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Media of Computer Science
Published by CV. Digital Innovation
ISSN : 30634822     EISSN : 30634997     DOI : https://doi.org/10.69616/mcs
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science
Media of Computer Science (MCS), a two times annually provides a forum for the full range of scholarly study . MCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol. 1 No. 1 (2024): June 2024" : 6 Documents clear
Expert System for Determining Diseases and Pests in Seaweed Using Forward Chaining (Case Study : Watorumbe Village, Mawasangka Tengah) Asriani, Ika; Muchtar, Mutmainnah; Ismail, Rima Ruktiari; Paliling, Alders; Sya'ban, Kharis; Karim, Rahmat
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.175

Abstract

Seaweed is a marine organism that plays a crucial role in both ecosystem and economy. However, it often faces attacks from diseases and pests that can jeopardize the productivity and sustainability of the seaweed industry. Hence, the development of an expert system to diagnose seaweed diseases and pests becomes imperative. This research aims to develop an Expert System for Determining Diseases and Pests in Seaweed using the Forward Chaining method, with a case study conducted in the Watorumbe Village, Mawasangka Tengah Sub-district, Southeast Sulawesi. The Forward Chaining method is employed to identify symptoms appearing in seaweed and determine potential diseases or pests. Testing is carried out with 30 data samples compared against expert diagnoses, resulting in an accuracy rate of 90%. Therefore, this system has the potential to assist seaweed farmers in diagnosing diseases and pests more quickly and accurately, thereby enhancing the productivity and sustainability of seaweed cultivation efforts.
Chicken Cage Incubator Cooling Control System Using Fuzzy Logic Agni Dhewa, Oktaf; Aldiasa Pangestu Aji
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.177

Abstract

This research examines the impact of prolonged cooling time in the incubator of chicken coops on the well-being of chicks during the incubation process. Poorly maintained temperature conditions can jeopardize the health of chicks and diminish the overall productivity of poultry farming. To address this issue, the study develops an efficient temperature control system by integrating a peltier as the main cooling component and fuzzy logic control combined with a DC fan. Experimental results indicate that the developed system is capable of providing a robust response to temperature changes in the chicken coop incubator, with transient response values meeting the expected criteria. Consequently, this temperature control system holds the potential to enhance the performance of chicken coop incubators and reduce the risk of chick mortality during the incubation process. This research serves as an initial step in advancing better temperature control for chicken coop incubators in the future.
Comparative Analysis of Google Vision OCR with Tesseract on Newspaper Text Recognition Prakisya, Nurcahya Pradana Taufik; Kusmanto, Bintang Timur; Hatta, Puspanda
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.178

Abstract

Optical Character Recognition (OCR) is a technique used to convert image files into machine-readable text. There are two Optical Character Recognition (OCR) algorithms that are currently well known and widely used, namely Google Vision's Optical Character Recognition (OCR) and Tesseract. The purpose of this study is to compare the Optical Character Recognition (OCR) algorithms of Google Vision and Tesseract so that people can more easily find out which algorithm is the right one to implement on the system they are going to build. The method used in this research is Research and Development (R&D) with the stages of literature study, needs analysis, dataset collection and expansion, architectural design development and application modeling, system implementation, testing and evaluation, drawing conclusions. To be able to determine the level of accuracy, precision and sensitivity of each algorithm, this research uses the Confusion Matrix formula. The results of this study conclude that Google Vision's Optical Character Recognition (OCR) algorithm is superior to Tesseract because the level of accuracy, sensitivity, and precision is superior to Google Vision.
Wi-Fi Optimization with Wireless Mesh Networks Arya, Primadona; Febriyan Priambodo, Dimas
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.179

Abstract

Wifi is one technology that can still penetrate into several services around us. The need for additional services in the form of our wifi trigger to provide solutions to improve quality, security and even coverage. By using an ordinary single access point, the wifi performance is not optimal because of the interference between signals and the strength or in between with wifi repeater. Access point need more power to extend range. With a mesh network, the performance of the access point can be maximized. This research shows that mesh implementation can increase from -20dB bellows to above -50dB or more than 100%. With mesh network wifi can be seen in single SSID different with repeater that has own coverage and setting. Mesh network connecting all client seamless to near node in single network SSID.
Identification of Fatigue from Facial Expressions Using Transfer Learning Manurung, Jefri; Setiawan, Andika; Cahyo Untoro, Meida
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.180

Abstract

Initially, teaching and learning activities were carried out face-to-face in the provided room, but now they have switched to online. Online learning has an impact on student learning disengagement, which is known through indicators of aspects of emotional exhaustion, physical fatigue, cognitive fatigue, and loss of motivation. Besides, the teacher must provide the material that has been provided. The teacher must also pay attention to all students who are participating in the online learning. This can be overcome by a system that can detect student disengagement using a camera device. The system works by scanning the direction of students' faces and views using OpenCV technology and Transfer Learning methods. Using context, facial expressions, and heart rate can be used to recognize student disengagement. However, with the widespread availability of cameras, it is easier to identify disengagement using facial expressions. The facial expression recognition system in this study will use the FER2013 dataset and Transfer Learning method. Facial expression recognition using the FER-2013 dataset and Transfer Learning method has a reading accuracy rate of 68% in 25 epochs. Then, after being implemented as an impression parameter in the disengagement identification system using 7 scenarios, the accuracy rate is 83.33%, precision is 100%, recall is 75%, and the f1-score is 85.71%.
Enhanced Laptop Recommendation System Using Tsukamoto Fuzzy Logic Nasrullah, Asmaul Husnah; Adiba, Fhatiah; Anastasia, Tezza; Farghina, Syakira Ayma; Akbar, Muh.
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.186

Abstract

One of the most popular and rapidly growing needs among the general public is laptops. Currently, there are many types of laptops with varying features, and not everyone knows the advantages and disadvantages of each type. The purpose of this research is to develop and build a Fuzzy inference system that applies the Tsukamoto method. This is to address issues in providing unclear or inaccurate services to customers during the laptop sales process. By developing a recommendation system that can provide guidance or suggestions in purchasing a laptop based on interest and needs in searching for references, and the type of laptop that meets the criteria. The decision to purchase a laptop uses parameters such as screen size, RAM capacity, SSD capacity, and price. The implementation of the Fuzzy Tsukamoto method for providing laptop purchase recommendations is able to give good recommendations.

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