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All Journal Scientific Journal of Informatics Journal of Soft Computing Exploration
Budi Prasetiyo
Department of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT

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News text classification using Long-Term Short Memory (LSTM) algorithm Indra Triyadi; Budi Prasetiyo; Tiara Lailatul Nikmah
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.136

Abstract

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
S-box Construction on AES Algorithm using Affine Matrix Modification to Improve Image Encryption Security Alamsyah Alamsyah; Budi Prasetiyo; Yusuf Muhammad
Scientific Journal of Informatics Vol 10, No 2 (2023): May 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i2.42305

Abstract

Abstract.Purpose: In this study, the AES algorithm was improved by constructing the S-box using a modified affine matrix and implementing it so that there was an increase in security in image encryption.Methods: The method used in this study starts from selecting the best irreducible polynomial based on previous studies. The irreducible polynomial chosen is . With this irreducible polynomial, an inverse multiplicative matrix is formed. The formed inverse mutiplicative matrix is implemented in the affine transformation process using the best 3 affine matrices based on previous research and 8-bit additional constants using AES S-box. This formulation produces 3 different S-boxes, i.e., S-box1, S-box2, and S-box3. Finally, the resulting S-boxes are implemented to carry out the image encryption process and are tested for their security level.Result: The test results show an increase in image encryption security compared to previous studies. The increase in security occurred at the entropy value of 7.9994 and the NPCR value of 99.6288%.Novelty: The novelty of this paper is the improvement of the S-box construction which is implemented in image encryption resulting in increased security in image encryption.
Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction Rini Muzayanah; Apri Dwi Lestari; Jumanto Jumanto; Budi Prasetiyo; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.50274

Abstract

Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem of data imbalance in data lending on peer-to-peer landing platforms.Methods: This study uses the XGBoost classification algorithm to evaluate and compare the resampling techniquesused. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, andRandom Oversampling.Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Lineresampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTEresampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by acombination of the XGBoost model with the SMOTE resampling technique.Novelty: It is hoped that from this research we can find the most suitable resampling technique combined with theXGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lendingplatforms so that the sorting algorithm can work optimally and produce optimal accuracy.
Co-Authors Alamsyah Alamsyah Apri Dwi Lestari Dwika Ananda Agustina Pertiwi Indra Triyadi Jumanto Unjung Much Aziz Muslim Rini Muzayanah Tiara Lailatul Nikmah Yusuf Muhammad