Article Per Year (5 Year)
p-Index From 2021 - 2026
2.863
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Journal of Animation & Games Studies Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CYCLOTRON Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAVA Journal of Electrical and Electronics Engineering Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Sewagati
Supeno Mardi Susiki Nugroho, Supeno Mardi
Departemen Teknik Komputer Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Real time face recognition of video surveillance system using haar cascade classifier Adlan Hakim Ahmad; Sharifah Saon; Abd Kadir Mahamad; Cahyo Darujati; Sri Wiwoho Mudjanarko; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1389-1399

Abstract

This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.
Serious game self-regulation using human-like agents to visualize students engagement base on crowd Khothibul Umam; Moch Fachri; Fresy Nugroho; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3780

Abstract

Nowadays, the emergence of artificial intelligent (AI) technology for games has been advancely developed. A serious game is a technology employing AI to create a virtual environment in a serious gamification strategy. This research describes AI based virtual classrooms to adopt proper strategies and focusing on maintaining and increasing student engagement by encouraging self-regulation behavior at the learning process. The self-regulation behavior describes student's ability to direct their own learning to achieve learning targets on a path full of obstacles. By employing a human-like agent to visualize student engagement, this visualization aims to provide human-like experiences for users to comprehend student behavior. A reciprocal velocity obstacles (RVO)-based crowd behavior is employed to visualize student engagement. RVO is an autonomous navigation approach for directing the achievement of agents target. The human-like agents behave in various ways to reach the goal points depending on the performances and the obstacles before them. We employ our method in an investigation of students' learning activities in a pedagogically-centered learning environment at Universitas Islam Negeri (UIN) Walisongo, Semarang, Indonesia. The results demonstrate the best scenario changes along with the performances and obstacles faced to reach the goal points as well as the learning target.
Menghitung Luas Bangun Datar pada Papan Tulis Menggunakan Yolo Alan Luthfi; Eko Mulyanto Yuniarno; Supeno Mardi Susiki Nugroho
Jurnal Teknik ITS Vol 11, No 3 (2022)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v11i3.92620

Abstract

pembelajaran revolusioner kedua setelah papan tulis hitam tradisional, karena papan tulis pintar yang bisa disematkan dalam ruang kelas modern bisa menggerakan sekolah ke arah mode operasi digital yang lebih terintegrasi. Pada papan tulis pintar harus memiliki fitur yang dapat membedakan papan tulis pintar dengan papan tulis biasa, karena papan tulis pintar memiliki fitur-fitur atau kegunaan lebih superior daripada papan tulis biasa. Oleh karena itu diperlukan pengembangan fttur pada papan tulis pintar. Tujuan penelitian adaah membuat program yang dapat mendeteksi bangun datar dan parameternya lalu menghitung luas bangun datar pada papan tulis pintar. Metode yang akan digunakan adalah dengan menggunakan YOLO sebagai framework pengerjaan dalam pembuatan program deteksi bangun datar dan parameternya.
Lightweight pyramid residual features with attention for person re-identification Reza Fuad Rachmadi; I Ketut Eddy Purnama; Supeno Mardi Susiki Nugroho
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.702

Abstract

Person re-identification is one of the problems in the computer vision field that aims to retrieve similar human images in some image collections (or galleries). It is very useful for people searching or tracking in a closed environment (like a mall or building). One of the highlighted things on person re-identification problems is that the model is usually designed only for performance instead of performance and computing power consideration, which is applicable for devices with limited computing power. In this paper, we proposed a lightweight residual network with pyramid attention for person re-identification problems. The lightweight residual network adopted from the residual network (ResNet) model used for CIFAR dataset experiments consists of not more than two million parameters. An additional pyramid features extraction network and attention module are added to the network to improve the classifier's performance. We use CPFE (Context-aware Pyramid Features Extraction) network that utilizes atrous convolution with different dilation rates to extract the pyramid features. In addition, two different attention networks are used for the classifier: channel-wise and spatial-based attention networks. The proposed classifier is tested using widely use Market-1501 and DukeMTMC-reID person re-identification datasets. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed classifier can perform well and outperform the classifier without CPFE and attention networks. Further investigation and ablation study shows that our proposed classifier has higher information density compared with other person re-identification methods.
Penerapan E-Commerce untuk Penguatan UMKM Berbasis Konsep One Village One Product di Kabupaten Karangasem I Ketut Eddy Purnama; Putu Gde Ariastita; Ketut Dewi Martha Erli Handayeni; Supeno Mardi Susiki Nugroho
Sewagati Vol 2 No 2 (2018)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.77 KB)

