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Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Published by Universitas Muhammadiyah Malang
ISSN : 25032259     EISSN : 25032267     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control was published by Universitas Muhammadiyah Malang. journal is open access journal in the field of Informatics and Electrical Engineering. This journal is available for researchers who want to improve their knowledge in those particular areas and intended to spread the knowledge as the result of studies. KINETIK journal is a scientific research journal for Informatics and Electrical Engineering. It is open for anyone who desire to develop knowledge based on qualified research in any field. Submitted papers are evaluated by anonymous referees by double-blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully within 4 - 8 weeks. The research article submitted to this online journal will be peer-reviewed at least 2 (two) reviewers. The accepted research articles will be available online following the journal peer-reviewing process.
Arjuna Subject : -
Articles 22 Documents
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Search results for , issue "Vol 4, No 1, February 2019" : 22 Documents clear
Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things Alieja Muhammad Putrada; Maman Abdurohman; Aji Gautama Putrada
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 1, February 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (594.29 KB) | DOI: 10.22219/kinetik.v4i1.704

Abstract

This paper proposes fire alarm system by implementing Naïve Bayes Method for increasing smoke classifier accuracy on Internet of Things (IoT) environment. Fire disasters in the building of houses are a serious threat to the occupants of the house that have a hazard to the safety factor as well as causing material and non-material damages. In an effort to prevent the occurrence of fire disaster, fire alarm system that can serve as an early warning system are required. In this paper, fire alarm system that implementing Naïve Bayes classification has been impelemented. Naïve Bayes classification method is chosen because it has the modeling and good accuracy results in data training set. The system works by using sensor data that is processed and analyzed by applying Naïve Bayes classification to generate prediction value of fire threat level along with smoke source. The smoke source was divided into five types of smoke intended for the classification process. Some experiments have been done for concept proving. The results show the use of Naïve Bayes classification method on classification process has an accuracy rate range of 88% to 91%. This result could be acceptable for classification accuracy.
Fuzzy Logic Control Design in Hybrid Energy Storage System Super-Capacitor Battery for Electric Vehicle Thomi Dhia; Nur Alif Mardiyah; Nurhadi Nurhadi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 1, February 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v4i1.658

Abstract

HESS is a suitable technology when applied to electric cars. One application is in SUV (Sport Utility Vehicle) car. The use of Supercapacitors can reduce the excess of load on the battery. Uneven soil contours cause current spike and drop in battery voltage which can reduce battery lifetime. This journal presents a simulated study of HESS batteries and Supercapacitors in SUV cars. Fuzzy-based energy management strategies and threshold control are introduced. The simulation study shows the difference between the control using the number of climb data and not, and the Fuzzy control response to the demand load. The simulation results mention the reduction of the maximum current and the battery voltage drop according to the load. The total energy ratio used between the two controls is also presented.

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