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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2023): July: Computer Science and Research" : 6 Documents clear
Deep Learning for Ensuring Food Security in Agriculture: An In-Depth Exploration of Innovations and Challenges Shapiro, Rudakova Brown; Cashore, Levin Zhao; Yuan, Wang Shaikh
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.18

Abstract

Ensuring food security in agriculture has become an increasingly critical challenge amid a growing global population and changing climatic conditions. Deep Learning, a subset of artificial intelligence, has emerged as a promising technology to address these pressing issues in agriculture. This research presents a comprehensive exploration of the potential of Deep Learning in revolutionizing agricultural practices to enhance food security. The study delves into various applications, including crop yield prediction, pest detection and control, crop disease diagnosis, and precision agriculture. A Convolutional Neural Network (CNN) based model is proposed as an example to showcase the transformative power of Deep Learning in crop disease diagnosis. The research discusses the innovations, challenges, and opportunities of integrating Deep Learning algorithms into agricultural systems. Data availability, computational resources, and model interpretability emerged as key challenges. Despite the hurdles, the research highlights the significant potential of Deep Learning to improve food security through increased agricultural productivity, resource optimization, and sustainable farming practices. Policy recommendations and public-private partnerships are proposed to facilitate the adoption of Deep Learning solutions in agriculture. By understanding the innovations and challenges, this research contributes to the ongoing efforts to ensure sustainable food production and meet the demands of the future.
Hybrid Grid Partition and Rought Set Methods for Generating Fuzzy Rules in Supply Chain Marsoit, Patrisius Michaud Felix; Pernadate, Park Vrançoisee; Jérôme, Jesca Fell
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.20

Abstract

Supply chain management in today's dynamic and complex business environment demands innovative approaches to decision support. This research introduces a novel hybrid framework that combines grid partition, rough set methods, and fuzzy logic to generate adaptive fuzzy rules tailored to supply chain data. By integrating these techniques, the study provides a comprehensive decision support system capable of addressing the intricacies and uncertainties prevalent in supply chain operations. A numerical example illustrates the practical application of this framework in optimizing inventory management within an e-commerce supply chain. The results showcase the effectiveness of the adaptive fuzzy rules in minimizing stockouts, reducing excess inventory, and optimizing inventory costs. Additionally, the study emphasizes the importance of balancing rule quality and complexity using a tunable parameter, offering flexibility for rule customization. The interpretability of the generated fuzzy rules further enhances their practical utility, enabling domain experts to comprehend and adjust decision criteria. This research not only contributes to advancing decision support systems in supply chain management but also lays the groundwork for future exploration of real-world data integration, adaptability to dynamic environments, and scalability challenges, thus promising significant enhancements in supply chain performance and resilience.
Optimizing Multi-Objective Flexible Job-Shop Scheduling Using Hybrid Bat Algorithm and Simulated Annealing Lee, See Cheng; Lee, Jian-Cheng; Jérôme, Jesca Fell
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.22

Abstract

This research investigates the application of a Hybrid Bat Algorithm (BA) and Simulated Annealing (SA) approach to solve the Multi-Objective Flexible Job-Shop Scheduling Problem (MOFJSSP) within contemporary manufacturing settings. MOFJSSP embodies the complexities of scheduling in modern industries, encompassing multiple conflicting objectives such as minimizing makespan, reducing idle time, optimizing machine utilization, and minimizing production costs. Traditional approaches often struggle to address these complexities adequately. To confront these challenges, a hybrid algorithm integrating BA and SA is proposed, leveraging their respective strengths in exploration and exploitation of solution spaces. The methodology involves problem formulation, solution representation, parameter settings, initialization strategies, iterative evolution mechanisms, and comprehensive evaluation. Experimental results showcase the hybrid approach's superior convergence rates, solution quality, and robustness in comparison to individual algorithms and state-of-the-art methods. The implications suggest potential applications in optimizing manufacturing scheduling, logistics, and diverse industries. Moreover, the research paves the way for future exploration into hybridization with emerging techniques, integration with Industry 4.0 technologies, and adaptation to dynamic manufacturing environments. Embracing these findings promises enhanced operational efficiency, informed decision-making, and continuous innovation in manufacturing scheduling practices.
Decision Support System Determines the Best Employees at PT Mahkota Group Tbk Arrahman, Hamzah; Jannah, Miftahul; Akbar , Azi Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.23

