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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 149 Documents
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Data and Management Traffic of IEEE 802.15.4 ZigBee-Based WSN Baqer , Naseem K.; Abbas , Ali W.; Salih , Bassam A.
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 2 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.2.210

Abstract

Wireless sensor networks (WSNs) are an amalgam of wireless technologies. They are extensively utilized in numerous industries, including agriculture, medical, and military fields. In the vast majority of cases, these technologies are deployed in monitoring environmental or physical parameters including sound, pressure, and temperature. WSNs employ various technologies, including radio frequency (RF), Wi-Fi, Bluetooth, ZigBee, and Z-Wave. Zigbee in particular has greater potential for energy-savings in long-distance transmissions, and consequently has emerged as the preferred standard for use in WSNs. In Zigbee-assisted networks, the three primary data-communication devices are ZigBee coordinators, routers, and nodes. The coordinator device gathers, stores, and processes the data before forwarding it to the next appropriate node or the base-station. The system model comprises several zones with each zone containing several sensors. Each sensor node transfers data to the master node, which serves as the ZigBee coordinator. The software used for this simulated investigation is the Riverbed Modeler V17.5. This paper examines the data traffic, management traffic, and load performance of the four modelled systems. The findings demonstrate that whereas the number of coordinators has no effect on data traffic, an increase in the number of routers correspondingly increases both the amount of data sent and received. The MAC follows the same pattern.
Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning Arif, Md; Hasan, Mehedi; Al Shiam, Sarder Abdulla; Ahmed, Md Parvez; Tusher, Mazharul Islam; Hossan, Md Zikar; Uddin, Aftab; Devi, Suniti; Rahman, Md Habibur; Ali Biswas, Md Zinnat; Imam, Touhid
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 2 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.2.211

Abstract

Social media platforms, particularly Twitter, have become essential sources of data for various applications, including marketing and customer service. This study focuses on analyzing customer interactions with Amazon's official support account, "@AmazonHelp," to understand and predict changes in customer sentiment during these interactions. Using the Twitter API, we extracted English-language tweets mentioning "@AmazonHelp," pre-processed the data, and categorized conversations to facilitate analysis. The primary objectives were to classify changes in customer sentiment and predict the overall sentiment change based on initial sentiment. We conducted experiments using multiple machines learning algorithms, including K-nearest neighbor, Naive Bayes, Artificial Neural Network, Bayes Net, Support Vector Machine, Logistic Regression, and Bagging with RepTree. Our dataset comprised over 6,500 conversations, filtered to include those with four or more tweets. Results indicated that K-nearest neighbor and Support Vector Machine offered the best balance between accuracy and F-measure, while Bagging with RepTree achieved the highest accuracy but had a lower F-measure. This study demonstrates the potential of integrating sentiment analysis and machine learning to effectively predict customer sentiment in social networks, providing valuable insights for improving customer engagement strategies.
A Cutting-Edge Deep Learning Method for Enhancing IoT Security Ansar, Nadia; Ansari, Mohammad Sadique; Sharique, Mohammad; Khatoon, Aamina; Malik, Md Abdul; Siddiqui, Md Munir
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.37

Abstract

There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious. The real-time processing capability, scalability, and low false alarm rate in our model surpass some traditional IDS approaches and, therefore, prove successful for application in today's IoT networks. The development and the performance of the model, with possible applications that may extend to other related fields of adaptive learning techniques and cross-domain applicability, are discussed. The research involving deep learning for IoT cybersecurity offers a potent solution for significantly improving network security.
Systematic Literature Review on Software Requirement Engineering in 5.0 Industry: Current Practices and Future Challenges Pujiharto, Eka Wahyu; Tikasni, Elisa; Lewu, Retzi; Sudirman, San; Utami, Ema
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.152

