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Feature selection for DDoS detection using classification machine learning techniques

10.11591/ijai.v9.i1.pp137-145
Andi Maslan , Kamaruddin Malik Bin Mohamad , Feresa Binti Mohd Foozy
Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.
Volume: 9
Issue: 1
Page: 137-145
Publish at: 2020-03-01

Design and development of Bi directional power meter using microcontroller

10.11591/ijeecs.v17.i3.pp1594-1600
Tasnim Ikra Rahman , Anika Fariha Rashid , Md. Habibur Rahman
The significance of power or energy measurement has been very acute in recent times with the increase of Grid-Tied Solar PV system that needs Net-Metering. Power can be flown in both directions, from grid to load or from load (PV- Panel) to grid line. If power flow from both side is not calculated and surplus power which is not being used at the time instant is not stored, accurate billing is not possible. That is why a bidirectional power meter is required which can measure both the positive and negative power at any instant. The aim of this paper is to develop a bidirectional power meter using microcontroller. The work of this paper constitutes a microcontroller, a current sensor, bulbs as load and a display. The rms values of voltage and current is determined and showed in the display. Then the calibration is done inside the developed program. After that the power is calculated and showed in the display. Then the direction of the power is determined depending on the direction (whether positive or negative) of the power which is also dependent on the phase difference between the voltage and current. If there is no phase difference between the voltage and current, then the power is positive and leaving from the grid. If there is 180 degree phase difference between the voltage and current, then the power is negative and entering to the grid. The direction of the power flow is very important for efficiently of the proposed meter. For some defined load the accuracy of the developed meter is calculated as 94%.
Volume: 17
Issue: 3
Page: 1594-1600
Publish at: 2020-03-01

Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving

10.11591/ijai.v9.i1.pp25-32
Wan Nazirah Wan Md Adnan , Nofri Yenita Dahlan , Ismail Musirin
This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving. 
Volume: 9
Issue: 1
Page: 25-32
Publish at: 2020-03-01

IR4.0: unmanned store apps

10.11591/ijeecs.v17.i3.pp1540-1547
Saidatul Rahah Hamidi , Muhammad Afiq Muhamad Yusof , Shuhaida Mohamed Shuhidan , Shamsiah Abd Kadir
The Internet of Things (IoT) is predicted to be one of the major megatrends in technology that applies worldwide including in Malaysia. Current advantages and expectations of using a connected device has become a reason to use applications in many areas, including the field of retail industry. The evolvement of technology has changed the retail landscape that replaces the manpower in their operations to improve the efficiency, quality and work ethics. Hence, this paper discussed comparative study of technology innovation applied and process flow in unmanned store from different country. Next, prototype of unmanned store apps was designed according to the most preferred technology based on the comparative study. IoT retailer model can be used to improve the retail experience for the customers. Thus, it may perform strategic marketing and gain retail intelligence. Besides, the revolution of using technology in this retail industry is contributing towards Industry 4.0 through digital transformation.
Volume: 17
Issue: 3
Page: 1540-1547
Publish at: 2020-03-01

Closed-loop ring resonator topology for bandpass filter applications

10.11591/ijeecs.v17.i3.pp1422-1426
Norfishah Ab Wahab , M. N. Md Tan , M. N. Hushim
This paper presents a single mode pseudo-elliptic bandpass resonator based on closed-loop ring topology. The resonator is built from six quarter wavelength transmission lines to form a square closed-loop ring structure. This structure creates transmission zeros at the lower and upper sidebands so that high selectivity bandpass filter response is achieved. The advantage of this topology is that the design is less complex since no perturbation is needed on the ring lines for creation of transmission zeros. Higher-order filters can be constructed by introducing quarter-wavelength coupled-lines, coupled at both input and output of the closed-loop ring resonator. For proof of concept, the filters are designed at 10 GHz up to 3rd order, simulated using full-wave electromagnetic simulator on microstrip substrate, FR-4 with characteristics given as Ԑr = 4.70, h = 1.499 mm and tan δ = 0.012.  The filters are simulated and responses are found to be agreeable with the proposed idea.
Volume: 17
Issue: 3
Page: 1422-1426
Publish at: 2020-03-01

Inductive instructional approach, career aspiration and noble values in history

10.11591/ijere.v9i1.20417
Mohd Razimi Husin , Hishamuddin Ahmad , Ismail Yusuf Panessai , Norliza Abdul Majid , Agus Lokman Sulam
This paper presents inductive instructional approach, career aspirations and noble values in History. In the 21st century there is still History teachers emphasize the only cognitive aspects of student learning, but still have little to do with everyday life, career aspirations and noble values. This study has developed a review list of learning experiences on specific learning content that takes into account inductive approach, career aspirations and noble values of History subject. Finally, this study examines the effectiveness of the learning that emphasize on the inductive approach, career aspirations and noble values. This study uses a qualitative approach supported by quantitative data involving four stages namely library studies, observations, expert suggestions and quasi-experiments. 30 teachers and 60 students were selected through purposeful sampling. The results show that mean inductive learning experiences, career aspirations and noble values in the subject is higher than the learning with other method. The Inductive-directed delivery approach showed a mean increase of 1.15 in students associate with classroom activities. The results also show that an increase of mean 1.5 and 0.35 in career aspirations and noble values. This study has revealed a checklist of learning experiences, career aspirations and noble values that beneficial to all educators.
Volume: 9
Issue: 1
Page: 162-167
Publish at: 2020-03-01

A conceptual integrated health information systems framework in postnatal care for modern and traditional malay medicine

10.11591/ijeecs.v17.i3.pp1531-1539
Raja Rina Raja Ikram , Lizawati Salahuddin , Mohd Hariz Mohd Naim , Ariff Idris , Nor Afirdaus Zainal Abidin , Nadiah Ishak , Noor Raihan Ab Hamid
This paper proposes an integrated health information systems framework for Traditional Malay Medicine (TMM) and modern medicine in the field of postnatal care. A qualitative study was conducted via healthcare experts in the field of modern medicine and Traditional Malay Medicine to assess the current situation and identify the research gap and point of isolation between both traditional and modern medicine field. A total of 26 healthcare practitioners whom represented five different set of healthcare organisations participated in this study. The healthcare practitioners consist of modern and traditional Malay medicine background with and without proper training. Results show that there is a gap in the current people, process and technology areas of the current framework. A novel conceptual framework, MyPostnatal, proposes the existence of a sufficiently generic, extensible in-formation model where new data sources can be integrated without major changes to the data scheme. Human and organization factors are also highlighted to stimulate the adoption towards electronic health records.
Volume: 17
Issue: 3
Page: 1531-1539
Publish at: 2020-03-01

Identification of mobile vehicle through multilayer intercommunication

10.11591/ijeecs.v17.i3.pp1390-1398
Ranjan Goyal , Anuradha Gnanaprakasam , Paritosh Pal Singh
In the recent times of data transmission, applying the Internet of Things (IoT) device, networking to the various IoT modules is still a great challenge with the deployment stage. Very few localities in the world have the deployed IoT module integrated in its communication, but still maximum of the location are in testbed. As a practical application, the IoT modules can be used to collect the information about either the drunken person who is driving the vehicle or the heavily loaded vehicles. The vehicle that we are about to search should be integrated with the IoT module. In this paper, a novel approach for object tracking specifically the drunken driving person and the heavily loaded vehicle is proposed to ensure the follow of rules and prevent any mis happening. The task is to identify the exact vehicle which does not follow the rules which can be tracked and eliminated. For that we are proposing a simple methodology on identifying the vehicle by the tracking the locality of the vehicle as a cluster of nodes where the cluster provides the key inference on how the vehicles are interrelated in the learning environment.
Volume: 17
Issue: 3
Page: 1390-1398
Publish at: 2020-03-01

Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach

10.11591/ijeecs.v17.i3.pp1467-1473
Sinan Adnan Diwan
Real estate forecasting has become an integral part of the larger process of business planning and strategic management in real estate sector. This study covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature which need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. This aim of the paper is to propose and develop a computer assisted real estate property price forecasting model. This proposed framework will examine the current uses of artificial intelligence, particularly combining case base reasoning and artificial neural network, in the business-forecasting field and considers suitable applications in real estate. The methodology consists of five phases: 1) Data gathering b) Data cleaning c) ANN Training d) CBR (Case Base Reasoning) similarity retrieval e) Result retrieval. This research will investigate the influence of residential real estate property characteristics on property values (prices) in global context, it revealed a high positive linear correlation between property characteristics and the property market values; an indication that these characteristics reasonably predict property market values. The results of the study will enable Real Estate Professionals to make fair estimates of the market values of residential real estate properties given the features/characteristics of such housing units.
Volume: 17
Issue: 3
Page: 1467-1473
Publish at: 2020-03-01

Students’ learning style and its effect on blended learning, does it matter?

