Articles
<![CDATA[Developing Artificial Neural Network Based on Visual Studio for Dance Assessment ]]>
<![CDATA[Rahmahwati, Febri Suci]]>;
<![CDATA[Arifin, Fatchul]]>
<![CDATA[ Jurnal Pendidikan Teknologi dan Kejuruan Vol 23, No 4 (2017): (October) ]]>
Publisher : <![CDATA[Faculty of Engineering, Universitas Negeri Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1011.311 KB)
|
DOI: 10.21831/jptk.v23i4.13800
<![CDATA[The dance assessment test still uses a manual system that tend to have frequent errors in the calculation for the final results thus it requires a system that accelerate the assessment process with an accurate result. This study aimed at: (1) designing an artificial neural network application based on visual studio for the dance assessment. (2) examining the performance of the artificial neural network application based on visual studio for the dance assessment. The design method consists of (1) system design (2) interface design (3) database design for artificial neural network system. (4) design of artificial neural network model. (5) programming (6) system testing. The design of visual studio artificial neural network application for the dance assessment has two stages: main program and supporting program. This research built a system by implementing visual studio and artificial neural network to assess dance examination which can give the final result to each participant directly. The application of the dance assessment can assess 3 types of dance with a training set of at least 10 pairs to undertake learning that produces the load to be used in the assessment. Besides assessing, this application can also delete, repair, and store data in the form of .xls. Based on the test results, it can be concluded that the application operates effectively for determining the final load, data input and data storage.]]>
<![CDATA[Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network ]]>
<![CDATA[Arifin, Fatchul]]>;
<![CDATA[Sardjono, Tri Arief]]>;
<![CDATA[Purnomo, Mauridhy Hery]]>
<![CDATA[ IPTEK The Journal for Technology and Science Vol 21, No 3 (2010) ]]>
Publisher : <![CDATA[IPTEK, LPPM, Institut Teknologi Sepuluh Nopember ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.12962/j20882033.v21i3.45
<![CDATA[The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter) also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A†and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya†voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.]]>
<![CDATA[Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion ]]>
<![CDATA[Mir Suhail Alam]]>;
<![CDATA[Malik Arman Morshidi]]>;
<![CDATA[Teddy Surya Gunawan]]>;
<![CDATA[Rashidah Funke Olanrewaju]]>;
<![CDATA[Fatchul Arifin]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijece.v12i4.pp3620-3631
<![CDATA[Augmented reality (AR) applications have become increasingly ubiquitous as it integrates virtual information such as images, 3D objects, video, and more to the real world, which further enhances the real environment. Many researchers have investigated the augmentation of the 3D object on the digital screen. However, certain loopholes exist in the existing system while estimating the object’s pose, making it inaccurate for mobile augmented reality (MAR) applications. Objects augmented in the current system have much jitter due to frame illumination changes, affecting the accuracy of vision-based pose estimation. This paper proposes to estimate the pose of an object by blending both vision-based techniques and micro electrical mechanical system (MEMS) sensor (gyroscope) to minimize the jitter problem in MAR. The algorithm used for feature detection and description is oriented FAST rotated BRIEF (ORB), whereas to evaluate the homography for pose estimation, random sample consensus (RANSAC) is used. Furthermore, gyroscope sensor data is incorporated with the vision-based pose estimation. We evaluated the performance of augmenting the 3D object using the techniques, vision-based, and incorporating the sensor data using the video data. After extensive experiments, the validity of the proposed method was superior to the existing vision-based pose estimation algorithms.]]>
<![CDATA[Troop camouflage detection based on deep action learning ]]>
<![CDATA[Muslikhin Muslikhin]]>;
<![CDATA[Aris Nasuha]]>;
<![CDATA[Fatchul Arifin]]>;
<![CDATA[Suprapto Suprapto]]>;
<![CDATA[Anggun Winursito]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i3.pp859-871
<![CDATA[Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.]]>
<![CDATA[PERANCANGAN DAN SIMULASI SISTEM SUSPENSI MOBIL BERBASIS KENDALI OPTIMAL ]]>
<![CDATA[Fatchul Arifin]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 4, No 3: December 2006 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.12928/telkomnika.v4i3.1315
