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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi InComTech: Jurnal Telekomunikasi dan Komputer Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE)
Mirna Yunita
Beijing Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis Yuliza Yuliza; Rachmat Muwardi; Danang Widya Pratama; Makmur Heri Santoso; Mirna Yunita
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.22336

Abstract

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915.
Network Security Monitoring System Via Notification Alert Rachmat Muwardi; Hongmin Gao; Harun Usman Ghifarsyam; Mirna Yunita; Andika Arrizki; Julpri Andika
Journal of Integrated and Advanced Engineering (JIAE) Vol 1, No 2 (2021)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v1i2.22

Abstract

The development of information technology nowadays has become faster, and this makes network security become important. A huge increasing number of computers that are connected makes many gaps in a network. An administrator has an important role in protecting the security of the network. The problem comes when an administrator has human problems such as pain, negligence, and tiredness while needing rapid information when there is an intrusion on the network. This problem can be solved by adding a data traffic detection system known as Intrusion Detection System (IDS). IDS will be connected to Mail Gateway until that administrator can receive notifications such as alerts during an intrusion to the network anytime and anywhere. Snort as one of the network security systems should be developed as a security detection system and network security. A security intrusion prevention system or an Intrusion Prevented System (IPS). The author tries to do analysis and testing on the subjects above to produce a system capable of detecting the intruder in a network that is mobile and also makes it easy for administrators to open data anywhere and anytime using any device.
Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted Galang Persada Nurani Hakim; Rachmat Muwardi; Mirna Yunita; Diah Septiyana
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1388-1395

Abstract

Fuzzy Mamdani knows as one of the modern control systems. It was known to have a better performance result when compared to conventional methods. However, because the input of this modern control system sometimes is based on human experience, therefore its performance is sometimes below the conventional one. We propose using the adaptive neuro fuzzy inference system assisted (ANFIS) approach to optimize the fuzzy Mamdani membership function input to overcome this problem. We have tested our hypotheses in water chiller applications based on microcontrollers. Even though it is still behind conventional methods to cool 200 ml water, which is 6 minutes, using fuzzy ANFIS methods, we manage to improve the speed performance in cooling water from 20 minutes to 17 minutes, which is from room temperature to just 24 oC.
Optimize Image Processing Algorithm on ARM Cortex-A72 and A53 Rachmat Muwardi; Mirna Yunita; Harun Usman Ghifarsyam; Hendy Juliyanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24457

Abstract

This work presents a technique to optimize processing image algorithms. The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has the mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM supported by shared memory, is presented with implementation details. The target platform chosen is the NanoPi M4V2. It has a dual-core and quad-core architecture with an ARM Cortex-A72 and Cortex-A53. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template-matching tasks such as face recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core, which runs a popular operating system such as Linux. The algorithms are tested on a variety of images, and performance results are presented, measuring the speedup obtained due to dual-core and quad-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks.
Monitoring chicken livestock process using Vento Application at a farm Firman Andika; Rachmat Muwardi; Mirna Yunita; Mhd Adanan Purba
Journal of Integrated and Advanced Engineering (JIAE) Vol 2, No 2 (2022)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v2i2.42

Abstract

Regular temperature monitoring in the poultry industry is necessary to produce high-quality products. However, the traditional methods of these activities are still massively applied. Therefore, a modest process on poultry farms requires temperature monitoring and control. Vento is a climate controller developed by Big Dutchman that is easy to understand and user-friendly. Further, the module provides a handy installation and operation on the farm. Therefore, Vento is suitable to use in hot climates area. Vento performs temperature control by reading the input collected from the DOL114 sensor and the DOL12 sensor. The DOL114 sensor performs temperature and humidity detection in the front area of the cage. At the same time, the DOL12 was installed to obtain temperature information behind the cage. Both functions as input to operate the Heater, but only the DOL114 sensor is used to activate the cooling pad. The temperature value gathered by the DOL114 sensor and the DOL12 sensor will be processed by Vento, resulting in average temperature data. Thus, the data obtained from the sensor will be transferred to the Vento system to operate the output in the form of an exhaust fan. The exhaust fan serves as a tool that removes air and ammonia in the cage so that the cage temperature complies with a predetermined setpoint. 
Comparison of 920 MHz and 2.4 GHz Near Ground Electromagnetic Wave Pathloss Propagation Model for Wireless Sensor Network in Forest Environment Application Galang P. N. Hakim; Rachmat Muwardi; Mirna Yunita; Diah Septiyana
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 2 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i2.14186

Abstract

A Wireless Sensor Network (WSN) system that uses wireless communication technologies occasionally experiences data loss when undertaking wireless data communication. This problem happens because of the WSN system being placed on top of the ground and surrounded by vegetation that adds more loss to the transmission. In order to avoid this problem, the wireless system design must meet its best performance. To build the best performing WSN system, electromagnetic wave behavior in the forest environment needs to be studied well. This paper investigates the electromagnetic wave behavior transmitted and propagated by a WSN node at less than 30 cm from the ground using a 920 MHz frequency. We have analyzed that low height (30 cm) and vegetation environments can also add more loss at about 30.96 dB to the free space pathloss model. The new 920 MHz (that adds 30.96 dB loss) model shows identical behavior to 2.4 GHz with an average difference of 12.24 dB. However, the 920 MHz model performs better, achieving an average RMSE of 1.06 compared with the 2.4 GHz model, which can only achieve an average RMSE of 4.92 compared with the 920 MHz measurement.
Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family Mirna Yunita; Rachmat Muwardi; Zendi Iklima
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023..1.004

Abstract

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.   
Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture Khoirul Aziz; Inggis Kurnia Trisiawan; Kadek Dwi Suyasmini; Zendi Iklima; Mirna Yunita
SINERGI Vol 27, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.2.003

Abstract

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.
Human Object Detection for Real-Time Camera using Mobilenet-SSD Rachmat Muwardi; Joe Mada Ranseda Permana; Hongmin Gao; Mirna Yunita
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 2 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i2.108

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can benefit from this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is safety, namely in searching for people, which is still done manually. Searching for teams to find people is often challenging because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the MobilenetV2-SSD, where this algorithm has high detection and accuracy. Using the mobilenetV2-SSD simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Fast Human Recognition System on Real-Time Camera Yuliza Yuliza; Rachmat Muwardi; Mustain Rhozaly; Lenni Lenni; Mirna Yunita; Galatia Erica Yehezkiel
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27009

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can get many benefits by implementing this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is in the area of safety, namely in the field of searching for people, which is still done manually. Often search teams find it challenging to find people because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the You only look once (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Co-Authors Andika Arrizki Danang Widya Pratama Diah Septiyana Firman Andika Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Inggis Kurnia Trisiawan Joe Mada Ranseda Permana Julpri Andika Kadek Dwi Suyasmini Khoirul Aziz Lenni Lenni Mhd Adanan Purba Mustain Rhozaly Rachmat Muwardi Septiyana, Diah Yuliza Yuliza Yuliza Yuliza Zendi Iklima