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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Journal of Urban Society´s Arts Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi SMATIKA Journal of Animation & Games Studies EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science Berkala Arkeologi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SENTIA 2015 Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Mnemonic Jurnal Informatika dan Sistem Informasi Journal of Innovation Information Technology and Application (JINITA) JAREE (Journal on Advanced Research in Electrical Engineering) JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Berkala Arkeologi
Eko Mulyanto Yuniarno
Dapartemen Teknik Komputer Iinstitut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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HiroPoseEstimation: A Dataset of Pose Estimation for Kid-Size Humanoid Robot Rafly Azmi Ulya, Amik; Hutama Harsono, Nathanael; Mulyanto Yuniarno, Eko; Hery Purnomo, Mauridhi
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383568

Abstract

Pose estimation is a field of computer vision research that involves detecting, associating, and tracking data points on body parts. It is used for health monitoring, sign language understanding, human gesture control, elderly activities, sports, and humanoid robot pose estimation. The anatomy of a humanoid robot is similar to a human, which forms the basis for utilizing humanoid robot pose estimation. The Humanoid League is a major domain of the RoboCup competition, featuring soccer matches between humanoid robots. Pose estimation is used to measure the robot’s performance. Nevertheless, there have not been many research done on this subject. A new dataset model needs to be developed to solve the proposed problem. This work introduces HiroPoseEstimation, a kid-size humanoid robot dataset with several types of robots used in various poses based on movements in a soccer game. It is evaluated with both bottomup and top-down approaches using keypoint mask R-CNN and single-stage encoder-decoder model. Both methods demonstrate good performance on the proposed dataset.
Facial Movement Recognition Using CNN-BiLSTM in Vowel for Bahasa Indonesia Rahman, Muhammad Daffa Abiyyu; Wicaksono, Alif Aditya; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 1 (2024): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i1.372

Abstract

Speaking is a multimodal phenomenon that has both verbal and non-verbal cues. One of the non-verbal cues in speaking is the facial movement of the subject, which can be used to find the letter being spoken by the subject. Previous research has been done to prove that lip movement can translate to vowels for Bahasa Indonesia, but detecting the whole facial movement is yet to be covered. This research aimed to establish a CNN-BiLSTM model that can learn spoken vowels by reading the subject's facial movements. The CNN-BiLSTM model yielded a 98.66% validation accuracy, with over 94% accuracy for all five vowels. The model is also capable of recognizing whether the subject is currently silent or speaking a vowel with 98.07% accuracy.
Rancang Bangun Lingkungan IoT Sistem Pemantauan dan Otomatisasi Rumah Kaca Melalui Saluran BLE dan WiFi Nugroho, Vidityar Adith; Yuniarno, Eko Mulyanto; Fandiantoro, Dion Hayu; Pramunanto, Eko
Jurnal Teknik ITS Vol 12, No 2 (2023)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v12i2.115110

Abstract

Di era modern revolusi industri 4.0 saat ini, hampir semua teknologi informasi digunakan untuk memudahkan aktivitas manusia. Permasalahan perkebunan saat ini adalah semakin menyempitnya komoditas tanaman yang dapat ditanam akibat cuaca ekstrim dan sempitnya lahan akibat pembangunan perumahan dan industri. Untuk mengatasi masalah ini, telah dikembangkan sistem pemantauan rumah kaca, dengan tujuan meningkatkan produktivitas tanaman. Sebelumnya pengembangan rumah kaca masih terbatas pada satu sensor dan menggunakan protokol HTTP. Oleh karena itu, pada artikel ini dikembangkan sistem monitoring kondisi rumah kaca dengan menyusun beberapa sensor yang kemudian disebut node sensor dan menempatkan node sensor pada titik tertentu. Node sensor kemudian akan mengirimkan datanya ke sink node menggunakan Bluetooth Low Energy. Kemudian data yang telah digabungkan pada sink node tersebut diteruskan oleh sensor node lain ke broker MQTT menggunakan konektivitas WiFi. Data tersebut kemudian akan di subscribe untuk diolah dan disimpan dalam database. Lalu aplikasi website akan menampilkan data pembacaan sensor sesuai dengan yang tersimpan di database. Dalam aplikasi website terdapat fitur untuk menyesuaikan parameter lingkungan rumah kaca. Data dari threshold yang telah diatur selanjutnya dikirimkan ke node aktuaktor. Node aktuaktor bertugas untuk membandingkan data dari pembacaan sensor dan juga data threshold agar dapat menjalankan beberapa aktuaktor sesuai dengan kebutuhannya.
Clustering Titik Fitur Model Wajah 3D Menggunakan K-Nearest Neighbour Nendya, Matahari Bhakti; Yuniarno, Eko Mulyanto; Sumpeno, Surya
Jurnal Informatika dan Sistem Informasi Vol. 7 No. 1 (2021): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

