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All Journal JOIV : International Journal on Informatics Visualization
Aldo Erianda
Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
Computer Science & IT

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Large Dataset Classification Using Parallel Processing Concept Mohammad Aljanabi; Hind Ra'ad Ebraheem; Zahraa Faiz Hussain; Mohd Farhan Md Fudzee; Shahreen Kasim; Mohd Arfian Ismail; Dwiny Meidelfi; Aldo Erianda
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.361

Abstract

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.
Developing Online Learning Applications for People with Hearing Impairment Hidra Amnur; Yandri Syanurdi; Rika Idmayanti; Aldo Erianda
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.457

Abstract

To make a communication with a hearing-impaired person, who is someone who has a problem with hearing ability, a special form of communication using sign language is required in order to make the purpose of the conversation convey properly. It is clearly that providing a proper and appropriate education for hearing impaired person is very important. Android technology is the best and useful solution for hearing-impaired person in learning as today's technological developments. The purpose of this research was to make an Android-based application for hearing impaired person. Scrum method was used to find and utilize existing libraries as well as the needs for application development. This application provides various kinds of subject from videos and documents uploaded by the teachers. It can be downloaded of it, if it is needed. It means that the users can study anytime and anywhere without concerning of limited time and internet access. Other features of the application are quiz, make schedule, event, chat, memory game, and other features to maximize the online learning process for hearing-impaired person.
A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering Mohammed Ahmed Jubair; Salama A. Mostafa; Aida Mustapha; Zirawani Baharum; Mohamad Aizi Salamat; Aldo Erianda
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.934

Abstract

Due to the rapid increase in data volumes, clustering algorithms are now finding applications in a variety of fields. However, existing clustering techniques have been deemed unsuccessful in managing large data volumes due to the issues of accuracy and high computational cost. As a result, this work offers a parallel clustering technique based on a combination of the K-means and Multi-Agent System algorithms (MAS). The proposed technique is known as Multi-K-means (MK-means). The main goal is to keep the dataset intact while boosting the accuracy of the clustering procedure. The cluster centers of each partition are calculated, combined, and then clustered. The performance of the suggested method's statistical significance was confirmed using the five datasets that served as testing and assessment methods for the proposed algorithm's efficacy. In terms of performance, the proposed MK-means algorithm is compared to the Clustering-based Genetic Algorithm (CGA), the Adaptive Biogeography Clustering-based Genetic Algorithm (ABCGA), and standard K-means algorithms. The results show that the MK-means algorithm outperforms other algorithms because it works by activating agents separately for clustering processes while each agent considers a separate group of features.
Co-Authors Aida Mustapha Dwiny Meidelfi Hidra Amnur Hind Ra'ad Ebraheem Mohamad Aizi Salamat Mohammad Aljanabi Mohammed Ahmed Jubair Mohd Arfian Ismail Mohd Farhan Md Fudzee Rika Idmayanti Salama A. Mostafa Shahreen Kasim Yandri Syanurdi Zahraa Faiz Hussain Zirawani Baharum