T. jobs published online analyzing using the FP-growth algorithm of association rule provide a new dimension in labor market research. The study revealed that skill words are highly related to a certain job requirement. The results of the study could provide insights on the gap between the school acquired skills and actual IT industry skill needs and as the basis for curriculum enhancement and policy-making interventions by the Philippine government in its educational system.
Vorapoj Patanavijit, Kornkamol Thakulsukanant
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2520-2529; https://doi.org/10.11591/eei.v10i5.3105

Because of the enormous necessity of contemporary noise suppressing algorithms, this article proposes the novel noise classification technique found on QTSD filter improved from the TTSD filter. The four thresholds for each auxiliary situations are incorporated into the proposed QTSD framework for dealing with the limitation of the earlier noise classification technique. The mathematical pattern is modeled by each photograph elements and is investigated in contradiction to the 1st threshold for analyzing whether it is non-noise or noise photograph elements. Subsequently, the calculated photograph element is analyzed with the contradiction between the 2nd threshold, which is modeled by using the normal distribution (mean and variance), and is analyzed with the contradiction between the 3rd threshold, which is modeled by using the quartile distribution (median). Finally, the calculated photograph element is investigated in contradiction to the 4th threshold, which is modeled from maximum or minimum value for analyzing whether it is non-noise or noise photograph elements FIIN. For performance evaluation, extensive noisy photographs are made up of nine photographs under FIIN environment distribution, which are synthesized for investigating the proposed noise classification techniques found on QTSD filter in the objective indicators (noise classification, non-noise classification and overall classification correctness). From these results, the proposed noise classification technique can outstandingly produce the higher correctness than the earlier noise classification techniques.
R. Roslina, Afritha Amelia, Heru Pranoto, Bakti Viyata Sundawa
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 2454-2465; https://doi.org/10.11591/eei.v10i5.2826

Public campus has a mandate to saving of electrical energy. Electrical energy consumption is often wasteful in building. There is tendency wasteful by user. Electronic equipment is often still turn on at idle time. Only a few students want to turn off the equipment and shut down the computer. Saving of electrical energy is not only at idle time but it can be improved into operational hour. It is not depending on idle time or operational hours, but depends on human presence. Implementation of electrical energy saving has to be supported by frugal behavior and equipment technology. In this study, we name system of smart detection and control to electrical energy (Sisdece). This system is consist of hardware and software. Hardware applies passive infrared sensor (PIR) sensor, wireless sensor network (WSN), microcontroller ESP32, access point, relay. Software use C++, hypertext preprocessor (PHP), hypertext markup language (HTML) and android studio. Result of measurement has been done in a month during November 2020. Average of energy saved is 12.51 kWh and total of electrical energy is 105.86 kWh. Comparison of energy saved to electrical energy is 11.81%. This is a significant reduction to electrical bill. The result is expected as benchmark of electrical energy management in Politeknik Negeri Medan (POLMED).
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