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

Abstract

The first step in the process of transferring animation using motion capture data to a 3D face model is to determine the facial feature points and the relationship between these points to form a 3D facial model motion system. This study focuses on grouping facial feature points where 33 centroids have been determined and looking for their association with other feature points. The 3D face model used is a humanoid character face model which is similar to a human 3D face model. The results obtained are the distribution of facial feature points that will be used as a reference in the mesh deformation process using linear blend skinning. Langkah awal dalam proses transfer animasi menggunakan data motion capture kepada model wajah 3D adalah menentukan titik fitur wajah dan keterkaitan antar titik tersebut supaya membentuk sistem gerak model wajah 3D. Penelitian ini berfokus pada pengelompokan titik fitur wajah dimana sudah ditentukan 33 titik pusat (centroid) dan mencari keterkaitannya dengan titik fitur lainya. Model wajah 3D yang digunakan berupa model wajah karakter humanoid yang mana mempunyai kemiripan dengan model wajah 3D manusia. Hasil yang didapatkan berupa sebaran titik fitur wajah yang akan digunakan sebagai acuan dalam proses mesh deformation menggunakan linear blend skinning
Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle Ulinuha, Masy Ari; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Hariadi, Mochamad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1464

Abstract

The human skull was the subject of study in various fields. Segmentation could be a basic tool for better understanding the skull. One of the most challenging tasks was facial bone segmentation. Our previous study had succeeded in segmenting facial bones from skull point clouds, however the quality of the results needed to be improved. In this paper, we proposed a new method to improve the results of facial bone segmentation from skull point clouds. The method consists of three stages: deviation angle extraction, smoothing, and thresholding. Each point in the point cloud was assigned a value based on the deviation angle. These values then went through a smoothing process to clarify the differences between the facial bone region and other regions. Next, thresholding was performed to divide the skull into two regions, namely facial bone and non-facial bone. The proposed method had succeeded in improving the quality of the segmentation results by achieving precision=0.931, recall=0.9854, and F=0.9573.
Pemanfaatan Teknologi Aquaponic pada Pondok Pesantren sebagai Upaya Pemberdayaan untuk Kemandirian Pondok Pesantren di Turirejo, Lawang, Kab. Malang, Jawa Timur Zaini, Ahmad; Muhtadin; Pramunanto, Eko; Boedinoegroho, Hanny; Setiawan, Rachmad; Kurniawan, Arief; Yuniarno, Eko Mulyanto
Sewagati Vol 8 No 6 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i6.2229

