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Rizki Wahyudi
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INDONESIA
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 41 Documents
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Analysis of Tomato Leaf Disease Identification Techniques Gaurav Chopra; Pawan Whig
Journal of Computer Science and Engineering (JCSE) Vol 2, No 2: August (2021)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v2i2.171

Abstract

India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not been displayed by any computer vision method. Under different conditions, the accuracy of the plant identification system is much lower than expected by algorithms. This study performs analysis on features learned by the backpropagation algorithm and studies the state-of-the-art results achieved by image-based classification methods. The analysis is shown through gradient-based visualization methods. In our analysis, the most descriptive approach to generated attention maps is Grad-CAM. Moreover, it is also shown that using a different learning algorithm than backpropagation is also possible to achieve comparable accuracy to that of deep learning models. Hence, state-of-the-art results might show that Convolutional Neural Network achieves human comparable accuracy in tomato leaf disease classification through supervised learning. But, both genetic algorithms and semi-supervised models hold the potential to built precise systems for tomato leaf detection.
A one step further approach to fraud detection Debjyoti Bagchi; Abhishek Mukherjee; Sarannak Pal
Journal of Computer Science and Engineering (JCSE) Vol 2, No 2: August (2021)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v2i2.250

Abstract

This paper will discuss about the different approaches to fraud detection such as Data mining, machine learning and artificial intelligence and statistical data analysis. Then we list some of the technical and troublesome challenges to modern fraud detection techniques. A comparison study of these techniques is also done according to the metrics like precision, False Alarm Rate (FAR), Accuracy, Cost, True Positive Rate (TPR) against different categories of frauds such as internal bank fraud, credit card fraud, loan fraud. Finally, we discuss the disadvantages of the existing fraud detection systems and we attempt to recommend a specific technique or algorithm for detecting a specific type of fraud with their advantages and disadvantages.
Prediction of Loan Behaviour with Machine Learning Models for Secure Banking Mayank Anand; Arun Velu; Pawan Whig
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.237

Abstract

Given loan default prediction has such a large impact on earnings, it is one of the most influential factor on credit score that banks and other financial organisations face. There have been several traditional methods for mining information about a loan application and some new machine learning methods of which, most of these methods appear to be failing, as the number of defaults in loans has increased. For loan default prediction, a variety of techniques such as Multiple Logistic Regression, Decision Tree, Random Forests, Gaussian Naive Bayes, Support Vector Machines, and other ensemble methods are presented in this research work. The prediction is based on loan data from multiple internet sources such as Kaggle, as well as data sets from the applicant's loan application. Significant evaluation measures including Confusion Matrix, Accuracy, Recall, Precision, F1- Score, ROC analysis area and Feature Importance has been calculated and shown in the results section. It is found that Extra Trees Classifier and Random Forest has highest Accuracy of using predictive modelling, this research concludes effectual results for loan credit disapproval on vulnerable consumers from a large number of loan applications
Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time Adi Winanto; Cahyani Budihartanti
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.249

Abstract

Currently, the Government is intensively utilizing social media, one of which is Twitter as a place of interaction with the community. The results of these interactions can be used as feedback to determine whether public opinion on public policies is positive or negative. Tweets from users can be a supporting parameter for the government in evaluating future policies and decision making by applying the sentiment analysis method. This study aims to determine positive or negative sentiments on user tweets against the official twitter account of the DKI Jakarta Provincial Government during the COVID19 vaccine period. The data obtained are 1658 lines from March 30 to April 5, 2021 with queries on tweets containing words or mentioning the username @dkijakarta, which will be grouped by sentiment class, namely negative and positive using the TF-IDF Vectorizer for word weighting and classification using several methods, namely, nave Bayes with accuracy values. 82.50% with class recall on positive sentiment 88% and negative 77% and in class precision showing positive at 79.28% and negative at 86.52% in the rapid miner application then k-NN with an accuracy value of 81.50% with class recall on positive sentiment 85% and negative 78% and class precision shows positive at 79.44% and negative at 83.87% in the rapid miner application. And the accuracy value of the best method in this training data classification comparison is nave Bayes, the results the end of testing the sample dataset using the nave Bayes method with 84.80% accuracy with class recall at 85.01% positive sentiment and 84.59% negative sentiment and at c lass precision shows positive at 85.21% and negative at 84.38% in rapid mining applications.
Pengunaan Metode Scrum Dalam Pengembangan Perangkat Lunak: Literature Review Muhamad Rizky; Yuni Sugiarti
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.353

