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Ainul Hizriadi, S.Kom., M.Sc.
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Data Science: Journal of Computing and Applied Informatics
Published by Universitas Sumatera Utara
ISSN : 25806769     EISSN : 2580829X     DOI : -
Core Subject : Science,
Computer Science & IT
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
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Articles 5 Documents
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Search results for , issue "Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear
Bayesian Regression for Predicting Price Empirical Evidence in American Real Estate Patria, Harry
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i1-10082

Abstract

The two foremost aims of classical regression are to assess the structure and magnitude of the relationship between variables. Despite the aforementioned benefits, unlike classical regression, which only offers a point estimate and a confidence interval, Bayesian regression offers the whole spectrum of inferential solutions. The results of this study demonstrate the Bayesian approach's suitability for regression tasks and its advantage in accounting for additional a priori data, which often strengthens studies. Using data from Boston Housing provided by from UCI ML Repository, this study proves that the prior distributions have the benefit of producing analytical, closed-form conclusions, which eliminates the need to use numerical techniques like Markov Chain Monte Carlo (MCMC). Second, software implementations are offered together with formulas for the posterior outcomes that are supplied, clarified, and shown. The assumptions supporting the suggested approach are evaluated in the third step using Bayesian tools. Prior elicitation, posterior calculation, and robustness to prior uncertainty and model sufficiency are the three processes that are essential to Bayesian inference.
Supporting Clinical Decision Making: Semantics Based Classification of Medical Referral Letters Jones, Laurence R; Wilson, Ian
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i1-10222

Abstract

This study aims to develop a Natural Language Processing based decision support system built from a repository of knowledge drawn from referral letters written between primary care doctors and specialist medical consultants. The developed system translates pre-processed referral letters into a semantic matrix of document vectors and a set of vocabulary features, based solely on the words used within each referral letter. The system applies a one-versus-rest heuristic using a Support Vector Machine (SVM) to convert a multinomial classification problem into individual binary classifications. Each document is matched to its probabilistic best fit specialism. The National Health Service Wales sourced 111,700 examples. Accuracy of 91.8% against 29 medical specialities is achieved. Accuracy increases to 97.4% and 99%, respectively, when also including one or two nearest neighbours to the best fit, providing a basis for informing the decision making of a medical professional. The study demonstrates the efficacy of using referral letters to allow or classification into specialisms and subsequent allocation of specialist care. The approach taken in this study does not require added ontologies and is readily extendable. The system offers support to medical professionals, particularly within training scenarios or where access to opinion may be in short supply.
Analysis of Embedding Locations in the Subband Frequency DCT on Scanned Images Hidayati, Indri; Budiman, Mohammad Andri; Zarlis, Muhammad
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i1-10359

Abstract

Uploading an identity card as an image for the account verification process or transactions online can be a threat to application users. Identity card theft can be carried out by irresponsible persons if the application can be hacked. Therefore, protection of the image is required for authentication. In this study, the proposed technique is watermarking. A watermark in the form of a binary image will be embedded into the image as ownership using a Discrete Cosine Transform. The Discrete Cosine Transform works in the frequency domain. The location of the embedding of different watermarks was analysed in each 8×8 DCT block. The results of the analysis to assess the imperceptibility of original images and watermarked images using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Measure), while assessing the watermark robustness embedded using NCC (Normalized Cross Correlation). The results show PSNR (Peak Signal to Noise Ratio) ≥ 54 dB with a watermark strength of 0,1 and an average SSIM (Structural Similarity Index Measure) ≥ 0,9 on 4 scanned images in BMP format with a resolution of 100 DPI. A good watermark embedding is done on the green component at middle frequencies to maintain a balance between imperceptibility and robustness. In contrast, the red component at low frequency is vulnerable to attacks in the form of brightness +20 and contrast +50 with an average NCC (Normalized Cross Correlation) ≤ 0,85.
Comparative Analysis of Ciphertext Enlargement on Generalization of the ElGamal and Multi-factor RSA Zega, Imanuel; Mohammad Andri Budiman; Syahril Efendi
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i1-10360

Abstract

Information and communication security has become more crucial and has become a new problem in relation to security, accessibility, data management, and other information policy challenges as a result of how easy it is for all users to use communication media. One of the fields of science that has a technique or art for disguising the data sent by the sender to the recipient with the aim of maintaining the confidentiality of the data is called cryptography. In determining better cryptographic algorithms for data security systems, in addition to considering strength, key length and ciphertext enlargement are also important factors to consider. Therefore, in this study, we attempted to compare the ciphertext magnification of the generalization of the ElGamal and multi-factor RSA algorithms by utilizing the same key length. Generalization of the ElGamal and Multi-factor RSA are both asymmetric algorithms that have public and private key pairs for encryption and decryption. However, at the level of security, the RSA algorithm is based on the difficulty of finding large integer factors into two prime factors. In contrast to the ElGamal algorithm, security is based on the difficulty of calculating the discrete logarithm of a large prime modulus. The results of the comparison algorithm carried out are represented in the form of a table containing the plaintext, key length, and size of the data.
Performance Analysis of Hybrid Cryptographic Algorithms Rabbit Stream and Enhanced Dual RSA Sarumaha, Demonius; Mohammad Andri Budiman; Muhammad Zarlis
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i1-10483

Abstract

Cryptography is a technique for encoding data by encrypting plaintext into an unreadable (meaningless) form. Cryptographic methods have good and bad performance depending on the type of algorithm we use. Therefore, the purpose of this study is to measure speed by combining the two algorithms used. The Rabbit Stream algorithm is a stream cipher algorithm whose system security depends on the generation of a key bit stream (keystream), which only guarantees 128-bit key security but has the advantage of being fast in the encryption and decryption process, while the Enhanced Dual RSA algorithm is an asymmetric algorithm to increase data protection from the Dual RSA algorithm by utilizing the Pells equation as a substitute for public key exponents. On the other hand, the algorithm in question requires a significant amount of time to encrypt messages with a large capacity when compared to the Rabbit Stream algorithm. Nonetheless, the study's findings suggest that using a hybrid method is comparatively faster for processing substantial amounts of data.

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