<![CDATA[Mobil adalah suatu alat transportasi darat yang sangat penting bagi kehidupan manusia modern. Salah satu faktor kenyamanan mengendarai mobil adalah sistem suspensi (soft breaker) yang dimilikinya. Dengan sistem suspensi yang bagus, ketika mobil terkena guncangan, mobil akan tetap stabil. Pada penelitian ini akan diajukan salah satu cara/pendekatan dalam merancang sistem suspensi mobil melalui pendekatan kendali optimal dengan metode Linear Quadratic Regulator (LQR). Mobil yang akan dirancang sistem suspensinya dimodelkan dalam persamaan matematis, dan selanjutnya akan didesain sistem suspensi yang tepat untuk mobil tersebut. Perancangan dilakukan dengan bantuan perangkat lunak MATLAB untuk mendapatkan parameter-parameter kendali yang dibutuhkan. Pengujian dilakukan pada mobil dengan muatan penuh dan kosong dengan diberikan guncangan. Berdasarkan simulasi dengan perangkat lunak MATLAB, didapatkan bahwa sistem suspensi yang dirancang memiliki unjuk kerja yang memuaskan (kondisi mobil relatif stabil).]]>
<![CDATA[Perancangan Prototipe Pendiagnosa Penyakit Jantung Koroner Dengan Metode Backpropagation ]]>
<![CDATA[Ranu Iskandar]]>;
<![CDATA[Prasetyo Prasetyo]]>;
<![CDATA[Muhammad Rofiq Banu Alfath]]>;
<![CDATA[Fatchul Arifin]]>
<![CDATA[ Lontara Journal of Health Science and Technology Vol 2 No 1 (2021): Ilmu dan Teknologi Kesehatan ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Masyarakat Politeknik Kesehatan Muhammadiyah Makassar ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.53861/lontarariset.v2i1.81
<![CDATA[Penyakit jantung koroner adalah suatu kelainan yang disebabkan oleh penghambatan pembuluh arteri yang mengalirkan darah ke otot jantung. Penyakit ini merupakan salah satu penyakit tidak menular yang kerap mengakibatkan kematian secara langsung pada para korbannya. Tujuan penulisan artikel ini adalah merancang sebuah arsitektur jaringan syaraf tiruan menggunakan metode backpropagation yang dapat memprediksi seseorang terkena penyakit jantung koroner dengan input kadar kolesterol, tekanan darah, dan kadar gula darah, dan indeks masa tubuh. Penelitian ini merupakan penelitian dan pengembangan. Metode penelitian yang digunakan pada pembuatan prototipe ini, yaitu: (1) analisa masalah, (2) analisa kebutuhan, (3) studi pustaka, (4) perancangan prototipe, dan (5) pengujian prototipe. Data pasien yang digunakan untuk menguji prototipe sejumlah 20. Hasil menunjukkan model jaringan syaraf tiruan yang digunakan memiliki nilai rata-rata kesalahan sebesar 0,792% dengan 5000 kali training. Prototipe diagnosa penyakit jantung koroner menggunakan backpropagation berjalan berhasil dibangun dengan hasil baik.]]>
<![CDATA[Teaching Aid For Diagnosing Motorcycle Damages Using Back Propagation Artificial Neural Network ]]>
<![CDATA[Nur Hasanah]]>;
<![CDATA[Fatchul Arifin]]>;
<![CDATA[Dessy Irmawati]]>;
<![CDATA[Muslikhin Muslikhin]]>;
<![CDATA[Zainal Arifin]]>
<![CDATA[ Jurnal Pendidikan Teknologi dan Kejuruan Vol 26, No 2 (2020): (October) ]]>
Publisher : <![CDATA[Faculty of Engineering, Universitas Negeri Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.21831/jptk.v26i2.21262
<![CDATA[The challenge of learning media in the world within the next 1 to 2 years is Bring Your Own Device. It forces the learning paradigm to think quickly to follow the development of technology that can optimally use it. In the Control Systems II course, there are some stereotypes that some of the material is mainly an Artificial Neural Network (ANN) was limited to theory and simulations and is difficult to be applied. Teaching aids are interpreted as teaching material that is used to help teachers in carrying out the teaching and learning activities in the classroom. The purposes of this study are: (1) to create teaching aid for ANN material to diagnose motorcycle damage in the Control System II Course (2) to define the accuracy of the application of the teaching aid for the material of ANN in the Control System II Course. The prototyping approach model is used to generally define the teaching aid product that will be developed. In detail, the development methods include (1) listen to the customer, (2) build or revise a mock-up, and (3) customer test drives mockup. Teaching aids products are built in the form of application for the diagnosis of motorcycle damages using the Back-Propagation ANN. This application can detect four types of motorcycle damages based on the sample sounds of motorcycles included. The application can recognize the type of damage from 100 new sound data outside its knowledge-base with a 60% accuracy level.]]>
<![CDATA[Predicting The Number of Tourists Based on Backpropagation Algorithm ]]>
<![CDATA[Dwi Marlina]]>;
<![CDATA[Fatchul Arifin]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (557.823 KB)
|
DOI: 10.29207/resti.v5i3.3061