Abstract

Pondok pesantren merupakan lembaga pendidikan nirlaba yang umumnya berbiaya rendah atau bahkan gratis. Operasionalnya bergantung pada donasi masyarakat dan unit usaha yang dimiliki. Namun, kemampuan setiap pesantren berbeda-beda; hanya pesantren besar dengan sejarah panjang yang mampu mandiri dalam memenuhi kebutuhan operasionalnya. Sebaliknya, banyak pesantren kecil menghadapi tantangan finansial untuk tetap menjaga kualitas layanan, karena donasi yang diterima sering kali tidak mencukupi. Dalam program pengabdian masyarakat ini, dilakukan pelatihan pemanfaatan teknologi aquaponic di pesantren sebagai solusi inovatif. Teknologi ini dipilih karena tidak memerlukan lahan yang luas serta mudah dikelola, sehingga cocok untuk memenuhi kebutuhan konsumsi sehari-hari santri, seperti sayuran dan ikan air tawar. Selain itu, kelebihan hasil produksi dapat dijual untuk menambah pendapatan pesantren. Pelatihan ini memberikan dampak positif, terutama dalam meningkatkan keterampilan santri mengelola aquaponic, yang dapat menjadi bekal wirausaha di masa depan. Lebih jauh, hasil panen dari dua kali produksi memberikan kontribusi nyata bagi operasional pesantren, dengan total pendapatan sebesar Rp11.400.000 dalam bentuk in kind dan in cash. Program ini menunjukkan potensi kemandirian finansial bagi pesantren secara berkelanjutan.
The Significance of Dynamic COVID-19 Dashboard in Formulating School Reopening Strategies Muqtadiroh, Feby Artwodini; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki; Pahlawan, Muhammad Reza; Rachmayanti, Riris Diana; Usagawa, Tsuyoshi; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76017

Abstract

Experiments conducted with the COVID-19 dataset have predominantly concentrated on predicting cases fluctuating and classifying lung-related diseases. Nevertheless, the consequences of the COVID-19 pandemic have also spread to the education sector. To safeguard educational stability in response to the remote learning policy, we leverage authentic COVID-19 datasets alongside school information across 154 sub-areas in Surabaya City, Indonesia. Our focus is predicting the dynamic within these sub-areas where schools are located. The outcomes of this study, by incorporating the recurrent neural network of long- and short-term memory (RNN-LSTM) architecture and refined hyperparameters, effectively enhanced the predictive model's performance. The findings are showcased on a dashboard, visually representing the transmission of COVID-19 in schools across each sub-area. This information serves as a basis for informed decisions on the safe reopening of schools, aiming to mitigate the decline in education quality during the challenging pandemic.
Adaptive Threshold Filtering to Reduce Noise in Elderly Activity Classification Using Bi-LSTM Rahayu, Endang Sri; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76064

Abstract

As the global population ages, there is an increasing need to provide better care and support for older individuals. Deep learning support to accurately predict elderly activities is very important to develop. This research discusses a new model integrating filtering techniques using adaptive thresholds with Bidirectional - Long Short-Term Memory (Bi-LSTM) networks. The problem of activity prediction accuracy, mainly due to noise or irrational measurements in the dataset, is solved with adaptive thresholds. Adaptive characteristics at the threshold are needed because each individual has different activity patterns. Experiments using the HAR70+ dataset describe the activity patterns of 15 elderly subjects and the gesture patterns of 7 activities. Based on body movement patterns, the elderly can be classified as using walking aids. The proposed model design obtains an accuracy of 94.71% with a loss of 0.1984.
Sign Language Recognition Based on Geometric Features Using Deep Learning Yuniarno, Eko Mulyanto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.82103

Abstract

Sign language plays a crucial role in facilitating communication among individuals with hearing impairments. In Indonesia, the deaf community often rely on BISINDO (Indonesian Sign Language) to communicate amongst themselves. People who are unfamiliar with sign language will face difficulties. This research aims to develop a system for recognizing sign language using geometric features extracted from hand joint coordinates using Google's MediaPipe Hands framework. The dataset contains 12 common words, each recorded 30 times with 30 frames recorded for each instance. This will facilitate communication between deaf and hearing individuals. We conducted tests on LSTM- Geometric and CNN1D- Geometric models using geometric features, and on CNN-LSTM-Spatial and CNN1D-LSTM-Spatial models using spatial features. The results indicate that the LSTM model with geometric features achieved the highest accuracy of 99%. Geometric features have been shown to be more effective than spatial features for classifying sign language gestures.
Early Detection Depression Based On Action Unit and Eye Gaze Features Using a Multi-Input CNN-WoPL Framework Sugiyanto, Sugiyanto; Purnama, I Ketut Eddy; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.84674

Abstract

Depression is a common mental disorder with significant life impact, including a high risk of suicide. Patients with depression attempt suicide five times more often than the general population. Self-reporting, subjective judgement and clinician expertise influence conventional diagnostic methods. For timely intervention and effective treatment, early and accurate diagnosis of depression is essential. This study proposes a framework called Multi-Input CNN-WoPL, a CNN-based method without a pooling layer that combines two features - action units and gaze - to improve accuracy and robustness in automatic depression detection. Pooling layer reduces spatial dimension of feature map, resulting in loss of information related to expression data, affecting depression detection result. The performance of the proposed method results in an accuracy of 0.994 and F1 score = 0.993, the F1 score value close to 1.0 indicates that the proposed method has good precision, recall and performance.
Co-Authors Aditya Nur Ikhsan Soewidiatmaka Agung Dewa Bagus Soetiono Agung Wicaksono Agustinus Bimo Gumelar Ahmad Zaini Alan Luthfi Ali Sofyan Kholimi Alwali, Muhammad Anang Kukuh Adisusilo Andreas Agung Kristanto, Andreas Agung Ardyono Priyadi Arief Kurniawan Arik Kurniawati Aris Widayati Atyantagratia Vidyasmara Daryanto Bambang Purwantana Beny Yulkurniawan Victorio Nasution Beny Yulkurniawan Victorio Nasution Boedinoegroho, Hanny Citra Ratih Prameswari Diah Puspito Wulandari Endang Setyati Endang Sri Rahayu Enggartiasto Faudi Ristyawan Esther Irawati Setiawan Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fakih, Muhammad Fadli Fandiantoro, Dion Hayu Farah Zakiyah Rahmanti Farodisa, Annida Miftakhul Feby Artwodini Muqtadiroh Fresy Nugroho Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Goenawan A Sambodo Gunawan Gunawan Gunawan Hardianto Wibowo Harfianti, Nadya Putri Herman Thuan Herman Thuan To Saurik Hermawan, Norma Hervit Ananta Vidada Hutama Harsono, Nathanael I Ketut Eddy Purnama I Made Gede Sunarya Imam Robandi Indar Sugiarto Isa Hafidz Ismoyo Sunu Jaya Pranata Joan Santoso Joko Priambodo Khairunnas Khairunnas Koeshardianto, Meidya Kurniawan, Arief Lailatul Husniah Lutfi Ananditya Septiandi Masy Ari Ulinuha Matahari Bhakti Nendya Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Moch. Iskandar Riansyah Mochamad Hariadi Mochamad Yusuf Alsagaff Muhammad Fadli Fakih Muhammad Reza Pahlawan Muhammad Zulfikar Alfathan Rachmatullah Muhtadin Mulyanto, Edy Myrtati Dyah Artaria Nasrulloh, Muhammad Nova Eka Budiyanta Nugroho, Vidityar Adith Oddy Virgantara Putra Pambudi, Sevito Fernanda Pramunanto, Eko Pramunanto, Eko Priambodo, Joko Putu Hendra Suputra R Dimas Adityo Radi Radi Rafly Azmi Ulya, Amik Ragil Bintang Brilyan Rahman, Muhammad Daffa Abiyyu Reza Fuad Rachmadi Rika Rokhana Rika Rokhana Riris Diana Rachmayanti Rokhana, Rika S. Suprapto Saiful Yahya Samuel Gandang Gunanto Sensusiati, Anggraini Dwi Setiawan, Rachmad Setijadi, Eko Soetiono, Agung Dewa Bagus Suciningtyas, Laras Sugiyanto - Sulistyono, Marcelinus Yosep Teguh Supeno M Susiki Nugroho Supeno Mardi Susiki Supeno Mardi Susiki Supeno Mardi Susiki N Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Surya Sumpeno Susiki N, Supeno Mardi Syauqi Sabili Tita Karlita Tita Karlita Tita Karlita Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Willy Achmat Fauzi Wisnu Widiarto Wiwik Anggraeni Yose Rizal Yoyon K. Suprapto Yoyon Kusnendar Suprapto Yuhana, Umi Laili Zaini, Ahmad