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All Journal Sinergi Jurnal Teknologi Elektro TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Abdi Masyarakat (JAM) Jurnal Ilmu Teknik dan Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Abdidas International Journal of Advanced Technology in Mechanical, Mechatronics and Material (IJATEC) Journal FORTEI-JEERI International Journal of Engineering Continuity Jurnal Informatika: Jurnal Pengembangan IT
Muhammad Hafizd Ibnu Hajar
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Sinergi

 1 
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Suwoyo, Heru; Tian, Yingzhong; Ibnu Hajar, Muhammad Hafizd
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.002

Abstract

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.
ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
SINERGI Vol 24, No 2 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.577 KB) | DOI: 10.22441/sinergi.2020.2.008

Abstract

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.002

Abstract

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.
MONITORING OF ELECTRICAL SYSTEM USING INTERNET OF THINGS WITH SMART CURRENT ELECTRIC SENSORS Muhammad Hafizd Ibnu Hajar; Akhmad Wahyu Dani; Satriyo Miharno
SINERGI Vol 22, No 3 (2018)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (198.163 KB) | DOI: 10.22441/sinergi.2018.3.010

Abstract

Electricity is one of the most important human needs. In the presence of electricity it can facilitate human work. But it should be noted that too large and uncontrolled electricity use will be wasteful and get high costs. The problem is that electricity is not monitored accurately, easily and efficiently. This study aims to design an electric current monitoring device with an IoT system. IoT is a concept with the ability to transfer data by network, no need humans to humans or humans to PCs. In this concept, the SCT 013-000 electric current sensor is connected to the load, it will be show electric current value in the LCD, if the electric current which is determined exceeds the capacity, Wemos D1 including Wifi ESP 8266 will be sending a notification to the telegram. The system has been implemented with ironing load for 3.29%, the dispenser load is 0.20% and Magicom's get load for 1.07%. The delay time also has been implemented in the relay for 1.50 second when relay is on and 0.78 second when relay is off. When the notification send to the telegram also have a delay for 6.2 second. So, monitoring of electrical system using internet of things with smart current electric sensors has been done.
The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm Suwoyo, Heru; Hajar, Muhammad Hafizd Ibnu; Indriyanti, Prastika; Febriandirza, Arafat
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.003

Abstract

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.
Co-Authors Abdurohman Abdurohman Adi Juliyanto Ahmad Firdausi Ahmad Rasikh Bahrain Akhmad Wahyu Dani Akhmad Wahyu Dani Akhmad Wahyu Dani, Akhmad Wahyu Alip Pandu Budiyanto Alvin Sepbrian Andi Adriansyah Andi Adriansyah Andi Adriansyah Andi Adriansyah Andrial Saputra Dinata, Rizky Eko Ihsanto Eko Ramadhan Eko Ramadhan Fadli Sirait Fauziah, Isti Febriandirza, Arafat Fina Supegina Freddy Artadima Silaban Galang Persada Nurani Hakim Gao Hongmin Hutomo, Fajri Rezki Indriyanti, Prastika Julpri Andika Ketty Siti Salamah Lukman Medriavin Silalahi Miharno, Satriyo Muhammad Yusuf Rachmat Muwardi Saputra, Yogi Ade Sari, Ovina Ambar Satriyo Miharno Setiyo Budiyanto Suwoyo, Heru Syarifudin Jupri Thong, Zhou Tian, Yingzhong Tong Zhou Trie Maya Kadarina Triwidya Mochtar, Thathit Gumilar Triyanto Pangaribowo Ulumudin, Mochammad Nawawi Yifan Li Yingzhong Tian Yingzhong Tian Yingzhong Tian Yingzhong Tian Yogi Ade Saputra Yudhi Gunardi Yudhi Gunardi Yudhi Gunardi Yulianto Wisnuputra Yuliza Yuliza Yuliza Yuliza Zendi Iklima Zhou Thong