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ISSN : 2407439X EISSN : - DOI : -

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Computer Science & IT Electrical & Electronics Engineering

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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Articles 649 Documents

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The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm Imam Tahyudin; Hidetaka Nambo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of PSO and Cauchy distribution because this performance proved to reach the optimal rules. In this paper, we focus to adopt the combination for solving association rule mining (ARM) optimization problem in numerical dataset. Therefore, the aim of this research is to extract the rule of numerical ARM optimization problem for certain multi-objective functions such as support, confidence, and amplitude. This method is called PARCD. It means that PSO for numerical association rule mining problem with Cauchy Distribu- tion. PARCD performed better results than other methods such as MOPAR, MODENAR, GAR, MOGAR and RPSOA.

Honey Yield Prediction Using Tsukamoto Fuzzy Inference System Tri Hastono; Albertus Joko Santoso; Pranowo Pranowo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides a solution for prediction of honey yield type Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield using FIS Tsukamoto closed the real honey yield with RMSE value of 9.44933860119277.

Discovering Drugs Combination Pattern Using FP-growth Algorithm Rini Anggrainingsih; Nach Rowi Khoirudin; Haryono Setiadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

A drug can be used to deal more than one diseases and to deal an illness often need a combination of more than one drugs. This paper present how to discover a pattern of a combination of medicines related to a diagnosis of diseases using FP-Growth one of frequent pattern mining algorithm. We use FP- Growth because it has better performance than Apriori and Eclat. Data is collected from outpatients pharmacy of Sukoharjo state hospital, Central Java, Indonesia during January 2015 to June 2016 and obtain 526,195 records of prescription data and use a diagnosis of diseases base on the ICD-10 standard. This studies just apply on the top ten of the most frequently occurred illness in the outpatient's services of Sukoharjo state hospital. Then the pattern of association between diseases and combination of drugs was reviewed by pharmacist committed to being validated. These studies result in some combination of medicines for to top ten of the most frequent diseases. We also found 21 similar combinations of drugs for various diseases. In the future, this finding can be used to provide suggestions to physicians to select an appropriate mix of the drug to deal some diseases.

Classifiers Evaluation: Comparison of Performance Classifiers Based on Tuples Amount Mochammad Yusa; Ema Utami
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

The aim of this study is to compare some classifiers’ performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features and 49.736 tuples. The methodology of this research starts from preprocessing phase. After that, the clean dataset is divided into 5 subsets which represent every multiple of 10.000 tuples randomly. Each particular subset will be validated by three traditional classifiers i.e. Naive Bayes, K-Nearest Neighbor (k-NN), and Decision Tree. We also implement some setting parameters of each classifier except Naïve Bayes. Validation method used in this research is 10-Fold Cross-Validation. As the final conclusion, we compare the performance of classifiers based on the number of tuples. Our study indicates that the more the number of tuples, the lower and weaker the MAE and Accuracy performances whereas the kappa statistic performance tend to be fluctuated. Our study also found that Naïve Bayes outperforms k-NN and Decision Tree in overall. The top classifiers performances were reached in a 20.000-tuple evaluation.The aim of this study is to compare some classifiers’ performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The r

Odor Localization using Gas Sensor for Mobile Robot Nyayu Latifah Husni; Ade Silvia Handayani; Siti Nurmaini; Irsyadi Yani
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

This paper discusses the odor localization using Fuzzy logic algorithm. The concentrations of the source that is sensed by the gas sensors are used as the inputs of the fuzzy. The output of the Fuzzy logic is used to determine the PWM (Pulse Width Modulation) of driver motors of the robot. The path that the robot should track depends on the PWM of the right and left motors of the robot. When the concentration in the right side of the robot is higher than the middle and the left side, the fuzzy logic will give decision to the robot to move to the right. In that condition, the left motor is in the high speed condition and the right motor is in slow speed condition. Therefore, the robot will move to the right. The experiment was done in a conditioned room using a robot that is equipped with 3 gas sensors. Although the robot is still needed some improvements in accomplishing its task, the result shows that fuzzy algorithms are effective enough in performing odor localization task in mobile robot.

