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All Journal Rekam : Jurnal, Fotografi, Televisi Animasi Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JAREE (Journal on Advanced Research in Electrical Engineering)
Eko Mulyanto Yuniarno
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

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Penjejakan Fitur Berbasis Koherensi Temporal dalam Sistem Animasi Ekspresi Wajah Samuel Gandang Gunanto; Mochamad Hariadi; Eko Mulyanto Yuniarno
Rekam : Jurnal Fotografi, Televisi, Animasi Vol 12, No 2 (2016): Oktober 2016
Publisher : Institut Seni Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24821/rekam.v12i2.1425

Abstract

AbstrakTingginya permintaan produktivitas industri animasi di Indonesia menuntut adanya perubahandi sektor produksi. Teknologi motion capture merupakan penerapan prinsip visi komputeryang mengadaptasi indera mata manusia untuk mengenali fenomena gerakan yang tertangkapkamera dan memetakannya dalam pola gerak virtual. Tulisan ilmiah ini akan membahas metodepenjejakan fitur penanda di wajah manusia untuk mendapatkan informasi mengenai ekspresiwajah. Teknik penjejakan menggunakan penerapan prinsip koherensi temporal. Asumsi yangdigunakan pada penelitian ini berargumentasi bahwa dengan menggunakan pendekatankoherensi temporal, maka proses penjejakan fitur di citra sekuensial dapat disederhanakandengan perhitungan nilai kedekatan pada penanda di setiap frame-nya. Hasil yang didapatmenunjukkan bahwa proses penjejakan fitur yang diusulkan memiliki hasil yang handal untukmenangani banyak frame. Komputasi yang digunakan juga sangat efisien dan hemat karenaprosesnya tidak memerlukan tahap pembelajaran terlebih dahulu. Kumpulan hasil penjejakanparameter fitur penanda secara sekuensial akan membentuk sebuah basis data ekspresi visualdari wajah manusia. AbstractTemporal Coherence Based Feature Tracking in the Animation System of Facial Expression.High demand on the productivity of the animation industry in Indonesia requires a changein the existing production process. Motion capture technology is the implementation of acomputer vision principle to adopt the human eye senses to understand the phenomenon ofmotion results from a camera and to map the virtual movement patterns. This paper willdiscuss a method for tracking marker features in the human face to obtain information aboutfacial expressions. The tracking technique is using implementation of temporal coherenceprinciple. This research assumes that by using temporal coherence approach, the trackingprocess in sequential images can be simplified by calculating similarity on markers in eachframe. The result shows that this feature-tracking process have reliable result to handle alot of frames. The computation used is very efficient and cheap because it does not requirea learning process in advance. The precision accuracy of tracking parameters generated adatabase of good visual expression.
Optimasi Penjadwalan Perkuliahan Menggunakan Metode Harmony Search Abd Rahman; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol 2, No 2 (2014): Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.477 KB) | DOI: 10.24256/jpmipa.v2i2.111

Abstract

Penjadwalan perkuliahan pada suatu perguruan tinggi adalah kegiatan rutin tiap semester dan merupakan suatu proses untuk menerapkan event yang berisi komponen mata kuliah dan kelas pada time slot yang berisi komponen waktu dan ruang. Permasalahan yang sering terjadi dalam kegiatan penjadwalan adalah terjadinya pertentangan antara jadwal yang satu dengan yang lain. Salah satu metode untuk menyelesaikan permasalahan tersebut ialah dengan menggunakan bantuan kecerdasan buatan atau Artificial Intelligence (AI). Salah satu metode dalam AI yang dianggap dapat memberikan solusi atas permasalahan penjadwalan ialah Harmony Search. Harmony Search merupakan suatu wilayah ilmu komputer yang mendasarkan algoritmanya pada musik. Algoritma Harmony Search menganalogikan musik dengan segala perangkatnya dengan permasalahan optimasi. Misalnya, setiap alat musik berkaitan dengan variabel keputusan, nada musik berkaitan dengan nilai variabel, harmoni berhubungan dengan vektor solusi. Seperti seorang musisi yang memainkan musik tertentu, berimprovisasi memainkan nada secara random atau berdasarkan pengalaman untuk menemukan harmoni yang indah, variabel dalam Harmony Search mempunyai nilai random atau nilai yang didapat dari iterasi (memory) dalam usaha mendapatkan solusi optimal. Dengan menerapkan algoritma Harmony Search dalam penyusunan jadwal perkuliahan, maka diharapkan dapat tercipta suatu susunan jadwal perkuliahan yang optimal.
Gamelan Demung Music Transcription Based on STFT Using Deep Learning Andi Rokhman Hermawan; Eko Mulyanto Yuniarno; Diah Puspito Wulandari
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i2.276

Abstract

Learning to play a gamelan instrument would be easier when there’s a musical notation guide. The process of converting a musical signal into a notation guide is called transcription. In this paper, we would like to transcript the gamelan music especially the Demung instrument using the Deep Learning method. Each Demung’s note from 6-low until 1-high would be converted to the time-frequency domain using STFT (Short-Time Fourier Transform). Then, those data will be treated as an input for the multilayers perceptron. The training method is a single label of each notation. The output returned by the model is a music roll transcription.
IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio Dewi Nurdiyah; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.827

Abstract

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score.
DEEP LEARNING ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) IN IMAGE CLASSIFICATION Fawaidul Badri; M. Taqijuddin Alawiy; Eko Mulyanto Yuniarno
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.349

Abstract

In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.
Co-Authors Abd Rahman Andi Rokhman Hermawan Dewi Nurdiyah Diah Puspito Wulandari Fawaidul Badri I Ketut Eddy Purnama M. Taqijuddin Alawiy Mauridhi Hery Purnomo Mochamad Hariadi Samuel Gandang Gunanto Yoyon Kusnendar Suprapto