Abstract

The advancements in technology within the industrial era 5.0 are swiftly progressing, particularly in software research and development, exerting a profound influence on various facets of software engineering, notably known as requirements engineering. This research undertakes a systematic literature study from 2020 to 2023, focusing on software system requirements engineering publications and exploring diverse methodologies and implementations. Most are journals and proceedings within these years. This SLR identifies, evaluates and interprets the prevailing practices in Requirements Engineering within the domain of 5.0 Industry. Specifically, it sheds light on the basic method used recently, highlighting adopting agile methodologies, model-based engineering, and interdisciplinary collaboration as auspicious trends. Initially, a pool of 137 articles from Scopus discussing software requirements engineering was identified and refined to 53 final articles based on predefined keywords. This result shows that current methodologies and trends are lacking in meeting new difficulties, which was raised as the side effect of 5.0. It implies the importance of a greater emphasis on cybersecurity, agile development processes, interoperability, and the smooth integration of IoT and AI technologies. The needs are the formidable challenges stemming from the intricacies of system architectures, and the absence of standardization looms large, necessitating concerted efforts for resolution. System architecture must be made in a compact form without any bargain while, at the same time, international standards should be proposed to meet the evolution of software requirement engineering. These findings underscore the imperative for innovation, data security, and an integrative approach to navigating the dynamic landscape of Industry 5.0.
The Effect of Acetylation on Mechanical Properties of Biodegradable Plastic Made from Hipere Starch Asmuruf, Frans A.; Chesiana; Aritonang, Winda; Supeno
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.154

Abstract

The study addresses the global problem of pollution caused by the long-term decomposition of plastic waste. It aims to explore the development of biodegradable plastics using Hipere starch and glycerol as a sustainable alternative, emphasizing their potential environmental benefits, abundance, and low cost. The primary materials used are Hipere starch, a natural polymer derived from plants, and glycerol as a plasticizer. These were selected for their compatibility and effectiveness in creating biodegradable plastics. Biodegradable plastics were synthesized through an acetylation process that modifies the starch, aiming to enhance its properties. Various concentrations of starch were tested to evaluate their impact on mechanical and physical characteristics. A soil burial test was conducted to assess biodegradability by monitoring mass reduction over seven days. The resulting plastics exhibited transparency, lightweight properties, insolubility in water, and mold-conforming shapes. Mechanical properties, including tensile strength and elongation, improved with higher starch concentrations. The soil burial test showed consistent mass reductions between 1-3% daily, with the most significant reduction occurring on day 7, demonstrating biodegradability. While improvements were observed, further research is needed to enhance mechanical properties by incorporating additional polymers or alternative modification techniques. This could expand the applications and durability of biodegradable plastics in various industries.
Enhance Operational Efficiency of Manufacturing Process Using Six Sigma in Small Scale Manufacturing Industry: DMAIC Approach Kumar, Narender; Kumar, Raj
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.192

Abstract

An effective framework for driving process enhancement and boosting operational efficiency in the manufacturing sector has developed as the Six Sigma DMAIC approach. Focusing on its capacity to methodically detect and resolve process inefficiencies, minimise defects, and optimise performance, this article investigates the implementation for Six Sigma DMAIC concepts in manufacturing. According to the research, it's crucial to set precise objectives for each project and measure success based on those goals in order to implement focused improvement efforts at the manufacturing plant The importance of data-driven decision-making, stakeholder participation, and a culture of continuous improvement in generating sustainable outcomes is highlighted in the article via a thorough analysis of the four DMAIC phases—Define, Measure, Analyse, Improve, and Control. In order to successfully apply DMAIC, the results highlight the need of leadership buy-in, employee agency, and cross-functional cooperation. In today's fast-paced manufacturing industry, staying ahead of the competition is crucial. To achieve continuous improvement, customer satisfaction, & long-term success, manufacturing organisations must embrace the Six Sigma DMAIC concepts.
Vouching the Digital Literacy in Instruction Viz-A-Viz Performance: Contextualized Enhancement Activities Suico, Nikko; Gabuya Jr, Alden Q.; Bardoquillo, Charina; Briones, Zarmie Lis R.; Charcos, Angen May F.; Abaquita, Ma. Carla Y.; Abaquita, Rowena P.; Mancio, Arliezl D.
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.198

Abstract

The study determined the degree of digital literacy and the various ICT skills of Senior High School Students of Biasong National High School, Balamban, Cebu, for the academic year 2021-2022. Significantly, it aimed to identify the students' levels of digital access, specifically in terms of motivational access, material access, skills access, and usage access. The data was processed with the respondents' profiles to create contextualized enhancement activities that used the available materials and digital devices. Simple percentage, weighted mean, Pearson product-moment coefficient of correlation r, simple ranking, independent samples t-test, and one-way analysis of variance (ANOVA) were used with the descriptive-correlation methodology. Findings indicated that there is virtually no correlation between respondents' levels of digital access and their age, sex, and academic standing. Moreover, skills access and use access have low positive correlations with motivation access and overall digital access (r=0.398, p>0.01) and with each other (r=0.398, p>0.01). This implies that, although the correlation value is slightly higher than that of the other intercorrelations of factors, it still shows low correlation. However, there is a weakly positive correlation between Usage and Overall Digital Access Levels (r=0.516, p>0.01) as well as between Skills and Overall Digital Accesses (r=0.643, p>0.01). The Moderate association indicates that, despite the critical importance of digital device usage and ICT skills for developing digital literacy, respondents do not exhibit lower motivation or use digital devices less frequently.
Distributed Denial-of-Service Attack Detection Using One-Dimensional Convolutional Neural Network in Airline Reservation Systems (ARS) Kareem Gharkan , Dhurgham; Kareem Mohammed, Bahaa; Ali Salah, Hussein; Mocanu, Mariana
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.202

