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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 Journal of Information Technology
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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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.
Model CNN Lenet Dalam Pengenalan Jenis Golongan Kendaraan Pada Jalan Tol Anggay Luri Pramana; Endang Setyati; Yosi Kristian
Jurnal Teknika Vol 12, No 2 (2020): Jurnal Teknika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jt.v13i2.469

Abstract

Research in the field of transportation, especially vehicle classification with various methods, is a widely developed field of study. Vehicles can be categorized by shape, dimension, logo, and  type. The vehicle dataset is also not difficult to find because it is general in nature. Based on the research that has been done, the introduction of group types based on the number of axles with CNN, the dataset is not yet available to the public. In this paper, we discuss the introduction of the types of groups using the Convolutional Neural Network method. The architecture used is the LeNet model. The trial scenario is carried out in 4 stages, namely 25 epochs, 50 epochs, 75 epochs and 100 epochs. Based on the test results, the accuracy obtained continues to increase at 50 epochs and 100 epochs iterations. Starting from an accuracy of 82%, 94% to the highest accuracy of 95%. Likewise in the prediction the data has increased from 80%, 85% to the highest accuracy that can be 86%. From 50 epochs to 75 epochs, the accuracy of both training and testing has decreased.
Pengenalan Makanan Tradisional Indonesia Beserta Bahan-bahannya dengan Memanfaatkan DCNN Transfer Learning Citra Mahaputri; Yosi Kristian; Endang Setyati
Intelligent System and Computation Vol 4 No 2 (2022): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v4i2.252

Abstract

Pengenalan makanan adalah langkah awal untuk melakukan penilaian diet seseorang. Dalam pengenalan makanan beserta bahan-bahannya, dirasakan kurang diseminasi foto-foto makanan tradisional Indonesia, sehingga peneliti terdorong untuk melakukan penelitian mengenai pengenalan makanan tradisional Indonesia. Peneliti membuat klasifikasi citra makanan yang inputannya merupakan citra makanan tradisional Indonesia. Ekstraksi fitur citra makanan sulit untuk diklasifikasikan karena citra makanan beraneka ragam penampilannya, termasuk tekstur, warna, bentuk dan karakteristik visual lainnya. Penelitian ini meneliti pemanfaatan Deep Convolutional Neural Network (DCNN) model EfficientNetB6 dan EfficientNetV2M untuk pengenalan makanan tradisional Indonesia beserta bahan-bahannya. DCNN merupakan metode yang biasa digunakan untuk mendeteksi citra yang komplek. Peneliti mengumpulkan citra makanan tradisional Indonesia secara manual sebanyak 1.202 citra makanan yang berbeda. Terdiri dari 20 jenis makanan tradisional Indonesia. Masing-masing jenis makanan terdapat 50-80 gambar makanan. Data yang digunakan untuk uji klasifikasi makanan adalah 241 data citra makanan di luar data yang digunakan untuk training dan mendapatkan akurasi 83,82% untuk model EfficientNetV2M dan 80,08% untuk model EfficientNetB6. Kemudian pada proses pengujian dalam memprediksi bahan-bahan makanan yang terlihat pada gambar rata-rata mendapatkan 64% untuk model EfficienNetV2M dan 59% untuk model EfficeintNetB6. Berdasarkan hasil penelitian menunjukkan bahwa metode DCNN dengan model EfficientNetV2M dapat mencapai performa terbaik dari model EfficientNetB6.
Model CNN Lenet Dalam Pengenalan Jenis Golongan Kendaraan Pada Jalan Tol Anggay Luri Pramana; Endang Setyati; Yosi Kristian
Jurnal Teknika Vol 12 No 2 (2020): Jurnal Teknika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jt.v13i2.469

