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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 8 Documents
 1 
Search results for , issue "Vol. 6 No. 2 (2024)" : 8 Documents clear
Disease Identification on Fig Leaf Images Using Deep Learning Method Bismi, Waeisul; Riana, Dwiza; Hewiz , Alya Shafira
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.6.2.203

Abstract

The fig plant, known as Ficus carica, has been cultivated worldwide, including in Indonesia. It has nutritional benefits and medicinal properties. However, there are still difficulties in growing it, making the plant scarce. The scarcity of fig plants in Indonesia is mainly due to the threat of diseases and viruses that affect them. Various diseases affect fig plants, including leaf rust (Cerotelium fici), mosaic disease, and Bemisia tabaci (whitefly) disease. Infected fig plants become unhealthy, experiencing stunted growth and deformed fruits; thus, it is necessary to identify the diseases accurately using technological assistance. This research aimed to identify diseases in fig leaves automatically. The method began by digitizing fig leaf images and consulting botanical experts specializing in fig plants to determine the types of diseases present. The research produced a dataset of fig leaf images consisting of four classes of fig leaves: Cerotelium fici, mosaic disease, whitefly, and healthy fig leaves. The dataset resulted in the confirmation of 300 fig leaf images. The augmentation techniques were applied to increase the number of images to 3,300 fig leaf images. This dataset was then divided into subsets for training, validation, and testing. For the classification and identification, a Deep Learning approach was used with three models: VGG16, VGG19, and MobileNet. Among these models, MobileNet achieved the highest accuracy of 98.79%. Subsequently, the identification system was implemented by converting the generated model into TensorFlow Lite and integrating it into the Android Studio software, enabling it to function as a mobile application on Android devices.
Impact the Classes’ Number on the Convolutional Neural Networks Performance for Image Classification Ali , Amna Kadhim; Abdullah , Abdulhussein Mohsin; Raheem , Sabreen Fawzi
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.6.2.204

Abstract

Deep learning was developed as a realistic artificial intelligence technique that takes in numerous layers of information and produces the best results in various classes. Deep learning has demonstrated excellent performance in several areas, particularly picture grouping, division, and recognition. The convolutional neural network (CNN) is one of the algorithms that relies on deep learning in its work. It has proven its effectiveness in classifying images with high efficiency in medical images and their diagnoses, face recognition, and other different fields. In this paper, the focus was on images to alert new researchers to their effects on the performance of CNN in terms of the number of classes that existed within the database, in addition to the impact of incorrect classification of images by the source on the classification result and the necessity of adopting reliable and correct sources of data to avoid inaccurate results. A group of face images has been used, and three experiments on them were conducted using all existing classes with reduction. The results showed a significant improvement in the performance of the algorithm whenever the number of classes was reduced. The best result was when only two classes were chosen for classification, reaching a validation accuracy of 85%.
Online Platform of Mathematical Terms in Karakalpak Language Allabay, Arziev; Jumabek, Seypullayev; Purxan, Nasirov; Begench, Geldibayev
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.6.2.205

Abstract

The paper is devoted to the creation of an online platform of mathematical terms in Karakalpak language and its importance for education, cultural heritage and development of scientific research in Karakalpakstan. The platform provides access to a rich base of mathematical terminology in the native language, which facilitates the learning and teaching of mathematics for Karakalpak students and teachers. It contributes to the preservation of cultural values, enriches linguistic resources and stimulates scientific cooperation in the field of mathematics. The creation of this platform emphasizes the importance of linguistic diversity and cultural identity in the context of scientific and educational progress.
Efficient Vehicle Registration Recognition System: Enhancing Accuracy and Power Efficiency through Digital Image Processing R. Madhumitha
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.6.2.206

Abstract

Number-plate recognition technology uses optical character recognition on images to read vehicle registration plates using OpenCV, PyTesseract OCR Engine, and YOLOv4 model. Images of the license plates are provided to the YOLOv4 model using a web app. The model is trained with the help of images from the Kaggle dataset. Then the number plate is extracted from the vehicle using the model. The string from the image is extracted using PyTesseract OCR Engine. Tested on several datasets this method gave us a success rate of 94%. This method can also be used in real-time incidents.
Modification in Strength Parameter (CBR) of Sub-Grade Soil with Addition of Fly Ash Jadhav , Abhijit; Nalawade , R D; Gaikwad , D P
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.6.2.207

Abstract

The determination of the California Bearing Ratio value of soil is tiresome, uneconomical, and time-consuming in the laboratory. Therefore, there is a required automation system to determine the California Bearing Ratio value of soil. Machine learning algorithms are being used for automation systems. In this paper, Artificial Neural Network has been proposed for the prediction of the California Bearing Ratio value of soil. Ash percentage, Liquid Limit, Plastic_Limit, Plasticity index, Shrinkage Limit, MDD and OMC parameters of soil affect the value of the California Bearing Ratio. In the laboratory, the training dataset has generated using these parameters of soil. The proposed classifier has been trained and tested using the training and testing dataset. Experimental results show that the proposed Artificial Neural Network is very accurate to predict California Bearing Ratio values of soil. It is also observed that the linear regression algorithm is very easy and useful to determine the value of the California Bearing ratio depending on seven attributes of soil. The rules generated by J48 and PART can be used to determine the California Bearing ratio. These models are very useful for civil engineers and civil constructors as a California Bearing ratio prediction automation system.
Development of Media Branding on Batik Products for MSME Actors in Kulon Progo Regency Istanti, Hanifah Nur; Kholifah, Nur; Putri, Gina Eka; Triyanto; Fitrihana, Noor
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.6.2.209

Abstract

Batik product marketing techniques are essential to the sustainability of an MSME in Kulon Progo. Various media can be used as a means of marketing. Based on previous research in 2021, the branding media used in the category is quite good, so there is a need to improve and develop the branding media used. This research aims to: (1) design the development of Sembung Batik branding media through an account on Shopee; and (2) Test the feasibility of developing Sembung Batik branding media through an account on Shopee. The research was carried out at Batik Sembung Kulon Progo. This type of research is development research using a 4D model and a quantitative descriptive approach. 4D Design comprises the definition, design, development, and dissemination stages. Three expert lecturers carried out the feasibility test. This research is to: (1). design the development of Sembung Batik branding media through an account on Shopee, which consists of four stages: the definition stage, the design stage, the development stage, and the disseminated stage, which is carried out 3 Shopee live times; and (2). the feasibility level of developing Sembung Batik branding media through an account on Shopee is very suitable for use based on the opinions of 3 experts. Based on this research, the development of branding media using live streaming can be implemented for other branding media so that it can have an impact on MSME branding.
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.

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