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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information System,Graphics, Hospitality and Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi dan Terapan (J-TIT) Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Jurnal Teknika Teknika Insyst : Journal of Intelligent System and Computation JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer International Journal of Robotics and Control Systems J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Inovasi Teknologi dan Edukasi Teknik
Yosi Kristian
Institut Sains Dan Teknologi Terpadu Surabaya
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Other

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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

 1 
Analisis Pendapat Masyarakat terhadap Berita Kesehatan Indonesia menggunakan Pemodelan Kalimat berbasis LSTM Esther Irawati Setiawan; Adriel Ferdianto; Joan Santoso; Yosi Kristian; Gunawan Gunawan; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1263.215 KB) | DOI: 10.22146/jnteti.v9i1.115

Abstract

The uncertainty of health news content, which is spread on social media, raises the need for validation of the truth. One validation approach is to consider the opinion or attitudes of most people, which is called a stance on a topic, whether they support, oppose, or being neutral. This paper proposes a stance analysis model to classify the relationship between sentences so that it can recognize the correlation of the opinion of the writer in the headline of the problem claim. The proposed model uses several Long Short-Term Memory (LSTM), which represent the interrelationship of news for analysis of the relationship between a claim with other news. The formation of word representation vectors is carried out in conjunction with LSTM-based stance classification training. Sentence embedding is done to get the vector representation of sentences with LSTM. Each word in a sentence occupies one time-step in LSTM and the output of the last word is taken as a sentence representation. Based on the results of trials with the Indonesian health-related dataset that was built for this study, the proposed stance classification model was able to achieve an average F1-score value of 71%, with the supporting value 69%, opposing as much as 70%, and neutral 74%.
Identifikasi Motif Jepara pada Ukiran dengan Memanfaatkan Convolutional Neural Network Sandhopi; Lukman Zaman P.C.S.W; Yosi Kristian
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1809.947 KB) | DOI: 10.22146/jnteti.v9i4.541

Abstract

The more the development of the carving motifs, the more varied the shapes and variations. It complicates the determination of a carving with Jepara motif. In this paper, the transfer learning method with developed FC was used to identify Jepara's distinctive motifs in a carving. The dataset was divided into three color spaces, i.e., LUV, RGB, and YcrCb. Besides, sliding windows, non-max suppression, and heat maps were utilized for the process of tracing the area of the engraved object and identifying Jepara motifs. The test results of all weights showed that the Xception on the Jepara motif classification had the highest accuracy values, namely 0.95, 0.95, and 0.94 for each LUV, RGB, and YCrCb color space dataset. However, when all the model weights were applied to the Jepara motif identification system, ResNet50 was able to outperform all networks with motif identification percentage values of 84%, 79%, and 80%, for the LUV, RGB, and YCrCb color spaces, respectively. These results prove that the system is able to assist in the process of determining whether a carving is included in the Jepara carving or not, by identifying the typical Jepara motifs contained in the carving.
Klasifikasi Nyeri pada Video Ekspresi Wajah Bayi Menggunakan DCNN Autoencoder dan LSTM Yosi Kristian; I Ketut Eddy Purnama; Effendy Hadi Sutanto; Lukman Zaman; Esther Irawati Setiawan; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1508.119 KB)

Abstract

Babies are still unable to inform the pain theyexperience, therefore, babies cry when experiencing pain. With the rapid development of computer vision technologies, in the last few years, many researchers have tried to recognize pain from babies expressions using machine learning and image processing. In this paper, a research using Deep Convolution Neural Network (DCNN) Autoencoder and Long-Short Term Memory (LSTM) Network is conducted to detect cry and pain level from baby facial expression on video. DCNN Autoencoder isused to extract latent features from a single frame of baby face. Sequences of extracted latent features are then fed to LSTM sothe pain level and cry can be recognized. Face detection and face landmark detection is also used to frontalize baby facial imagebefore it i s processed by DCNN Autoencoder. From the testing on DCNN autoencoder, the result shows that the best architecture used three convolutional layers and three transposed convolutional layers. As for the LSTM classifier, the best model is using four frame sequences.
Pemanfaatan Deep Learning pada Video Dash Cam untuk Deteksi Pengendara Sepeda Motor Stephen Ekaputra Limantoro; Yosi Kristian; Devi Dwi Purwanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 2: Mei 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1738.3 KB)

Abstract

The number of motorcyclists in Indonesia was 105.15 million in 2016. It made the Indonesian government difficult to monitor motorcyclists on the highways. Dash cam could be used as the alternative tool to detect motorcyclists when given the intelligence. One of the typical drawbacks in detecting objects is complex and varied feature. A convolutional neural networks (CNN) that was capable of detecting motorcyclists was proposed. CNN successfully classified the ship object with f1-score of 0.94. Sliding window and heat map were used in thispaper to search the localization and region of motorcyclists. Two experiments had been done in this paper. The goal of this paper was to set the best combination of CNN architecture and parameter. The first experiment consisted of three trained weights while the second experiment consisted of one trained weight. Weight peformances against test data in experiment 1 and experiment 2 were measured using f1-score of 0.977, 0.988, 0.989, and 0.986, respectively. From the experimental results using the sliding window, experiment 2 had a lower error rate to predict motorcyclists than experiment 1 because the training data on experiment 1 contained more and various images.
Co-Authors Adriel Ferdianto Ajeng Restu Kusumastuti Aji Prasetya Wibawa Aji Wibawa Alamgunawan, Suriani Albertus Josef Andika Alwi, Luthfi Andy Hermawan Anggay Luri Pramana Anik Nur Handayani Arya Tandy Hermawan Ayyub Chastine Fatichah Citra Mahaputri Damayanti, Elok Darlis Herumurti Devi Dwi Purwanto Dhaniswara, Erwin Edwin Pramana Effendy Hadi Sutanto Eha Renwi Astuti Eka Rahayu Setyaningsih Endang Setyati Endang Setyati Esa Prakasa Esther Irawati Setiawan Fitra Hidayah Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan, Riko Hariadi Yutanto Hartono, Nickolas Hatem Alsadeg Ali Salim Hendrawan Armanto Hendrawan, William Hartanto Hermawan Andika Heru Wahyu Herwanto I Ketut Eddy Purnama Irawati Setiawan, Esther Joan Santoso Kenny Vincent Kms Yusuf Effendi Kurniawan S, Putu Widiarsa Kusuma Susanto, Evan Lukman Zaman M. Najamudin Ridha Masrur Anwar Mauridhi Hery Purnomo Maysas Yafi' Urrochman Muhammad Harris Syafa'at Nafi'iyah, Nur Nur Nafi’iyah Putra, Bayu Anggara Putra, Ramadhan Hardani Retno Wardhani Sandhopi Santoso, Elkana Lewi Setiono, Kevin Setyaningsih, Eka Rahayu Shandy Darmawan Stephen Ekaputra Limantoro Surya Sumpeno Wahyu Sakti Gunawan Irianto Wahyu Widodo Wikjatmiko, Zhulfi Bajra William Sugiarto YOGA DWI WAHYU NUGRAHA Yuliana Melita Pranoto Yulianto, Masrizal Eka Yunan Kholilul Fatah Zaeni, Ilham Ari Elbaith Zuhan, Miftahuz