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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. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear
Signcryption with Matrix Modification of RSA Digital Signature Scheme and Cayley-Purser Algorithm Ginting, Cindy Laurent; Budiman, Mohammad Andri; Nasution, Sawaluddin
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): 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.v8.i1-12226

Abstract

The sender must ensure the security of messages and authenticated messages in messaging communications. Additionally, the sender must guarantee the message's integrity and cannot deny its authenticity or involvement with the message. This aspect is more robust because the recipient can verify, ensuring that the message originates from an authorized sender. In addition to this crucial aspect, the Signcryption method employing the Matrix Modification of RSA Digital Signature Scheme and the Cayley-Purser Algorithm can accomplish both of the objectives of this study. Encrypt-then-sign is the Signcryption method used, and the MD5 hash function performs one-way hashing during the signing procedure to enhance message security. This study tested the message plaintext in the form of a collection of strings consisting of uppercase (capital), lowercase (small), numbers (numeric), and other punctuation characters with varying numbers of characters in each string, as well as the value of modulus n from 10 digits up to its maximum length, which is unconstrained. The test results indicate that the time required for encryption and decryption is proportional to the number of plaintext characters used.
Daycare Recommendation System Using Fuzzy Logic Method and Haversine Formula (Case Study : Medan City) Br Sirait, Friska Pegrisentia; Hayatunnufus, Hayatunnufus; Hardi, Sri Melvani
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): 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.v8.i1-14354

Abstract

The problem currently faced is the lack of trust from some parents towards daycare due to the lack of detailed information about daycare and the lack of attractive promotional media for working parents to entrust their children. Therefore, a decision support system is needed that uses the fuzzy logic method and haversine formula to assist in decision making when choosing the best alternative in daycare selection. The criteria used in this study were price, distance, quantity of caregivers, quality of caregivers, and facilities and infrastructure. The results of this study indicate that the system calculations are in accordance with manual calculations and the results of system testing prove that this system percieved of usefulness it has an actual score of 93.69% (0.9369), in terms of percieved ease of use it has an actual score of 93.21% (0, 9321), in terms of attitude toward using it has an actual score of 92.68% (0.9368) and in terms of behavior in use it has an actual score of 91.19% (0.9119). This was obtained by distributing questionnaires to 28 users (parents) during system testing.
Fuzzy Approach For Determining Statistical Process Control (Spc) Tools Location On Production Floor Ishola, Christie Y.; Olabode, Adewoye S.
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): 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.v8.i1-17137

Abstract

Statistical Process Control (SPC) is a technical tool that is used to control and to improve almost any kind of process. However, because of cost consideration, management need to decide which process should apply SPC. In this paper, we propose the use of probability and fuzzy membership function to determine SPC allocation. Conditional probability is used to analyse process failure rate and process repair rate. Then, using Markov Matrix, we calculate the probability of out-of-control process (PO). Nevertheless, in a production line that consists of many parts, the probability value is not adequate to be used as a reference to determine SPC allocation. There are cases for instance, where the value of PO in one part does not mean the same as in other parts since each part may have different sensitivity degree to the final product. For example 0.25 of PO in part 1 may have higher influence to the final product compare to 0.25 of PO in part 2 or part 3. Furthermore, we cannot randomly choose one of those parts to apply SPC or even decide to apply SPC in all parts of the production line. To overcome this problem we propose fuzzy membership function that uses linguistic terms and degree of memberships to analyse PO instead of the probability values. By this mean, the SPC allocation could be determined without ambiguity. For this purpose, the membership function is classified into three categories, namely LOW, MEDIUM and HIGH. Any part with PO fall into the “HIGH” category and high degree of membership is prioritized to apply SPC.
A Review on Metaheuristic Approaches for Job-Shop Scheduling Problems Abdolrazzagh-Nezhad, Majid; Abdullah, Salwani
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): 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.v8.i1-17138

Abstract

Over the past several decades, interest in metaheuristic approaches to address job-shop scheduling problems (JSSPs) has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides a significant attention on reviewing state-of-the-art metaheuristic approaches that have been developed to solve JSSPs. These approaches are analysed with respect to three steps: (i) preprocessing, (ii) initialization procedures and (iii) improvement algorithms. Through this review, the paper highlights the gaps in the literature and potential avenues for further research.
Simulation of Vehicle Distance Detection for Traffic Order Baldemor, Milagros Racacho
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): 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.v8.i1-17139

Abstract

The use of ultrasonic sensors, especially HC-SR04, in microcontroller projects is expanding thanks to its ability to accurately detect distance. In this final project, the HC- SR04 is connected to an ESP32 to measure the distance of an object and provide feedback in the form of sound when the object approaches the sensor within a certain distance. The HC-SR04 sensor works by emitting ultrasonic waves and measuring the time it takes for the waves to reflect back to the sensor. The ESP32, as the microcontroller connected to the sensor, processes this time data to calculate the object's distance from the sensor. When the distance of the object is below a predetermined threshold, the ESP32 will activate the buzzer as a sound signal. This implementation can be applied in various systems, for this test we used it on the zebra crossing system automatically. The test results show that this system is able to detect distance with sufficient accuracy and provide a fast and consistent sound response according to changes in object distance.

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