<![CDATA[The number of tourists always fluctuates every month, as happened in Kaliadem Merapi, Sleman. The purpose of this research is to develop a prediction system for the number of tourists based on artificial neural networks. This study uses an artificial neural network for data processing methods with the backpropagation algorithm. This study carried out two processes, namely the training process and the testing process with stages consisting of: (1) Collecting input and target data, (2) Normalizing input and target data, (3) Creating artificial neural network architecture by utilizing GUI (Graphical User Interface) Matlab facilities. (4) Conducting training and testing processes, (5) Normalizing predictive data, (6) Analysis of predictive data. In the data analysis, the MSE (Mean Squared Error) value in the training process is 0.0091528 and in the testing process is 0.0051424. Besides, the validity value of predictive accuracy in the testing process is around 91.32%. The resulting MSE (Mean Squared Error) value is relatively small, and the validity value of prediction accuracy is relatively high, so this system can be used to predict the number of tourists in Kaliadem Merapi, Sleman.]]>
<![CDATA[Development of Javanese Speech Emotion Database (Java-SED) ]]>
<![CDATA[Fatchul Arifin]]>;
<![CDATA[Ardy Seto Priambodo]]>;
<![CDATA[Aris Nasuha]]>;
<![CDATA[Anggun Winursito]]>;
<![CDATA[Teddy Surya Gunawan]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022 ]]>
Publisher : <![CDATA[IAES Indonesian Section ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52549/ijeei.v10i3.3888
<![CDATA[Javanese is one of the most widely spoken regional languages in Indonesia, alongside other regional languages. Emotions can be recognized in a variety of ways, including facial expression, behavior, and speech. The recognition of emotions through speech is a straightforward process, but the outcomes are quite significant. Currently, there is no database for identifying emotions in Javanese speech. This paper aims to describe the creation of a Javanese emotional speech database. Actors from the Kamasetra UNY community who are accustomed to performing in dramatic roles participated in the recording. The location where recordings are made is free of interference and noise. The actors of Kamasetra have simulated six types of emotions, including happy, sad, fear, angry, neutral, and surprised. The cast consists of ten people between the ages of 20 and 30, including five men and five women. Both humans (30 Javanese-speaking verifiers ranging in age from 17 to 50) and a machine learning system (30 Javanese-speaking verifiers with ages between 17 and 50) verify the database that has been created. The verification results indicate that the database can be used for Javanese emotion recognition. The developed database is offered as open-source and is freely available to the research community at this link https://beais-uny.id/dataset/]]>
<![CDATA[On the Audio-Visual Emotion Recognition using Convolutional Neural Networks and Extreme Learning Machine ]]>
<![CDATA[Arselan Ashraf]]>;
<![CDATA[Teddy Surya Gunawan]]>;
<![CDATA[Fatchul Arifin]]>;
<![CDATA[Mira Kartiwi]]>;
<![CDATA[Ali Sophian]]>;
<![CDATA[Mohamed Hadi Habaebi]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022 ]]>
Publisher : <![CDATA[IAES Indonesian Section ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52549/ijeei.v10i3.3879
<![CDATA[The advances in artificial intelligence and machine learning concerning emotion recognition have been enormous and in previously inconceivable ways. Inspired by the promising evolution in human-computer interaction, this paper is based on developing a multimodal emotion recognition system. This research encompasses two modalities as input, namely speech and video. In the proposed model, the input video samples are subjected to image pre-processing and image frames are obtained. The signal is pre-processed and transformed into the frequency domain for the audio input. The aim is to obtain Mel-spectrogram, which is processed further as images. Convolutional neural networks are used for training and feature extraction for both audio and video with different configurations. The fusion of outputs from two CNNs is done using two extreme learning machines. For classification, the proposed system incorporates a support vector machine. The model is evaluated using three databases, namely eNTERFACE, RML, and SAVEE. For the eNTERFACE dataset, the accuracy obtained without and with augmentation was 87.2% and 94.91%, respectively. The RML dataset yielded an accuracy of 98.5%, and for the SAVEE dataset, the accuracy reached 97.77%. Results achieved from this research are an illustration of the fruitful exploration and effectiveness of the proposed system.]]>