Abstract

Abstrack: The use of scrum in software engineering as a system development method is considered effective and quite easy to apply in application development. Many Start-up industries as well as IT companies use this method. In software development, Scrum has an iterative and incremental nature that makes this company able to continue to compete in improving its products in the market. Scrum itself is a method that is not rigid, but a framework whose application can be used in various tools and techniques. The purpose of this research is to find out how to use the scrum method in software development and to find out how to deal with the problem of using scrum which has a complex process. The methodology used in this research is literature study. The results of this study are the use of Scrum in software development can be utilized according to the need to both help and solve existing problems in the framework of software development. In solving problems that exist in Scrum related to such a complex process, the step that needs to be done is to identify the cause of the problem. Which is then continued by discussing the right solution with the team in order to find a way out of every problem that exists in each process.Abstrak: Penggunaan scrum dalam rekayasa perangkat lunak sebagai metode pengembangan sistem dianggap efektif dan cukup mudah diterapkan dalam pengembangan aplikasi. Banyak industri Star-up maupun juga perusahaan TI menggunakan metode ini. Dalam pengembangan perangkat lunak Scrum ini memiliki sifat yang iterative dan juga incremental yang menjadikan perusahaan ini bisa terus bersaing dalam meningkatkan produknya dipasaran. Scrum sendiri merupakan metode yang tidak kaku, melainkan framework yang penerapannya ini bisa digunakan dalam berbagai tools maupun juga teknik.  Tujuan penelitian ini adalah untuk mengetahui bagaimana penggunaan metode scrum dalam pengembangan perangkat lunak dan mencari bagaimana langkah menangani permasalahan penggunaan scrum yang memiliki proses yang kompleks. Metodologi  yang digunakan  dalam penelitian ini adalah studi literatur. Hasil penelitian ini adalah penggunan scrum dalam pengembangan perangkat lunak dapat dimanfaatkan  sesuai dengan kebutuhan baik membantu maupun menyelesaikan permasalahan yang ada dalam rangka pengembangan perangkat lunak. Dalam menyelesaikan permasalahan yang ada pada scrum terkait proses yang begitu kompleks, langkah yang perlu dilakukan adalah dengan mengidentifikasi penyebab masalah. Yang kemudian dilanjutkan dengan mendiskusikan solusi yang tepat dengan tim agar mencari jalan keluar dari setiap permasalahan yang ada dalam setiap prosesnya.
Usulan Perencanaan Enterprise Architecture Aplikasi Flip.id Menggunakan TOGAF ADM Muhamad Rizky; Faaza Bil Amri; Ani Rosidah; Nanda Putri Styaningrum; Fitroh Fitroh
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.367

Abstract

ABSTRACT: Financial Technology is used as a tool to accelerate and facilitate financial services arising from innovations in the financial services industry. One of the fintechs in the field of fund transfer services that is well known to many people is Flip.id. The problems that often occur in the process of interbank money transfer transactions are a consideration for users to use the Flip.id application or prefer to use other platforms. The problems that occur with Flip.id are the refund system, unsatisfactory service, and speed in transactions. This can be born from a bad corporate architecture. The purpose of this study is to provide suggestions about enterprise architecture planning on the Flip.id application. The method used in problem solving is using the TOGAF ADM framework based on previous research literature studies. This research will produce an IT Blueprint for users to feel comfortable and have loyalty for future evaluations of the Flip.id company.ABSTRAK: Financial Technology digunakan sebagai alat untuk mempercepat dan memudahkan pelayanan keuangan yang timbul akibat inovasi pada industri jasa keuangan. Salah satu fintech dalam bidang jasa transfer dana yang sudah dikenal banyak orang adalah Flip.id. Permasalahan yang banyak terjadi pada proses transaksi pengiriman uang antarbank menjadi pertimbangan untuk para pengguna akan menggunakan aplikasi Flip.id atau lebih memilih menggunakan platform lain. Masalah yang terjadi pada Flip.id yakni sistem pengembalian dana, pelayanan yang kurang memuaskan, serta kecepatan dalam transaksi. Hal tersebut bisa lahir dari arsitektur perusahaan yang kurang baik. Tujuan dari penelitian ini untuk memberikan usulan tentang perencanaan enterprise architecture pada aplikasi Flip.id. Metode yang digunakan dalam pemecahan masalah yakni menggunakan framework TOGAF ADM berdasarkan studi literatur penelitian sebelumnya.  Penelitian ini akan menghasilkan IT Blueprint untuk para pengguna agar mendapatkan kenyamanan dan memiliki loyalitas untuk evaluasi perusahaan Flip.id kedepannya.
Performance study of the Memory Utilization of an Improved Pattern Matching Algorithm using Bit-Parallelism John Abiodun Oladunjoye; Moses Timothy; Okpor James; Baku Agyo Raphael
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.460

