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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 16 Documents
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Search results for , issue "Vol 5, No 2 (2021)" : 16 Documents clear
A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem Halim, Yosua; Nugraheni, Cecilia Esti
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.491

Abstract

Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time.  Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time.
Bioinformatic Analysis in Designing Mega-primer in Overlap Extension PCR Cloning (OEPC) Technique Mardalisa, -; Suhandono, Sony; Yanti, Novi; Rozi, Fazrol; Nova, Fitri; Primawati, -
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.459

Abstract

Bioinformatics has developed into an application tool for basic and applied research in the biomedical and biotechnology field. Polymerase Chain Reaction (PCR) is a common technique in the molecular area that has always involved bioinformatics science. PCR cloning techniques such as TA cloning and PCR-mediated cloning exhibit complex processes with low success rates. One easy, effective, and practical solution is to use a mega-primer with the Overlap Extension PCR Cloning (OEPC) technique. The success of PCR cloning using the mega-primer design in the OEPC technique is strongly influenced by the characteristics of the mega-primer used. Knowledge of mega-primer characteristics is one of the important factors in the success of PCR cloning. The design process for the mega-primer str promoter was characterized based on the principle of a genetic algorithm using the web-based bioinformatics tools such as ClustalW, NetPrimer, and BLAST. The success of the mega-primer construction in producing recombinant pSB1C3 vector has been confirmed by the sequencing method and the function of the reporting protein (AmilCP). DNA analysis shows a 100 % homologous sequence on the str promoter, while  E. coli colonies successfully express the purplish-blue color. Mega-primer characters can save costs and time of the research by maintaining the primer parameters that provide optimal values and increase the success value of PCR cloning via bioinformatics software. Hence, implications on biological problems, especially using DNA and amino acid sequences, could solve rapidly.
Analysis of Web-based Learning Interface Design based on Experts’ Verification for Higher Education Bakar, Zuriana Abu; Salim, Fatin Sarah; Zainuddin, Nor Fatin Farzana; Noor, Noor Maizura Mohamad; Mohemad, Rosmayati
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.410

Abstract

Recently, the Web-based learning (WBL) platform, particularly for higher education, has become more crucial due to the Covid-19 pandemic. Thus, due to the increased use of WBL   in higher education, an effective WBL interface design for higher education is truly important in order to attract students to use WBL and to further keep them engaged during learning via the Web-based platform. Therefore, the aim of this study was to determine the aesthetics of web interfaces based on experts’ opinions. This study adopted a quantitative research approach involving a data-gathering survey. Fifteen (15) WBL interfaces were designed based on nine (9) design principles which were balance, proportion, simplicity, alignment, movement, hierarchy, consistency, contrast, and proximity. The results of this study discovered that nine (9) WBL interfaces were determined by the experts as aesthetic interfaces, five (5) WBL interfaces as non-aesthetic and 1 (one) WBL interface was considered neither aesthetic nor non-aesthetic. This finding revealed that six (6) out of nine (9) interfaces had the balance design principle. However, balance was also in most non-aesthetic interfaces. A possible reason that balance was the most design principle in both the aesthetic and the non-aesthetic interfaces is that when designing WBL interfaces, there is a need to consider the combination of the design principles as a whole, and not count the design principles individually. In conclusion, this study's findings could contribute to the knowledge in the Human Computer Interaction domain, specifically in the interface design area.
Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors Youngseok Lee; Jungwon Cho
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.552

Abstract

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.
Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes A.B.M. Wijaya; D.S. Ikawahyuni; Rospita Gea; Febe Maedjaja
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.448

Abstract

Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.
Data Analysis from Two-choice Decision Tasks in Visual Information Processing Kraleva, Radoslava; Kralev, Velin; Koprinkova-Hristova, Petia
JOIV : International Journal on Informatics Visualization Vol 5, No 2 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.2.413

Abstract

Data analysis are important tasks in research. The present study focuses on the analysis of data sets from human eye movement experiments. The results of the experiments were analyzed according to two criteria – gender and age of the participants. The participants were divided into 3 groups, respectively group 1: between 20 and 35 years, group 2: between 36 and 55 years and group 3: between 56 and 85 years. The results showed that 75% of the two-choice decision tasks were solved correctly. This trend was maintained among the participants from group 1 – respectively 75.4%. The participants from group 2 gave more correct answers – respectively 82.2%, but the participants from group 3 gave fewer correct answers – respectively 70.2%. The average value of the response time indicator (of all participants) was 1455 ms. The response time of the participants from groups 1 and 2 was shorter than the average (respectively with 483 ms and 235 ms). The response time of the participants from group 3 was longer than the average (respectively with 626 ms).

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