Proceeding of the Electrical Engineering Computer Science and Informatics
Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, students, engineers and practitioners together to participate and present their latest research finding, developments and applications related to the various aspects of electrical, electronics, power electronics, instrumentation, control, computer & telecommunication engineering, signal processing, soft computing, computer science and informatics.
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<![CDATA[Case Based Reasoning Adaptive E-Learning System Based On Visual-Auditory-Kinesthetic Learning Styles ]]>
<![CDATA[Abdul Rahman]]>;
<![CDATA[Utomo Budiyanto]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1955
<![CDATA[Current technological developments have reached all fields including education. With the support of technology, teaching and learning activities can increase to a better level. The problem that occurs at this time in improving the quality of education is the difficulty of students to get grades that are in accordance with the Minimum Completeness Criteria, the difficulty of the teacher providing material in accordance with each student's learning style. This study aims to develop adaptive E-Learning to assist teachers in recommending material that is suitable for each student's learning style. This adaptive e-learning adopts a Visual Auditory Kinesthetic (VAK) learning style and to recommend material using the Case Based Reasoning (CBR) method. Student test results after using adaptive E-learning have fulfilled the Teaching Mastery Criteria with an average grade of 85. This suggests that under adaptive E-learning has been able to improve student grades.]]>
<![CDATA[Prediction Of Students Academic Success Using Case Based Reasoning ]]>
<![CDATA[Abdul Rahman]]>;
<![CDATA[Rezza Anugrah Mutiarawan]]>;
<![CDATA[Agung Darmawan]]>;
<![CDATA[Yan Rianto]]>;
<![CDATA[Mohammad Syafrullah]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1956
<![CDATA[Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.]]>
<![CDATA[OTEC Potential Studies For Energy Sustainability In Riau Islands ]]>
<![CDATA[Ibnu Kahfi Bachtiar]]>;
<![CDATA[Risandi Dwirama Putra]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1957
<![CDATA[Interest in the use of alternative renewable energy resources has been developed recently due to increased energy consumption and depletion of fossil fuel reserves. A major concern the world to reduce is dependence impact from fossil fuel consumption with renewable energy. Renewableenergy sources have enormous economic, environmental benefits and provide energy security. The most potential renewable energy sources of ocean energy include Ocean Thermal Energy Conversion (OTEC). OTEC is a technology to generate electricity using a heat source thermal energy stored in the sea and is becoming increasingly attractive option to supply additional energy for many tropical countries and islands such as Riau Islands. Two monitoring stations were collected in Bintan Island using CTD. CTD profiler allows to the determination of derived and relevant quantities in situ measurement ocean temperature per depth. The relationship between ocean temperature and ocean depth represented with Regression Model Fit Analysis (RMFA). RMFA models to estimates ocean temperature profiles from CTD measurements. To predict ocean depths up to 2000 meters using Equation of State Model (EoSM) of ocean water. The OTEC efficiency value can be calculated using the equation of Carnot efficiency (η). Carnot efficiency maximum in Riau Island is η <0.7.]]>
<![CDATA[Marine Vessel Telemetry Data Processing Using Machine Learning ]]>
<![CDATA[Herry Susanto]]>;
<![CDATA[Gunawan Wibisono]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1958
<![CDATA[In Indonesia, one of the causes of the high cost of fuel in the shipping industry is theft and misuse of fuel. This happened because ship management center unable to monitor all the activities of the ship when the ship sailing in the middle of the ocean. Lately, ship monitoring through the latest technology are being carried out, one of which is the Machine to Machine (M2M) based Vessel Monitoring System (VMS) technology. The development of VMS and telemetry technology has enabled monitoring of engines and fuel consumption of ships in real time. The problem with this VMS system is that there is still a dependency on the analysis of experts who need a long time to analyze various parameters of existing telemetry data, which lead to inaccuracy and delay in anomaly detection. This study conducted a statistical analysis of telemetry data, especially in ship movement and machine activities, and then designed the fuel consumption regularity classification system with the Naive Bayes and Logistics Regression. Naive Bayes method was chosen because it can produce maximum accuracy with little training data, and Logistics Regression was chosen for its simplicity and excellent results in prediction of numerical and discrete data. The results of this study indicate that telemetry data from the VMS system can be used to detect irregularities in Fuel consumption. Tested with selected data, Naive Bayes classification accuracy in irregularities detection is up to 92% while logistic regression is up to 96%.]]>
