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Journal of Computer Science and Engineering (JCSE)
Published by Institute of Computer Science and Engineering
ISSN : -     EISSN : 27210251     DOI : https://doi.org/10.36596/jcse
Core Subject : Science,
Computer Science & IT
Computer Architecture, Processor design, operating systems, high-performance computing, parallel processing, computer networks, embedded systems, theory of computation, design and analysis of algorithms, data structures and database systems, theory of computation, design and analysis of algorithms, data structures and database systems, artificial intelligence, machine learning, data science, Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 61 Documents
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Measuring Computational Psychometrics Analysis Motivational Level in Learner’s using Different Parameters through Deep Learning Algorithm Bhatia, Ashima Bhatnagar; Mittal, Kavita
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Learning is an ongoing process irrespective of age, gender and geographical location of acquiring new understanding, knowledge, behaviours, skills, values, attitudes, and preferences. Formative assessment methods have emerged and evolved to integrate learning, evaluation and education models. Not only is it critical to understand a learner's skills and how to improve and enhance them, but we also need to consider what the learner is doing; we need to consider navigational patterns. The extended learning and assessment system, a paradigm for doing research, captures this entire view of learning and evaluation systems. The function of computational psychometrics is to facilitating the translation from raw data to meaningful concepts. In this research study, several factors are considered for psychometric analysis of different kinds of learners, and based on a motivational level, many interesting conclusions have been drawn and presented in the result section at the end of the paper. Deep learning model Ludwig Classifier used to calculate, motivational Level is obtained for 100 number of epochs and it is found that the loss is decreasing and in other words, the accuracy of the machine goes on increasing. Each of the categories discussed here has new capabilities, or at the very least expansions of current ones.
Enhance Facial Biometric Template Security using Advance Encryption Standard with Least Significant Bit D, Gbolagade Morufat; Hambali, Moshood Abiola; Abdulganiyu, Oluwadamilare H.; Lawrence, E
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Security of data has been a major issue for many years which has lead to several challenges; loss of data and also allure hackers to where data are been stored. Biometric system to some extend has helped in combating these security issues, the major issues with the biometric system is how to properly secure the template been generated from hackers and unauthorized users. In this paper we combine cryptography and steganography to enhance security of biometric templates using the advance encryption standard (AES) and the least significant bit (LSB) technique to encrypt these templates and secure it from hackers, Cryptography and Steganography are the two widely employed techniques for securing and hiding data. The center of attention of this paper is the potency of combining cryptography and stenography techniques to improve the security of biometric templates.
Heart Disease Prediction Using Principal Component Analysis and Decision Tree Algorithm Hambali, Moshood Abiola; Gbolagade, Morufat Damola; Olasupo, Yinusa Ademola
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.617

Abstract

Globally, cardiovascular disease is among the major diseases that lead to death. Early forecasts are crucial. Using the patient's medical record, the supervised learning algorithm for predicting heart disease at an early stage was proposed. The principal component analysis (PCA) classifier and decision tree algorithm were created to classify medical record data. To predict cardiovascular diseases, data mining was utilized. The proposed strategy improves the diagnostic efficiency of physicians. Using data received from the UCI repository, the classifier's efficacy was confirmed. PCA offers 98% precision, 100% sensitivity, and 98% accuracy. In terms of accuracy, sensitivity, and precision, the results showed that the PCA outperformed the decision tree. 
A fault-tolerance model for Hadoop rack-aware resource management system Moses, Timothy; Abiodun, Oladunjoye John
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.651

Abstract

The central resource manager of Hadoop Yet Another Resource Manager (YARN) has posed a major concern to big data analysis and exploration. The central arbiter is overwhelmed whenever there are resource requests by application masters and heartbeat communication from several name nodes in the Hadoop cluster; thereby, degrading the performance of the framework. An attempt to decentralize the resource manager's responsibilities by introducing a new layer in the cluster named the Rack Unit Resource Manager (RU_RM) layer increased cluster performance but introduced a fault-tolerance concern. This work, therefore, developed a fault-tolerant model to allow for efficient and effective data analysis in the Hadoop cluster. A pseudo-distributed computation was set up with the help of the YARN Scheduler Load Simulator (SLS) and WordCount operation performed with varying input sizes. Two fault scenarios were presented and the results obtained showed that with an increase in input size (workload), the running time of the developed fault-tolerant model though slightly higher than that of the existing model, is significantly negligible when compared to the computation bottleneck incurred anytime RU_RM fails. The developed model, therefore, has good performance in the presence of failure of a unit (RU_RM) in the cluster.
Sistem Monitoring Mesin Pengaduk Adonan Roti Otomatis berbasis Internet of Things (IoT) Artono, Budi; Prakoso, Dimas Nur; Lestari, Tri Annisa Widya
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.660

Abstract

The purpose of this research is to develop an Internet of Things (IoT)-based automated bread dough mixing machine monitoring system using ultrasonic sensors and an integrated load cell sensor with a solenoid valve. The system is designed to control the distance between each bowl of dough ingredients and measure the weight of the mixed dough using a smartphone as the control interface. Additionally, the system allows users to issue on/off commands to the solenoid valve. This system provides detailed monitoring of the mixed blue dough ingredients. The research is conducted through several stages, including literature review, system design, hardware development, software development, implementation and integration, and testing. The testing phase involves both hardware and software components to ensure their functionality.
Pengembangan dan Pengujian Aplikasi Computer Based Test dengan Menggunakan Algoritme Fisher-Yates Shuffle sebagai Pengacakan Soal Abdullah, Naufal Fachry
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.695

