PERANCANGAN MEDIA INFORMASI MENGENAI SEJARAH DAN PERKEMBANGAN DESAIN GRAFIS DI KOTA BANDUNG

Bandung sebagai kota desain sudah banyak membuktikanya, terlepas dari sebuah fakta dari United Nations of Educational, Scientific, and Cultural Organization (UNESCO) yang mengatakan bahwa Bandung secara resmi menjadi member sebagai kota desain pada 2015, salah satu aspek yang berkembang pesat adalah Desain Grafis, perkembangan nya terbukti dalam bidang sejarah dengan latar belakang pendidikan pertama Desain Grafis di Indonesia, juga industri dan juga organisasi. Namun dengan adanya tiga bidang yang melengkapi Desain Grafis di Bandung belum memiliki pencatatan yang lengkap, karena kesadaran manusia tidak bisa hanya sebatas mengingat tanpa mempunyai sebuah arsip yang menjadi data lengkap yang membahas mengenai Desain Grafis di Bandung. Maka diperlukan sebuah pencatatan yang membahas mengenai sejarah dan perkembangan Desain Grafis di Bandung, karena dengan pencatatan siapapun bisa melihat dan juga mempelajari bagaimana sebuah proses tentang Desain Grafis di Bandung. Penilitian ini menggunakan metode kualitatif dengan pengumpulan data studi pustaka dan wawancara narasumber juga menggunakan analisa SWOT untuk mengetahui apa kelebihan dan juga kekuarang dari sebuah media yang akan dirancang. Pencatatan ini akan dirancang menjadi sebuah media informasi berbentuk Buku pembelajaran mengenai proses pembentukan Desain Grafis di Bandung yang akan berguna bagi generasi muda yang menfokuskan diri kepada Desain Grafis, sehingga generasi selanjutnya memiliki pemahaman tentang sejarah dan perkembangan masa kini dan juga memiliki referensi yang kuat dalam hal pengetahuan Desain Grafis di Bandung Kata Kunci : Media Informasi, Sejarah, Desain Grafi

A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

The selection of parameters in grinding process remains as a crucial role to guarantee that the machined product quality is at the minimum production cost and maximum production rate. Therefore, it is required to utilize more advance and effective optimization methods to obtain the optimum parameters and resulting an improvement on the grinding performance. In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. The efficiency of the three algorithms are evaluated and comparedwith previous results obtained by other optimization methods on similar studies.The experimental results showed that PSO algorithm achieves better optimization performance in the aspect of convergence rate and accuracy of best solution.Whereas in the comparison of results of previous researchers, the obtained result of PSO proves that it is efficient in solving the complicated mathematical model of surface grinding process with different conditions

Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model

Using convolution neural networks for improving customer requirements classification performance of autonomous vehicle

Customer requirements are vital information prior to the early stage of autonomous vehicle (AV) development processes. In the development process of AV many decisions have been made concerning customer requirements at the first stage. The development of AV that meets customer requirements will increase the global consumer and remain competitive. Safety and regulation are one of crucial aspect for customers that requires to be concerned and evaluated at the early stage of AV development. If safety and regulation related requirements did not well identified, AV developer could not develop the safest vehicles due to the huge compensation of accidents. To efficiently classify customer requirements, this study proposed an approach based on natural language processing method. For classification purpose, the customer requirements are divided into six categories that the concept are come from the quality management system (QMS) standard. These categories will be as input for the next process development in making the best decision. Most of conventional algorithms, such as, Naive Bayes, MAXENT, and support vector machine (SVM), only use limited human engineered features and their accuracy for customized corpus in sentences classification are proven low which is less than 50 percent. However, in literature, convolution neural networks (CNN) have been described efficiently to overcome the customized corpus of sentence classification problems. Therefore, this study implements CNN architecture in customized corpus classification operations. As the results, the accuracy of CNN classification has improved at least 6 percent compared to the conventional algorithms

A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

The selection of parameters in grinding process remains as a crucial role to guarantee that the machined product quality is at the minimum production cost and maximum production rate. Therefore, it is required to utilize more advance and effective optimization methods to obtain the optimum parameters and resulting an improvement on the grinding performance. In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. The efficiency of the three algorithms are evaluated and comparedwith previous results obtained by other optimization methods on similar studies.The experimental results showed that PSO algorithm achieves better optimization performance in the aspect of convergence rate and accuracy of best solution.Whereas in the comparison of results of previous researchers, the obtained result of PSO proves that it is efficient in solving the complicated mathematical model of surface grinding process with different conditions

Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network

There is a growing interest of research being conducted on detecting eye blink to assist physically impaired people for verbal communication and controlling devices using electroencephalogram (EEG) signal. One particular eye blink can be determined from use of peak points. Therefore, the purpose of peak detection algorithm is to distinguish an actual peak location from a list of peak candidates. The need of a good peak model is important in ensuring a satisfy classification performance. In general, there are various peak models available in literature, which have been tested in several peak detection algorithms. In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models. To generalize the performance evaluation, two case studies of eye blink EEG signal are considered, which are single and double eye blink signals. It has been observed that the best test performance, in average, is 91.94% and 87.47% for single and double eye blink signals, respectively. These results indicate that the Acir’s peak model offers high accuracy of peak detection for the two eye blink EEG signals as compared to other peak models. The result of statistical analysis also indicates that the Acir’s peak model is better than Dingle’s and Dumpala’s peak models

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model

Resducational Kit The Development Of An Electronic Quiz Board That Test Engineering Students Knowledge On Resistors Concept In Electrical Circuit

The concept of basic electrical circuit has been covered in most engineering field especially electrical and electronic engineering via Fundamental of Electrical Circuit subject. One of the topic is to test student knowledge on calculation on the combinatorial resistors connection which can be either in series, parallels or combination of both. This can be done by theoretical calculation or hands-on assembling the circuit. This educational kit attempts to bridge the gap between theory and real-time hands-on for the circuit connection. The kit will give questions by providing the desired resistors output while the students need to do the connection base on the available resistors values in order to obtain the desired output resistor. The educational kit will deliverer the answer once the connection is successfully constructed. The kit is developed using Arduino Uno Microcontroller with other components such as LCD, LED, keypad, buzzer and resistors. This paper explains the procedure required in developing the prototype and testing being done to verify the functionality of the proposed kit

An Experimental Study of the Application of Gravitational Search Algorithm in Solving Route Optimization Problem for Holes Drilling Process

Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures