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All Journal JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science Bulletin of Social Informatics Theory and Application
Samah, Khyrina Airin Fariza Abu
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Computer Science & IT Engineering Social Sciences

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Educational technology using multimedia in science learning: A systematic review Riza, Lala Septem; Hasanah , Lilik Nur; Putri , Ananda Hafizhah; Budiman, Budiman; Safitri, Fibriyana; Putri , Liandha Arieska; Hayati , Nurlaila; Solihah, Putri Amelia; Samah, Khyrina Airin Fariza Abu
Bulletin of Social Informatics Theory and Application Vol. 7 No. 2 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i2.661

Abstract

This systematic review aims to provide a systematic review of the scientific published studies that examined different multimedia tools in the science teaching and learning process to identify the existing multimedia-based tools and understand their usage, application areas, and impacts on the education system. There are 60 articles extracted from the Scopus database from 2012 to 2022. This review generates six averments about the current study; (1) The most numerous multimedia components are text components; (2) the learning process and application of the use of multimedia in science education can be applied at various levels of education; (3) The majority of the study was conducted in the United States; (4) the current use of technology that is most widely used is the use of power points; (5) the role of technology in the learning process as learning tools, teaching tools, assessment, and evaluation; and (6) the most science content conducted in multimedia research is chemistry.
Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode Riza, Lala Septem; Rahman, M Ammar Fadhlur; Prasetyo, Yudi; Zain, Muhammad Iqbal; Siregar, Herbert; Hidayat, Topik; Samah, Khyrina Airin Fariza Abu; Rosyda, Miftahurrahma
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p231-248

Abstract

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families.
An Assessment Algorithm for Indoor Evacuation Model Samah, Khyrina Airin Fariza Abu; Halim, Amir Haikal Abdul; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.933

Abstract

The public buildings increased significantly with the economy's growth and the population's advancement. The complexity of the indoor layout and the involvement of many people cause the indoor evacuation wayfinding to the nearest exit to be more challenging during emergencies such as fire. In order to overcome the problem, each building is compulsory to follow the standard evacuation preparedness required by Uniform Building By-Law (UBBL). Researchers have also developed evacuation models to help evacuees evacuate safely during the evacuation from a building. However, building owners do not know which evacuation model is suitable for implementing the chosen high-rise building. Two problems were identified in choosing a suitable evacuation model during the decision-making process. First, many developed evacuation models focus on studying different features of evacuation behavior and evacuation time. Second, the validation and comparison of the evacuation model is the missing process before applying the suitable evacuation model. Both validation and comparison procedures were made independently without any standard assessment that encapsulates the critical incident features during the indoor evacuation and virtual spatial elements. Therefore, this research proposed an indoor evacuation assessment algorithm to solve the problem. The assessment algorithm refers to the elements developed in our previous study. We determined attributes, executed simulations, and evaluated the cluster performance using the developed framework. The outcome can help the building owners assess which suitable existing evacuation model is the best to implement at the chosen building.
Co-Authors Budiman Budiman Halim, Amir Haikal Abdul Hasanah , Lilik Nur Hayati , Nurlaila Ibrahim, Zaidah Lala Septem Riza Putri , Ananda Hafizhah Putri , Liandha Arieska Putri Amelia Solihah Rahman, M Ammar Fadhlur Rosyda, Miftahurrahma Safitri, Fibriyana Siregar, Herbert Topik Hidayat Yudi Prasetyo Zain, Muhammad Iqbal