<![CDATA[Fish Eggs Calculation Models Using Morphological Operation ]]>
<![CDATA[Syaipul Ramdhan]]>;
<![CDATA[Muhammad Syafrullah]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1959
<![CDATA[Calculations on group objects are the concern of current researchers, to find optimal detection and calculation solutions. One of them is fish eggs in a group. Fish cultivators need precision in calculations, because currently conventional methods often make errors in calculations. If the calculation is wrong, it will have an impact on production and sales that are not balanced (loss). Small and easily broken fish eggs are grouped and it isdifficult to do manual calculations. The purpose of this study is to test which segmentation method is the most optimal in calculating these grouped fish egg objects and produce precise and fast calculations. The test model was developed from algorithm of morphological operations,watershed and statistical approaches with the same number of samples. The result shows morphological operation is better than the others with 96.67%, watershed 81.28% and the count statistic is 95.62% with an average calculation process speed of 54.5 seconds for morphological operations, watershed 1 minute 55 seconds and statistical approach 58.9 seconds. As a result. morphology gets the most optimal and fast calculation results.]]>
<![CDATA[Implementation of L3 Function on Virtualization Environment using Virtual Machine Approach ]]>
<![CDATA[Marcel Yap]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1960
<![CDATA[There are 2 approaches to implement layer 3 network function on virtualization platforms, the first approach uses the conventional physical devices; while the second is software-based. Several previous studies have been carried out to test the performance of L3 function on virtualization using software-based and obtained positive result for the performance over the physical-based. While the previous studies were limited only within the scope of testing environment, this paper tries to extend the study not only limited to the performance test based-on RFC 2544 standard, but also implementation in the production environment using virtual machine (VM) approach. Mikrotik CHR (Cloud Hosted Router) designed specifically for virtualization environment will be used as the L3 platform on the VM. Implementation in the production environment was conducted at University computer laboratory that has 207 desktops (190 in the form of virtual desktops, 17 in the form of PCs) not including user' devices that connected via WiFi networks. VM-based approach for routing functions (Layer 3) using Mikrotik CHR has proven to be stable and sufficient for use in the computer laboratory after 6 months of usage. Performance test also shown that VM-based L3 function had higher transfer rates; physical-based router was about 23,4% slower for 1 routing load and 4,25% slower for 2 routings load. The characteristic of VM itself also add some benefits like VM snapshot and migration for recovery. The test also revealed that VM-based L3 function prone to performance penalties when more than one routing load performed compared with physical-based.]]>
<![CDATA[A Third Order based Additional Regularization in Intrinsic Space of the Manifold ]]>
<![CDATA[Rakesh Kumar Yadav]]>;
<![CDATA[Abhishek Singh]]>;
<![CDATA[Shekhar Verma]]>;
<![CDATA[S. Venkatesan]]>;
<![CDATA[M. Syafrullah]]>;
<![CDATA[Krisna Adiyarta]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1961
<![CDATA[Second order graph Laplacian regularization has the limitation that the solution remains biased towards a constant which restricts its extrapolationcapability. The lack of extrapolation results in poor generalization. An additional penalty factor is needed on the function to avoid its over-fitting on seen unlabeled training instances. The third order derivative based technique identifies the sharp variations in the function and accurately penalizes them to avoid overfitting. The resultant function leads to a more accurate and generic model that exploits the twist and curvature variations on the manifold. Extensive experiments on synthetic and real-world data set clearly shows thatthe additional regularization increases accuracy and generic nature of model.]]>