Abstract

This research discusses the development and implementation of a decision support system (DSS) to determine the best employees at PT Mahkota Group Tbk. The main objective of this research is to increase efficiency and objectivity in the decision-making process related to employee performance assessment. The research methodology involves collecting employee performance data, analyzing company needs, and implementing appropriate decision-making models. The SPK developed uses artificial intelligence techniques to process and analyze employee performance data, provide scores, and ultimately determine the best employees based on established criteria. The research results show that the implementation of SPK is able to increase objectivity in assessing employee performance and provide effective support for the decision-making process. With this system, it is hoped that the company can identify and utilize employee potential more optimally, increase productivity, and strengthen the competitiveness of PT Mahkota Group Tbk in the market. This data will be processed and assessed by a system developed using the Simple Additive Weighting (SAW) method. The results of the performance assessment will be presented in the form of ratings and grades for each employee, making it easier for related parties to make a more precise and transparent decision-making process. It is hoped that the results of this research can make a positive contribution to the efficiency and effectiveness of human resource management
Analysis and design of intelligence test applications (psychotest) for job applications Pasaribu, Sutrisno Arianto; Pasaribu, Victor Patar; Hirzi , M Fakrul; Arahman, Hamzah; Halawa , Jerisekiawan
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.24

Abstract

In this era of globalization, increasingly fierce competition in the world of work encourages companies to choose employees who not only have high academic qualifications, but also intelligence that matches job demands. Therefore, this research aims to develop and analyze the application of Intelligence Tests (Psychotest) as an effective job application assessment tool. System analysis and design methods are used to design applications that can measure various aspects of intelligence, including verbal, numerical and spatial intelligence. The analysis stage involves an in-depth understanding of the company's needs in assessing applicant intelligence. Meanwhile, the design stage includes the design of an intuitive user interface, an efficient database structure, and a valid and reliable intelligence measurement algorithm. This application is designed to be accessed online so that applicants can take intelligence tests anytime and anywhere. By using the latest security technology, applicant data security is guaranteed during the testing process. In addition, this application will provide test results automatically and present reports that are clear and easy to understand for companies. Through the application of the Test Intelligence application, it is hoped that companies can identify prospective employees who not only have technical qualifications, but also intelligence that matches the characteristics of the job being offered. Thus, this application is expected to make a positive contribution in increasing the efficiency and effectiveness of the recruitment process, ensuring appropriate employee selection, and ultimately improving company performance and productivity.
Performance sensor analysis of HC-SR04 proximity sensor on distance measuring device with fuzzy logic method Hirzii, M. Fakhrul; Pasaribu, Sutrisno Arianto; Sabila, Puji Chairu; Parlindungan, Mhd. Reivan
Journal of Computer Science and Research (JoCoSiR) Vol. 1 No. 3 (2023): July: Computer Science and Research
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v1i3.25

Abstract

The HC-Sr04 ultrasonic sensor (proximity sensor) is a widely used ultrasonic sensor, in addition to its affordable price also because of its easy use and easy installation. Ultrasonic sensors are electronic devices whose ability can convert from electrical energy into mechanical energy in the form of ultrasonic sound waves. HC-SR04 sensor is one of the ultrasonic sensors that is often used to monitor the distance of objects (objects) with sensors. This sensor consists of a series of ultrasonic transmitters called transmitters and ultrasonic receivers called receivers. The distance that can be handled ranges from 2 cm to 400 cm, with a precision level of 0.3 cm. The detection angle that can be handled is no more than 15°. The required current is not more than 2mA and the required voltage is +5V. The number of pins is 4. In this study, the authors used the fuzzy logic method to help classify the level of distance received by the sensor. Fuzzy logic is used because this method is able to determine the classification results of the received values, according to what is needed by the distance measuring device with this ultrasonic sensor.

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