Abstract

Research in the field of transportation, especially vehicle classification with various methods, is a widely developed field of study. Vehicles can be categorized by shape, dimension, logo, and  type. The vehicle dataset is also not difficult to find because it is general in nature. Based on the research that has been done, the introduction of group types based on the number of axles with CNN, the dataset is not yet available to the public. In this paper, we discuss the introduction of the types of groups using the Convolutional Neural Network method. The architecture used is the LeNet model. The trial scenario is carried out in 4 stages, namely 25 epochs, 50 epochs, 75 epochs and 100 epochs. Based on the test results, the accuracy obtained continues to increase at 50 epochs and 100 epochs iterations. Starting from an accuracy of 82%, 94% to the highest accuracy of 95%. Likewise in the prediction the data has increased from 80%, 85% to the highest accuracy that can be 86%. From 50 epochs to 75 epochs, the accuracy of both training and testing has decreased.
Pemilihan Kata Benda Bahasa Indonesia Berdasarkan Cakupan Suku Kata Menggunakan Genetic Algoritma untuk Dataset Audio Visual Eka Rahayu Setyaningsih; Anik Nur Handayani; Wahyu Sakti Gunawan Irianto; Yosi Kristian
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.63970

Abstract

Dalam pembentukan model Kecerdasan Buatan yang menggunakan pendekatan Deep Learning, dataset memegang peranan yang sangat penting. Memahami dan memilih kumpulan data yang tepat, sangatlah penting untuk memastikan keberhasilan sebuah model Kecerdasan Buatan. Salah satu topik yang cukup baru adalah mempelajari bagaimana pembentukan suara dari hasil pembacaan gerakan bibir manusia, dengan cakupan variasi bunyi dan bentuk bibir yang diharapkan dapat membantu pembelajaran sistem. Mayoritas dataset audio visual, yang biasa digunakan untuk pembangunan model pembentukan suara ataupun pembacaan gerakan bibir tidak memperhatikan keluasan cakupan variasi bunyi yang ada. AVID, salah satu dari dataset audio visual berbahasa Indonesia, mengadopsi susunan kata dalam dataset GRID, yang mengubah setiap kata penyusunnya dari Bahasa Inggris ke bahasa Indonesia. Sedangkan pada Bahasa Indonesia sendiri terdapat banyak ragam bunyi yang dibentuk dari satu atau sederet rangkaian fonem. Penelitian yang dilakukan penulis dengan memanfaatkan Genetic Algorithm untuk mendapatkan susunan kombinasi kata benda guna memperoleh nilai cakupan yang optimal. Dengan cakupan kombinasi suku kata yang lebih baik, maka dapat dihasilkan dataset untuk Deep Learning yang lebih baik lagi. Dalam penelitian ini, kata benda yang diproses, diperoleh dari KBBI edisi 2008, baru kemudian difilter untuk mendapatkan kata benda yang tepat mengandung 3 suku kata, yang bukan nama kota, tokoh maupun lokasi. Dari 39.070 kata benda yang ada, diperoleh 2936 kata benda yang akan digunakan. Ujicoba yang telah dilakukan pada 10.000 hingga 200.000 epoch, diperoleh rata-rata cakupan suku kata 72%-75% dengan batasan 26 variasi kata benda penyusunnya.
Pengenalan Varietas Ikan Koi Berdasarkan Foto Menggunakan Simple Linear Iterative Clustering Superpixel Segmentation dan Convolutional Neural Andy Hermawan; Ilham Zaeni; Aji Wibawa; Gunawan Gunawan; Yosi Kristian; Shandy Darmawan
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 1 No. 11 (2021)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (533.052 KB)

