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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 57 Documents
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Search results for , issue "Vol 9, No 6: December 2020" : 57 Documents clear
Performance evaluation of decision tree classification algorithms using fraud datasets Eddie Bouy B. Palad; Mary Jane F. Burden; Christian Ray Dela Torre; Rachelle Bea C. Uy
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2630

Abstract

Text mining is one way of extracting knowledge and finding out hidden relationships among data using artificial intelligence methods. Surely, taking advantage of different techniques has been highlighted in previous researches however, the lack of literature focusing on cybercrimes implies the lack of utilization of data mining in facilitating cybercrime investigations in the Philippines. This study therefore classifies computer fraud or online scam data coming from Police incident reports as well as narratives of scam victims as a continuation of a prior study. The dataset consists mainly of unstructured data of 49,822 mainly Filipino words. Further, 5 decision tree algorithms namely, J48, Hoeffding Tree, Decision Stump, REPTree, and Random Forest were employed and compared in terms of their performance and prediction accuracy. The results show that J48 achieves the highest accuracy and the lowest error rate among other classifiers. Results were validated by Police investigators where J48 was likewise preferred as a potential tool to apply in cybercrime investigations. This indicates the importance of text mining in the field of cybercrime investigation domains in the country. Further work can be carried out in the future using different and more inclusive cybercrime datasets and other classification techniques in Weka or any other data mining tool.
Automatic whole-body bone scan image segmentation based on constrained local model Ema Rachmawati; Jondri Jondri; Kurniawan Nur Ramadhani; Achmad Hussein Sundawa Kartamihardja; Arifudin Achmad; Rini Shintawati
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2631

Abstract

In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Optimized multimodal biometric system based fusion technique for human identification Muthana H. Hamd; Rabab A. Rasool
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2632

Abstract

This paper presents three novelty aspects in developing biometric system-based face recognition software for human identification applications. First, the computations cost is greatly reduced by eliminating the feature extraction phase and considering only the detected face features from the phase congruency. Secondly, a motivation towards applying a new technique, named mean-based training (MBT) is applied urgently to overcome the matching delay caused by the long feature vector. The last novelty aspect is utilizing the one-to-one mapping relationship for fusing the edge-to-angle unimodal classification results into a multimodal system using the logical-OR rule. Despite some dataset difficulties like unconstrained facial images (UFI) which includes varying illuminations, expressions, occlusions, and poses, the multimodal system has highly improved the accuracy rate and achieved a promising recognition result, where the decision fusion is classified correctly (84, 92, and 72%) with only one training vector per MBT in contrast to (80, 62, and 68%) with five training vectors for normal matching. These results are measured by Eucledian, Manhattan, and Cosine distance measure respectively.
Autonomous microgrid based parallel inverters using droop controller for improved power sharing Siddaraj Siddaraj; Udaykumar R. Yaragatti; Nagendrappa H.; Vikash Kumar Jhunjhunwala
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2663

Abstract

The existing microgrid has become a challenge to the sustainable energy source to provide a better quality of power to the consumer. To build a reliable and efficient microgrid, designing a droop controller for the microgrid is of utmost importance. In this paper, multiple voltage source inverters connected in parallel using an active power-frequency/reactive power-voltage droop scheme. The proposed method connected to two distributed generators local controllers, where each unit consists of a droop controller with an inner voltage-current controller and a virtual droop controller. By adding this controller to the microgrid reliability and load adaptability of an islanded system can be improved. This concept applied without any real-time communication to the microgrid. Thus, simulated using MATLAB/Simulink, the obtained results prove the effectiveness of the autonomous operation's microgrid model.
K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification Mohd Hatta Jopri; Mohd Ruddin Ab Ghani; Abdul Rahim Abdullah; Mustafa Manap; Tole Sutikno; Jingwei Too
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2685

Abstract

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.
Internet of Things-based telemonitoring rehabilitation system for knee injuries Muheeb Musaed M. Al-Omri; Nayef Abdulwahab Mohammed Alduais; Mohamad Nazib Adon; Abdul-Malik H. Y. Saad; Antar Shaddad H. Abdul-Qawy; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2428

Abstract

Rehabilitation engineering, as one of the active research areas in biomedical science, needs further investigations and improvements. The process of rehabilitation, whether after a stroke, ligament, or accident-related injuries, is commonly based on clinical assessment tools, which can be executed, either by self-reported (home-based) treatment or through observer-rated therapy. However, people with reduced mobility (e.g., stroke, surgical, and ligament patients) can benefit from rehabilitation programs only if effective and appropriate assistive tools are provided. In this paper, a new Internet of Things (IoT)-based telemonitoring system is introduced for knee injuries’ rehabilitation (Knee-Rehab). The proposed system is a real-time rehabilitation and monitoring framework designed to be a portable, home-based, and online-based instrument comprised of bio-mechanical, bio-instrumentation and IoT-based elements. The system helps patients to rest at home after surgeries or physical treatment, do their rehab-exercises, and receive suggestions form their advisors, which gain the ability to monitor the situation over the exercising time and propose necessary medication/activities to be followed by the patients accordingly, based on their current status. The experimental measurements show the high accuracy achieved by the developed system in terms of monitored knee joint angle, where the maximum error is 3.5° compared to manual goniometer measurements.
QoS controlled capacity offload optimization in heterogeneous networks Siva Priya Thiagarajah; Mohamad Yusoff Alias; Wooi-Nee Tan
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2706

Abstract

An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap.  User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.

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