Abstract

Many studies have confirmed that the application of information technology has helped humans to be more efficient in carrying out their activities. The daily repetition at the 5 Brebes State High School, using the conventional method, with the question of the written examination, it poses some challenges such as, teachers take quite a long time to correct the answers, since one teacher can teach up to 7 classes, another obstacle when examining is cheating by students during the examination. This research aims to develop and evaluate a Computer Based Test based on a Website, thus making it easier for teachers to correct students' answers, which are dual-choice, because the results of the correction will automatically exit the system and the application of the Fisher-Yates Shuffle algorithm will be able, minimizing the level of student fraud, because questions are spotted for each student. System development will use the Waterfall method with phases, analysis, design, program code making, testing. Based on the random permutation calculation of an existing question, when there are 15 questions but only show 10 questions, it results in permutations of 360.360 models, then the probability calculation that has been done yields 0,00832%, the chances of 30 students getting the same sequence of topics of 15 existing questions. Based on the results of the validation test, the comparison of the correction time for one class with the issue of double selection, which previously took 15 minutes, while using a Computer Based Test of 32 seconds. While the level of cheating is minimized, because the sequence of matters presented to each student will be different.
An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network Tahsin, Mohammad Sadman; Karim, Musaddiq Al; Ahmed, Minhaz Uddin; Tafannum, Faiza; Firoz, Neda
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

This paper investigates how the Fisher score feature selection approach can be used with capsule networks for diabetes detection. It also evaluates how well this algorithm works based on a number of evaluation parameters. The selected features using Fisher score method was then employed to train a capsule network model. Accuracy (94%), precision (94%0, recall (94%), F1 score (94%), and other performance evaluation metrics were thoroughly analyzed to determine the algorithm's efficacy. The results demonstrated that the combination of Fisher score feature selection and capsule networks yielded promising performance in diabetes detection. The selected features effectively captured the relevant information necessary for accurate classification The capsule network model was very accurate, which shows that it could be a good tool for diagnosing diabetes. Also, the accuracy and recall values showed that the algorithm could correctly place both positive and negative cases of diabetes, minimizing the risk of misdiagnosis. By merging the Fisher score feature selection approach with capsule networks, this research study contributes to advancing diabetes detection.
Enhancing Privacy in Graph Algorithms: Data-Oblivious Approaches to DFS and Dijkstra's Algorithm CH, Koteswararao; Singh, Kunwar; Kumar, Anoop
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Data obliviousness is characterized by a consistent sequence of operations irrespective of input data and data-independent memory accesses, making it a suitable solution for users utilizing outsourced storage data services who aim to conceal their data access patterns. In ACM SIGSAC 2013, data-oblivious algorithms were introduced for breadth-first search, single-source single-destination (SSSD) algorithms, maximum flow, and minimum spanning tree. In this study, we present novel data-oblivious algorithms designed for depth-first search and single-source shortest path (Dijkstra’s algorithm). Our proposed data-oblivious algorithms demonstrate efficiency comparable to non-data-oblivious counterparts, particularly for graphs containing fewer than 1000 nodes. This research contributes to advancing data privacy in outsourced storage services by providing effective data-oblivious solutions for common graph algorithms.
TikTok Shop: Unveiling the Evolution from Social Media to Social Commerce and its Computational Impact on Digital Marketing Nur, Zinda Rud Faiza; Rabbiana, Intan Nas Nas; Diba, Tiara; Fitroh, Fitroh
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Social media application platforms such as TikTok have become social commerce platforms. TikTok provides the TikTok Shop feature, which is designed for business actors to make sales and users to make transactions on the TikTok application. This study used the method of studying literature from published journals available on open-source sites. It aims to present the potential of TikTok Shop as a digital marketing medium in the future. Therefore, the discussion of this literature review is only focused on the TikTok Shop feature as social commerce. TikTok Shop promoted efforts to provide improvements for MSMEs during the pandemic through an SEO marketing strategy and influencers providing interesting content according to the interests of the audience. TikTok also provides Live TikTok for business people, which is in the form of streaming video that can interact with users as potential buyers. Our research found that TikTok Shop can be one of the platforms with great potential for promoting products, supported by several TikTok Shop features for transactions.
Analisa Penerimaan Pengguna e-wallet Sebagai Transaksi Digital Menggunakan Metode TAM (Technologi Acceptance Model) Siahaan, Heppy Rosalina; Budihartanti, Cahyani
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.28

Abstract

As technology advances and digital transactions continue to grow, many companies are now launching digital or cashless transaction applications. This allows users to be more selective in choosing a digital or cashless transaction application, also known as an e-wallet, that they want to use. Factors considered in this selection process include ease of use, offered features, transaction speed, security, and the number of merchants available. This research aims to provide users with information to choose a high-demand e-wallet by implement the Technology Acceptance Model (TAM) to calculate the selection of a high-demand digital wallet. Based on the data calculated using the TAM method and survey results that include appearance criteria, offered items, convenience, security, and the number of merchants, GO-PAY is considered the primary alternative for a digital wallet with a demand rate of 47.8%. OVO comes in second place with 31.1%, followed by DANA in the last position with 21.1%

Page 4 of 7 | Total Record : 61

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