<![CDATA[Spatial Coordinate Trial : Converting Non-Spatial Data Dimension for DBSCAN ]]>
<![CDATA[Eka Arriyanti]]>;
<![CDATA[Ita Arfyanti]]>;
<![CDATA[Pitrasacha Adytia]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1962
<![CDATA[In big data, noise in data mining is a necessity. Its existence depends on data and algorithm, but it does not mean the algorithm caused noise. Although the advantages of the Density Based Spatial Clustering Application with Noise, DBSCAN algorithm, in executing spatial data (two-dimensional data) have been widely discussed, but it has not been convincing in executing non-spatial data. As an algorithm should perform well on any data for optimizing data mining, this research proposes a trial to convert dimensions of non-spatial data into 2 dimensions for executing with DBSCAN algorithm, and a different input value for epsilon to know about its minimum which begins arising noise in the execution. Method of analysis in trial is with considering the attributes of non-spatial data as variables that represent coordinate points, rather than cardinality. Technically, it is assumed that 2-dimensional coordinate axes as a spot point for coordinate with more than or equal 3 dimensions according to development of Cartesian coordinate system, by first paying attention to relationship of variables (attributes). This way is then called Spatial Coordinate. The different input values are with paying attention to numbers from non-zero minimum distance to the forth of epsilon where the epsilon is in integer. The results of trial and testing on clusters formed, with Silhouette Coefficient, point out that the clusters are well, strong, and quality enough. Therefore, this research gives a new way on how preprocessing non-spatial data for DBSCAN algorithm performance.]]>
<![CDATA[Genetic Algorithm With Random Crossover and Dynamic Mutation on Bin Packing Problem ]]>
<![CDATA[Hairil Fiqri Sulaiman]]>;
<![CDATA[Bruri Trya Sartana]]>;
<![CDATA[Utomo Budiyanto]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1963
<![CDATA[Bin Packing Problem (BPP) is a problem that aims to minimize the number of container usage by maximizing its contents. BPP can be applied to a case, such as maximizing the printing of a number of stickers on a sheet of paper of a certain size. Genetic Algorithm is one way to overcome BPP problems. Examples of the use of a combination of BPP and Genetic Algorithms are applied to printed paper in Digital Printing companies. Genetic Algorithms adopt evolutionary characteristics, such as selection, crossover and mutation. Repeatedly, Genetic Algorithms produce individuals who represent solutions. However, this algorithm often does not achieve maximum results because it is trapped in a local search and a case of premature convergence. The best results obtained are not comprehensive, so it is necessary to modify the parameters to improve this condition. Random Crossover and Dynamic Mutation were chosen to improve the performance of Genetic Algorithms. With this application, the performance of the Genetic Algorithm in the case of BPP can overcome premature convergence and maximize the allocation of printing and the use of paper. The test results show that an average of 99 stickers can be loaded on A3 + size paper and the best generation is obtained on average in the 21st generation and the remaining space is 3,500mm2.]]>
<![CDATA[Civil Servant’s E-Government Adoption Levels: Are age and context matters? ]]>
<![CDATA[Iman Sudirman]]>;
<![CDATA[Atya Nur Aisha]]>;
<![CDATA[Joe Monang]]>;
<![CDATA[Ilham Reza Prasetyo]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v6.1964
<![CDATA[This study examines the differences of e-government adoption by civil servants among age groups and between mandatory and voluntary context using UTAUT model. It used a non-probability sampling technique and an online survey to collect the data. A one-way ANOVA using SPSS was conducted to analyze the data. The study finds that most employees have the highest positive adoption levels in effort expectancy. Furthermore, there are significant mean differences between employee’s age group of performance expectancy for mandatory system and facilitating conditions for voluntary system. However, there is no statistically different on civil servant’s adoption level between mandatory and voluntary context.]]>