Abstract

Object segmentation and image recognition are two computer vision tasks which are still being developed until today. Simple Linear Iterative Clustering is an algorithm which is very popular to help with object segmentation tasks because it is the best in terms of result and speed. In image recognition, Convolutional Neural Networks are also one of the best approaches for any kind of recognition tasks because of their efficiency and the ability to recognize objects like animals do. Koi fish have become a very interesting object to be researched because they are difficult to segment and distinguished between their varieties. The dataset consists of 600 images of Koi fish from 10 different varieties. The Koi fish’s recognition process begins with generating super pixels for the input image. The next step is to merge all neighborhood super pixels by their color similarities. After this step, almost all the background pixels should be detected so that the actual object, the Koi fish, can be segmented. The segmented image is then given to a Convolutional Neural Networks, to learn any important features which distinguish every Koi fish variety from one another. A trained Convolutional Neural Networks can then give a Koi fish variety prediction for an input image. Based on a series of segmentation and model tests performed, it is proven that the segmentation technique, which uses Simple Linear Iterative Clustering in this project, performs exceptionally well across almost all the images in the dataset. The model produced from this project is also able to classify a wide range of Koi fish varieties accurately at 90 percent accuracy with segmentation and 87 percent without segmentation. Segmentasi dan pengenalan objek pada gambar masih merupakan dua buah masalah pada computer vision yang masih terus diteliti dan dikembangkan hingga saat ini. Simple Linear Iterative Clustering merupakan salah satu algoritma segmentasi superpixel yang cukup populer untuk membantu melakukan segmentasi objek karena memiliki hasil superpixel yang baik dan dapat berjalan dengan cepat. Untuk pengenalan objek, Convolutional Neural Networks masih merupakan salah satu yang terbaik untuk berbagai masalah karena efisien dan mampu mengenali objek pada gambar layaknya hewan mengenali objek yang dilihatnya. Ikan koi menjadi sebuah objek yang menarik untuk diteliti karena sulit untuk disegmentasi dan dikenali jenisnya bahkan oleh manusia. Dataset yang digunakan berisi 600 gambar yang terdiri dari 10 varietas ikan koi. Pengenalan ikan koi diawali dengan melakukan generate superpixel pada gambar input, kemudian menggabungkan superpixel-superpixel terdekat yang memiliki warna yang mirip. Dengan cara ini, maka hampir seluruh pixel background dapat dideteksi sehingga objek ikan koi dapat disegmentasi. Gambar hasil segmentasi kemudian dilatihkan ke Convolutional Neural Networks yang akan mempelajari fitur-fitur penting pada setiap jenis ikan koi yang diteliti. Convolutional Neural Networks yang telah dilatih kemudian dapat memberikan prediksi varietas ikan koi dari sebuah input gambar. Berdasarkan hasil uji coba segmentasi dan model yang digunakan, dibuktikan bahwa teknik segmentasi yang memanfaatkan Simple Linear Iterative Clustering yang dilakukan berhasil untuk hampir seluruh gambar pada dataset. Model yang dibuat mampu mengklasifikasikan varietas ikan koi dengan akurasi 90 persen dengan segmentasi dan 87 persen tanpa segmentasi.
Klasifikasi Penyakit Tanaman Cabai Rawit Dilengkapi Dengan Segmentasi Citra Daun dan Buah Menggunakan Yolo v7 Masrur Anwar; Yosi Kristian; Endang Setyati
INTECOMS: Journal of Information Technology and Computer Science Vol 6 No 1 (2023): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v6i1.6071

Abstract

Diseases that attack chili plants can be diagnosed early by observing symptoms or changes that occur in the leaves and fruit of the chili plant. However, diseases or pests that attack chili plants within a single plant can vary. In this study, YOLO v7 was used to perform leaf and chili segmentation on images, and the segmented results were then classified for chili plant disease using Deep Convolutional Neural Network (DCNN) Transfer Learning with the Fine Tuning method. The test results of the constructed model showed that the Yolo v7 segmentation accuracy was 0.970 on mAP50 when performing chili plant leaf and fruit segmentation. For the DCNN model testing with transfer learning method using the EfficientNetV2M based model, an accuracy value of 0.912 was obtained for leaf disease classification and an accuracy of 0.889 was obtained for chili fruit classification. Keyword: Chili Plant Diseases; Classification; Transfer Learning, Yolo v7 segmentation
System of gender identification and age estimation from radiography: a review Nur Nafi’iyah; Chastine Fatichah; Darlis Herumurti; Eha Renwi Astuti; Ramadhan Hardani Putra; Esa Prakasa; Yosi Kristian
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5491-5500

Abstract

Under extreme conditions postmortem, dental radiography examinations can play an essential role in individual identification. In forensic odontology, individual identification traditionally compares antemortem dental records radiographs with those obtained on postmortem examination. As such, these traditional methods are vulnerable to oversights or mistakes in the individual identification of unidentified bodies. Digital technology can develop forensic odontology well. An automatic individual identification system is needed to support the forensic odontology process more easily and quickly because there are still opportunities to be created. We aimed to review the complete range of recent developments in identifying individuals from panoramic radiographs. We study methods in gender identification, age estimation, radiographic segmentation, performance analysis, and promising future directions.
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 Putu Widiarsa Kurniawan S 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