Abstract

A prevalent and perilous in the contemporary are Distributed Denial of Service (DDoS) attacks. in which attackers attempted to prevent authorized users from accessing internet services by deploying many attack workstations. This research presents a detection approach based on One Dimension Convolutional Neural Networks, which has created an innovative approach for detecting DDoS attacks that addresses the limitations of conventional methods. The primary objective of this study was to analyze and detect DDoS attacks through the examination of a dataset about the booking of airline tickets. The present investigation utilized the APA-DDoS dataset, comprising two discrete categories: benign traffic and DDoS traffic. Wireshark was utilized to simulate airline data as well. Utilized as one-dimension convolutional neural network (1D CNN) technology, the model achieved an accuracy rating of 99.5%. The experimental outcomes demonstrated that the proposed model effectively and consistently identified DDoS attacks. Solid ability to differentiate between legitimate and malicious traffic has been exhibited by the system, thereby ensuring network security.
Convolutional Neural Network-Based Recognition of Children's Facial Expressions in Response to Gaming Santoso , Hadi; Ferreira Soares, Genoveva; Angelo, Cristopher Marco
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.213

Abstract

This study explores the use of Convolutional Neural Network (CNN) algorithms for the purpose of recognizing children's facial expressions during gaming activities, with a focus on understanding the emotional consequences of gaming. This study intends to build a robust model and assess the accuracy of CNN in detecting six basic emotions among children aged between 6 and 13 years using our dataset that we collected from children in Timor Leste as many as 600 images and the Children's Real-World Facial Expressions (CFEW) dataset of more than 11,000 images for training data. Then we also use our video data and the LIRIS-CSE dataset from the internet as test data as many as 180 videos and images. The data we obtained were images of children when not playing games and playing games consisting of facial expressions, especially those showing anger, happiness, sadness, fear, surprise, and neutral. This methodology consists of various processes, including data collection, preprocessing, augmentation, model training, and evaluation, with the main goal of identifying patterns and trends in children's emotional responses to games. The results of this study indicate that the final accuracy of detecting children's faces when playing games is 96.78% and the validation data accuracy value is 95.32%. It is proven that the CNN architecture or model used in this research dataset is optimal.
Analysis of Pile Foundation Bearing Capacity and Soil Classification in Padang City Liliwarti; Silvianengsih; Mahardika, Tiara; M, Bagaskoro; Abddilah, Farizi
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.220

Abstract

Padang City, west of Sumatra, is highly vulnerable to natural disasters such as earthquakes, landslides, and floods. The soil conditions in this area, consisting of tuff, silica, and rocks with low cohesion, pose challenges due to their weak bearing capacity, increasing the risk of ground movement or subsidence. This study analyzes the bearing capacity of pile foundations and soil classification in the Sungai Sapih area using the Cone Penetration Test (CPT) method and soil classification based on the Bagemann method. Investigations were conducted at five sounding points (S1–S5) in Sungai Sapih to understand the soil characteristics and provide optimal foundation planning recommendations. The results of this study indicate that the soil layers are dominated by clay, with variations such as organic clay, very stiff clay, and clay loam, all of which tend to have low end-bearing capacity. At depths of up to 11 meters, the cone resistance (qc) values were very low at all points, indicating that the pile foundation cannot rely on end-bearing support at this depth. However, increased friction along the pile shaft significantly contributes to the ultimate bearing capacity of the pile foundation. Hard soil layers were identified at depths of 12–16 meters, with qc values of approximately 150 kg/cm², providing a reference for deep foundation design. Based on the result of this study, the use of piles foundation and bored piles is recommended to reach the hard soil layers to ensure structural stability. Additionally, an analysis of soil settlement is necessary due to the high potential for deformation in the clay layers. This study is expected to serve as a guideline for safe and sustainable foundation planning in the Sungai Sapih area, rapidly developing as a center for government and public facilities in Padang City.

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