Garuda - Garba Rujukan Digital

FACIAL EXPRESSIONS RECOGNITION USING BACKPROPAGATION NEURAL NETWORK FOR MUSIC PLAYLIST ELECTIONS Setiawardhana Setiawardhana; Nana Ramadijanti; Peni Rahayu
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Penelitian ini dibuat untuk mengenali ekspresi wajah sebagai indikator untuk menjalankan playlist musik. Sistem pengenalan ekspresi wajah berasal dari data masukan seseorang yang diambil secara offline, dengan posisi terdekat dengan kamera, dimana posisi wajah tidak boleh miring. Prosesnya dengan pengambilan citra wajah secara offline yang dikenali dengan kombinasi warna, dan mengekstrak fitur penting dari wajah berdasarkan lokasi alis, mata, dan bentuk mulut kemudian mengenali ekspresi wajah menggunakan Jaringan Saraf Tiruan Propagasi Balik (Backpropagation Neural Network). Ekspresi yang akan dikenali Data keluaran dari pengenalan ekspresi wajah berupa indek yang secara otomatis akan digunakan sebagai indikator untuk menjalankan musik, sehingga musik akan berubah mengikuti perubahan ekspresi wajah seseorang. Sistem yang telah dibuat dapat mengenali tiga jenis ekspresi yaitu: normal, marah, dan bahagia. Pengujian dengan pengambilan gambar wajah secara offline sebagai data masukan untuk Jaringan Saraf Tiruan Propagasi Balik, dimana pada saat pembelajaran diperoleh hasil yang konvergen dengan error terendah dengan jumlah neuron pada lapisan hidden sebanyak 10 unit, nilai laju pembelajaran sebesar 0.0625325 dan nilai mean square error sebesar 0.0135. Kata Kunci: Ekspresi Wajah, Backpropagation, Music Playlist. Abstract The objective of the research is to detect facial expression as indicator to cast a music playlist. Facial expression detection system input is performed offline by taking photograph of a subject with nearest position from the camera and facial position should not be tilted. The image is identified as a combination of color and feature extraction is performed based on location of eyebrow, eye, and mouth. Facial expression is detected with Artificial Neural Network Backpropagation method. The output data is an index, which automatically select and play the music. In this way, the music is modified according to the changes of facial expression. The system is designed to detect three facial expressions: normal, angry, and happy expression. The similarity between features values from each expression influence the ability to differentiate each expression. Offline system evaluation is performed with backpropagation neural network method,for learning process, it reaches convergent value with lowest error value when using 10 unit neuron on hidden layer, learning rate value is 0.0625325 and mean square error value is 0.0135.

P5 Module Embedded System online training using HD-2020 for vocational students in Surabaya Heny Yuniarti; Reni Soelistijorini; Maretha Ruswiansari; Setiawardhana Setiawardhana; Riyanto Sigit; Bayu Sandi Marta; Mochamad Mobed Bachtiar; Dewi Mutiara Sari; Iwan Kurnianto Wibowo
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol 7, No 2 (2022): May 2022
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v7i2.6310

Vocational students need hard skills and empowerment to enter the workforce. This community service activity aims to improve the skills of SMK Negeri 6 Surabaya students through training to make simple applications for running text LEDs from the P5 module. Running text is one of the electronic media that is very useful for conveying messages and information. Program activities that have been adapted to the needs of the school. So that this program is able to improve the quality of their hard skills and bring benefits to the community, lecturers, employees, and students of the EEPIS (Electronic Engineering Polytechnic Institute of Surabaya) Computer Engineering Study Program. The implementation method includes an initial survey of partner needs, vocational needs analysis, online training preparation, and installation and delivery of tools. The results of the post-test using a questionnaire showed that 97.9% of participants stated that there was an increase in embedded systems skills, especially in the P5 module and its software applications. There were 85.4% of participants wanted to learn more about embedded systems for other areas of expertise.

Embedded system training based on Arduino to improve software programming knowledge for vocational students Idris Winarno; Aliridho Barakbah; Dadet Pramadihanto; Wahjoe Tjatur Sesulihatien; Tri Harsono; Bima Sena Bayu Dewantara; Setiawardhana Setiawardhana; Arna Fariza; Iwan Syarif; Tessy Badriyah; Ilham Iskandariansyah; Puspasari Susanti
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol 7, No 4 (2022): November 2022
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v7i4.8039

Vocational high school students majoring in engineering, who are the successors to technology developers in society, need hard skills to enter the workforce. However, the students of SMK Negeri 2 Bangkalan have not received knowledge about embedded systems. In supporting this need, Master in Applied Informatics and Computer Engineering Program at Politeknik Elektronika Negeri Surabaya held a community service program by holding practice-based training on Arduino Uno-based embedded systems. This program is aimed at vocational high school students so that they can improve their abilities and skills in hardware programming and embedded. The method used in this activity is workshop-based training. The workshop includes a basic explanation of recognizing Arduino and embedded systems, a simple Arduino circuit practicum, and questions and answers between tutors and trainees. The results achieved from this activity are indicated by the results of the training evaluation questionnaire. In general, the training material is considered to be in accordance with the need to add insight, knowledge, skills, and expertise. In addition, the practice-based training on Arduino Uno-based embedded systems has provided benefits for students to use technology in their daily lives.

Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control Alif Wicaksana Ramadhan; Bima Sena Bayu Dewantara; Setiawardhana Setiawardhana
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.

Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Ricky Afiful Maula; Agus Indra Gunawan; Bima Sena Bayu Dewantara; M. Udin Harun Al Rasyid; Setiawardhana Setiawardhana; Ferry Astika Saputra; Junaedi Ispianto
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.

Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control Alif Wicaksana Ramadhan; Bima Sena Bayu Dewantara; Setiawardhana Setiawardhana
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i1.28330

The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.

Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Ricky Afiful Maula; Agus Indra Gunawan; Bima Sena Bayu Dewantara; M. Udin Harun Al Rasyid; Setiawardhana Setiawardhana; Ferry Astika Saputra; Junaedi Ispianto
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i3.25903

Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.

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