10.11591/ijere.v9i1.20422
Nurasma’ Shamsuddin , Jasber Kaur
Blended learning is an approach in education system that provides multi delivery mode to optimize learning outcome and cost of program delivery in institutions. Little is known on how impactful blended learning in terms of achieving the desired learning outcome. This is because students’ learning style has influenced their achievement and if mediated in a hybrid learning environment like blended learning will further result in evaluation and performance of blended learning environment. This study aims to investigate learning styles among the students and their relationship with perceptions of blended learning. The study involved 119 students taking the course of Diploma in Information Technology at a private university. The learning style of the students was determined by using Kolb's Learning Style Inventory, while perceptions of blended learning were investigated using elements of process, content, and usability. Student perceptions on blended learning were assessed using a one-way ANOVA to determine the correlation with the learning style of the students. Majority of the students belong to the Convergent category, followed by Divergent, Accommodator, and Assimilator. The outcomes of this study showed no meaningful difference between students’ learning styles and their perceptions towards blended learning. The findings from the study could benefit academician in designing more suitable material according to students' preferred mode such as more hands-on tasks for Convergent groups, which belief, can improve the student's achievement.
Volume: 9
Issue: 1
Page: 195-202
Publish at: 2020-03-01

Veins projection performance based on ultrasonic distance sensor in various surface objects

10.11591/ijeecs.v17.i3.pp1362-1370
I Putu Adi Surya Gunawan , Riyanto Sigit , Agus Indra Gunawan
Intravenous therapy aims to inject fluids such as medicine or nutritions into the body via vein vessel. This procedure is needed in various cases whether in an ordinary or emergency. Every person has a different difficulty level thus a nurse usually encountered a problem when locating the position of vein vessel. A visualization device that able to work in realtime and have high mobility is really necessary for an emergency situation to speed up the intravenous access. In this study, a stand-alone veins visualization system was developed. The back-projection method that can adjust based on distance was used to speed up the visualization process. The distance between the device and the object is obtained by an ultrasonic distance sensor. The results of this projection method with a flat surface have maximum shift of 0.48 mm. While on various surfaces, projection shifts under 0.9 mm reach 89% from 140 measurement points. Projection shifts that reach more than 0.9 mm occurred due to the sensor readings are on steep curvature or large angles between segments and sensors.
Volume: 17
Issue: 3
Page: 1362-1370
Publish at: 2020-03-01

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

10.11591/ijai.v9.i1.pp146-154
Putra Wanda , Marselina Endah Hiswati , Huang J. Jie
Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.
Volume: 9
Issue: 1
Page: 146-154
Publish at: 2020-03-01

The opinions of students, professors and practice teachers on the teaching practice course

10.11591/ijere.v9i1.20363
Esra Töre
The goal of this study was the evaluation of the teaching practice course in the Psychological Counseling and Guidance (PCG) department by students, professors and practice teachers. It was a pneumology study designed with qualitative research method. The study group consists of seven students, five professors and five practice teachers from 3 different schools and 3 different universities in İstanbul. Study data were analyzed with content analysis. It was found that the communication and collaboration between the professors and practice teachers of the teaching practice course were low, and the applications within the scope of the course were inadequate. Additionally, the study has revealed that it is necessary to inform the practice teachers about the previous courses and internships the students have taken, to ensure the attendance of students to full-day practice courses on different days and that a detailed teaching practice course guide is prepared. This study is thought to benefit the literature in the sense that it shows how PCG teaching practice course differs from other teaching practice courses, in addition to suggestions to the decision makers and practitioners for improving the course.
Volume: 9
Issue: 1
Page: 10-20
Publish at: 2020-03-01

Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

10.11591/ijeecs.v17.i3.pp1632-1638
Surender Reddy Salkuti
This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.
Volume: 17
Issue: 3
Page: 1632-1638
Publish at: 2020-03-01

Detection of cardiac sounds components: a pilot study

10.11591/ijeecs.v17.i3.pp1330-1337
Norezmi Jamal , Nabilah Ibrahim , MNAH Sha’abani , Zulkifli Taat
This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCAL Classifying Heart Sounds Challenge database were examined for analysis. This work is significant in studying the time and time-frequency based detection of cardiac sounds components characteristics. In time-based analysis, envelope of signal energy was used to do the peak detection of S1, S2 and murmur and also analysis of cardiac cycle, systole and diastole duration. While time-frequency based analysis was used to determine the S1, S2 and murmur frequency range. The findings yield the overall accuracy of envelope-based detection for normal cardiac sound signal at 60.85% while for abnormal cardiac sound signal at 57.24%.
Volume: 17
Issue: 3
Page: 1330-1337
Publish at: 2020-03-01
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