A Study of the Number of Wavelengths Impact in the Optical Burst Switching Core Node Hani A. M. Harb; Waleed M. Gaballah; Ahmed S. Samra; Ahmed Abo-Taleb; Arief Marwanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

In Wavelength Division Multiplexing (WDM), several wavelengths run on an optical fiber link that connects two optical switches. The multiple wavelengths are exploited that minimized the contention problem in the Optical Burst Switching (OBS) core node. Mathematical model is used in order to investigate the impact of the wavelengths numbers OBS core node. Two performance metrics are proposed such as the steady-state throughput and the probability of burst loss using steady- state occupancy probabilities and Poisson traffic model arrivals. Numerical results show that at different values of network traffic and some design parameters such as wavelength conversion capability and the mean arrival rate could reveal the OBS performance.

Performance Analysis of CSI:T Routing in a Delay Tolerant Networks Hardika Kusuma Putri; Leanna Vidya Yovita; Ridha Muldina Negara
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Delay Tolerant Network (DTN) is a network that allows nodes to move dynamically and doesn’t always provide for end-to-end connection. The dynamically of nodes make the movement of the nodes becomes important. The movement model of nodes will record their behavior and this could be utilized to develop a new routing protocol in DTN. One of the DTN routing protocol that utilize it is CSI: T routing. CSI: T routing viewed a node based on their behavioral profile which is represent the mobility preference. Our study is measure the performance of CSI: T in terms of delivery probability, overhead ratio, and average latency by changing the buffer capacity, packet lifetime, and number of nodes. We used Opportunistic Network Environment (ONE) Simulator for the simulate the real-life scenario of a college-routines.

A Reconfigurable MIMO Antenna System for Wireless Communications Evizal Abdul Kadir
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

A reconfigurable antenna system is proposed to improve data throughput limitations in multiple input multiple output wireless communication systems in this investigation. The4×4 MIMO antenna is designed to operate in the 2.4 and 2.6 GHz for Wireless Local Area Network (WLAN) and Long TermEvolution (LTE) applications. The system’s radiation pattern re-configurability is realized by using the microcontroller-drivenPIN diode switching concept. Simulations and measurements exhibited good agreements for the single, 2×2 MIMO and 4×4MIMO configurations. The antenna is operational between 2.387to 2.628 GHz, while the simulated and measured reflection coefficients are at least -24.3 dB. All configurations produced anarrow beam forward radiation, while the envelope correlation coefficient (ECC) and diversity gain for the two MIMO configurations are below 0.5 and at least 9 dBi, respectively.

Conceptual Framework for Public Policymaking based on System Dynamics and Big Data Feldiansyah Bin Bakri Nasution; Dr. Nor Erne Nazira Bazin; Dr. Hasanuddin Hasanuddin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Public policy is the critical key of the welfare programs. It is also a powerful instrument to achieve a feasible national competitiveness. Unfortunately, many public policy making processes does not utilize an appropriate data and tool in holistic and systematical approach. This research will focus on creating a comprehensive conceptual framework for public policymaking based on data and system approach. In connection with information technology, there are at least two approaches that will be considered for obtaining a more comprehensive public policy. First is utilization of Big Data to extract information. It is believed that if more accurate data are collected and analyzed, then more comprehensive public policy is created. Utilization of data mining will be intensively used to obtain knowledge. The second approach is the system dynamics. The knowledge created in the first approach is useful in modeling the system. The model will be used to simulate the future possibilities of several scenarios. The scenario with the best outcome is selected as an input for public policymaking. At the end of this research, a conceptual framework for public policy making will be created by incorporating Big Data and system dynamics.

Discovering Process Model from Event Logs by Considering Overlapping Rules Yutika Amelia Effendi; Riyanarto Sarno
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of the discovered process models is a must. Nowadays, using process execution data in the past, process models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete information, there are some cases that are overlapping in process model. Moreover, the rules which are generated from existing method are not suitable with the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.

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