Abstract

The strategy of packing several data values in a single computer word and refreshing them all in a solitary operation is referred to bit parallelism. It assumes a significant part in pattern matching because it can handle in parallel the length of pattern sizes. In this paper, an Improved Pattern Matching model (IPM) proposed, which makes searching process quicker and decreases how much memory used in processing input data. C Sharp was used for the development of the model. With a computer word size of 64bits and pattern length ranging from 8 characters to 72 characters, the system decides how much memory is used. The developed model was evaluated and contrasted with the existing model using 64bits computer word size (cws) and the pattern length of 72 characters. The assessment showed that the IPM had minimal worth of MU contrasted with the existing model (BNDM, SBNDM, and FSBNDM). This IPM model can be embraced for improvement of the size of string data stored in computer word because of its capacity to diminish memory space usage.
Perkembangan Evaluasi Tata Kelola Teknologi Informasi: Literature Review Shafa Salsabila Khansa; Della Novia Ramadhan; Ahmad Fadil Alfarisy; Fitroh Fitroh
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i2.521

Abstract

Information technology is an important organizational asset to be managed and maximized so that its use can be aligned with business needs, thus helping to improve the achievement of the organization's vision and mission. Therefore, the implementation of information technology governance in every organization needs to be monitored for its implementation, this makes the organization more mature and immediately makes improvements to its management of its IT assets. Various studies have been conducted to evaluate IT governance in organizations using various frameworks. This study aims to conduct a review of previous studies related to the development of IT governance evaluation in reviewing research activities carried out in a certain period, analyzing the framework used and why the framework is used. The results show that research to evaluate IT governance peaked in 2014 which was dominated by the institutional sector using the COBIT 5 framework and the various reasons for using COBIT 5 are discussed further in this study.
Big Data Indexing: Taxonomy, Performance Evaluation, Challenges and Research Opportunities Abubakar Usman Othman; Timothy Moses; Umar Yahaya Aisha; Abdulsalam Ya’u Gital; Boukari Souley; Badmos Tajudeen Adeleke
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i2.548

Abstract

In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.
Novel approach of Predicting Human Sentiment using Deep Learning Ebtesam Shadadi; Shama Kouser; Latifah Alamer; Pawan Whig
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i2.533

Abstract

Due to its interactive and real-time character, gathering public opinion through the analysis of massive social data has garnered considerable attention. Recent research have used sentiment analysis and social media to do this in order to follow major events by monitoring people's behavior. In this article, we provide a flexible approach to sentiment analysis that instantly pulls user opinions from social media postings and evaluates them. As time passed, an increasing number of people shared their opinions on social media. More individuals can now communicate with one another as a result. Along with these advantages, it also has certain drawbacks that cause resentment in some people. Hate speech is another possibility. Hate speech impacts the community when it contains insulting or threatening language. Before it spreads, this kind of speech has to be identified and deleted from social media platforms. The process of determining whether a text's feelings reflect hatred or not involves sentiment analysis. Python language was used to analyze the Twitter dataset. There were 5000 Tweets in total in this dataset, and we used deep learning to improve the machine learning model's accuracy. The experimental outcome in both cases of the Twitter dataset uses the Random Forest approach, which has a 99 percent accuracy rate.

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