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Alde Alanda
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International Journal of Advanced Science Computing and Engineering
Published by Society of Visual Informatics
ISSN : 27147533     EISSN : 27147533     DOI : https://doi.org/10.30630/ijasce
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The journal scopes include (but not limited to) the followings: Computer Science : Artificial Intelligence, Data Mining, Database, Data Warehouse, Big Data, Machine Learning, Operating System, Algorithm Computer Engineering : Computer Architecture, Computer Network, Computer Security, Embedded system, Coud Computing, Internet of Thing, Robotics, Computer Hardware Information Technology : Information System, Internet & Mobile Computing, Geographical Information System Visualization : Virtual Reality, Augmented Reality, Multimedia, Computer Vision, Computer Graphics, Pattern & Speech Recognition, image processing Social Informatics: ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2022)" : 6 Documents clear
The Effect of Adaptive Synthetic and Information Gain on C4.5 and Naive Bayes in Imbalance Class Dataset Sulistiyono, Mulia; Wirasakti, Lucky Adhikrisna; Pristyanto, Yoga
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.576 KB) | DOI: 10.62527/ijasce.4.1.70

Abstract

Class imbalance is a severe problem in classification due to the deep slope on the class axis. The dataset is dominated by the majority class, which has the potential for misclassification. Another problem in classification and clustering is that high-dimensional datasets are found that have the potential to affect the performance of classification algorithms in terms of computation and accuracy. In this study, the class imbalance was handled using the ADASYN k - NN resampling technique and the selection feature using Information Gain. Based on the evaluation results, the sampling contribution matrix can improve the classification model by improving the geometric mean value. The selection feature helps interpret data with more simple features but can reduce the accuracy of the results. The results showed that the implementation of ADASYN k-NN and Information Gain could increase the accuracy score and geometric mean score of Decision Tree C4.5 and Naive Bayes. For further work, this proposed method will be tested on multiclass imbalanced datasets.
Cloud Computing Adoption in the Financial Banking Sector- A Systematic Literature Review (2011-2021) Adwan, Ehab Juma; Alsaeed, Bader Ali
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.997 KB) | DOI: 10.62527/ijasce.4.1.73

Abstract

Scholarly research works on the adoption of Cloud Computing (CC) have recently emerged with the technology’s importance for organizations at a fast pace. Despite the numerous advantages of CC adoption for financial institutions (FI) in terms of storage cost mitigation, computation higher increase, and information access higher access rates from any place, Banking’s CC adoption executives and practitioners are badly seeking to obtain trustworthy recipes of how to utilize CC adoption frameworks to transform banks operations to cloud. In this vein and based on a systematic literature review (SLR) method, we conducted a review of 370 empirical studies from 2011 to 2021, downsized the studies to 27 directly relevant papers to reveal 14 frameworks, methods, models, or strategies of CC adoption in Banking sectors in 14 countries, and compared the findings across studies in terms of the utilized frameworks, methods, models, or strategies.
Design Attention System of Single Mode Aerial Fiber Optic Cable Transmission on Connection Loss on Passive Splitter Asril, Aprinal Adila; Yolanda, Amelia; Lifwarda, -; Putri, Dwi Kemala; Kasmar, Andre Febrian
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.516 KB) | DOI: 10.62527/ijasce.4.1.74

Abstract

In this study, a comparison of the attenuation value of single mode aerial cable with pigtail is made. Perform several types of connections using a 4 cm and 6 cm protection sleeve, with one and two connections and use a barrel adapter connector. How does the connection affect the installation of a 1:2 passive splitter device. The measuring instruments used for the measurement process are Optical Power Meter (OPM) and Optical Time Domain Reflectometer (OTDR). The attenuation value of aerial single mode optical cable is smaller than that of pigtail cable. The measurement results using OPM at a wavelength of 1310 nm, connection using a 4 cm protection sleeve, one connection, the attenuation value is 0.18525dB, this value is smaller than the pigtail cable, which is 1.2728 dB. In the installation of a passive splitter, the attenuation value on the aerial cable is smaller, namely 0.2081 dB compared to the pigtail cable, which is 4.3281. The measurement results using the OTDR obtained the connection loss value for the connection type using a 6 cm protection sleeve, one connection is smaller with a value of 0.155 dB, compared to the connection type using an adapter barrel with a value of 12,216 dB.
Additive Ratio Assessment (ARAS) Method in the Selection of Popular Mobile Games Meidelfi, Dwiny; Idmayanti, Rika; Maulidani, Farhan; Ilham, Muhammad; Muhlis, Farid Alfajr
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.684 KB) | DOI: 10.62527/ijasce.4.1.81

Abstract

Dealing with using smartphones was growing will increase the sales value of the mobile game industry, both from local and foreign developers. It can impact the increasing number of start-ups trying to get involved in the game industry, as well as a large number of job opportunities for those who want to be strengthened in the development of mobile games. This opportunity become the main sector in the domestic creative industry. In Indonesia, the development of mobile games was deemed necessary to analyze the most popular mobile game products that assist by criteria of game. It shows the market trend currently. The researcher hopes this result can provide for developers to choose the type of game to be strengthened based on certain categories. The mobile games involved in this research are Garena Free Fire, Mobile Legend: Bang Bang, PUBG Mobile, Higgs Domino Island, Ragnarok X: Next Generation, Rise of Kingdoms: Lost Crusade, Roblox, State of Survival: Survive The Zombie Apocalypse, Genshin Impact, Coin Monster, Minecraft, Clash Royale, Clash of Clans, Ragnarok M: Eternal Love, State of Survival: The Walking Dead Collaboration. The research was an Additive Ratio Assessment (ARAS) method with the results of 5 recommended mobile games. The test is carried out with a sensitivity test which shows the criterion of "not containing violence" which is the most sensitive criterion among other criteria.
Utilizing Requirement Testing Methods on Web-Based Swab Data Information System Defni, -; Johan, -; Putra, Andani Eka; Nova, Fitri; Andriani, Wiwik
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.219 KB) | DOI: 10.62527/ijasce.4.1.75

Abstract

The spread of the virus caused by SARS-CoV-2 has been designated as an extraordinary event so that the government to speed up the collection and management of this disease then appoints several hospitals that serve as referral hospitals for people who want to check themselves. One of the hospitals used as a reference by the government in West Sumatra is Andalas University hospital. In just one day, the hospital received thousands of samples for examination. The number of samples to be examined and the limited number of administrations that conduct data collection results in the length of the sample collection process. This article aims to design and implement an information system in the data collection of Swab Covid-19 using waterfall model system development methods. The requirement testing model is used for testing a web-based Covid 19 swab data information system whether it is in accordance with system users' needs analysis and design.
Multi-SAP Adversarial Defense for Deep Neural Networks Sharma, Shorya
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 1 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (500.333 KB) | DOI: 10.62527/ijasce.4.1.76

Abstract

Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.Deep learning models have gained immense popularity for machine learning tasks such as image classification and natural language processing due to their high expressibility. However, they are vulnerable to adversarial samples - perturbed samples that are imperceptible to a human, but can cause the deep learning model to give incorrect predictions with a high confidence. This limitation has been a major deterrent in the deployment of deep learning algorithms in production, specifically in security critical systems. In this project, we followed a game theoretic approach to implement a novel defense strategy, that combines multiple Stochastic Activation Pruning with adversarial training. Our defense accuracy outperforms that of PGD adversarial training, which is known to be the one of the best defenses against several L∞ attacks, by about 6-7%. We are hopeful that our defense strategy can withstand strong attacks leading to more robust deep neural network models.

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