Abstract

Pendekatan OVOP (One Village One Product) merupakan konsep dalam pengembangan ekonomi wilayah yang mengarahkan suatu wilayah/desa mampu menciptakan suatu produk khas yang bercirikan lokal dengan memanfaatkan segala potensi sumberdaya lokal yang dimilikinya serta berdaya saing global. Dalam mendorong implementasi OVOP di Indonesia, kebijakan dan program yang mendukungnya ditetapkan oleh Kementerian Perindustrian sejak tahun 2008. Pendekatan OVOP mengandalkan peran usaha mikro, kecil, dan menengah (UMKM) serta koperasi sebagai ujung tombak dalam menghasilkan produk unggulan desa/wilayah. Namun, hingga kini implementasi OVOP di daerah masih sering menemukan kendala, terutama pada penguatan kapasitas pelaku usaha dalam menciptakan produk unggul yang berdaya saing global. Pada tahun 2017 telah dilaksanakan kegiatan pengabdian masayrakat melalui identifikasi komoditas unggulan dengan pendekatan OVOP di Kabupaten Karangasem. Hasilnya adalah terdapat 11 jenis produk unggulan yang diproduksi oleh UMKM yang tersebar di 8 kecamatan yang hanya memiliki skala pemasaran sebagian lokal. Kegiatan perekonomian di Kabupaten Karangasem didominasi oleh kegiatan UMKM ini dengan karakteristik modal kecil, tenaga kerja sedikit, manajemen/pengelolaan yang sederhana, serta teknologi yang juga masih sederhana. Sektor ini mampu menyerap tenaga kerja sebanyak 27.709 orang. Peran UMKM ini strategis sebagai penopang perekonomian wilayah dan kesejahteraan masyarakat Kabupaten Karangasem. Namun, kendala promosi dan strategi pemasaran produk menjadi hambatan utama sektor UMKM ini. Oleh karena itu, perlunya penguatan sektor UMKM terutama dalam hal pemasaran produk melalui penerapan E-Commerce. E-Commerce adalah platform aktifitas jual-beli barang atau jasa melalui komputer dan jaringan internet. Penerapan e-commerce dilakukan di banyak negara-negara berkembang dan berhasil meningkatkan produktifitas, menjangkau pasar lebih luas, dan kesempatan penjualan yang lebih baik sehingga juga meningkatkan daya saing global. Implementasi OVOP di Karangasem dapat diperkuat dengan sebuah platform Information and Communication Technology (ICT) berupa e-commerce atau perdagangan elektronik. Beberapa manfaat e-commerce diantaranya dapat menambah jangkauan pemasaran dan jumlah konsumen, mempermudah promosi produk, mengurangi rantai distribusi pemasaran barang, mempermudah interaksi antara penjual dan pembeli termasuk mendapat saran dari pembeli, dan pada akhirnya dapat meningkatkan produksi. Dengan penerapan e-commerce, maka sektor UMKM diharapkan dapat mengaplikasikan bisnis/usaha yang berkelanjutan dan bersaing secara global berdasarkan prinsip-prinsip OVOP.
Klasifikasi Citra Satelit menggunakan Lightweight Ensemble Convolutional Network Rachmadi, Reza Fuad; Prioko, Kentani Langgalih; Nugroho, Supeno Mardi Susiki; Purnama, I Ketut Eddy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14346

Abstract

Citra satelit dapat digunakan salah satunya sebagai pengamatan kondisi atmosfer dan permukaan pada bumi. Dengan semakin berkembangnya teknologi citra satelit, waktu untuk pengambilan citra satelit menjadi lebih efisien. Makalah ini melakukan eksperimen menggunakan klasifier ensemble convolutional network untuk melakukan pengenalan kondisi atmosfer pada citra satelit. Empat buah arsitektur Convolutional Neural Network (CNN) digunakan dalam eksperimen ini, yaitu MobileNetV2, ResNet18, ResNet18Half, dan SqueezeNet. Keempat arsitektur CNN tersebut dipilih karena mempunyai jumlah parameter yang tidak terlalu besar (lightweight) serta dapat diterapkan pada banyak perangkat keras tertanam. Eksperimen yang dilakukan dengan menggunakan dataset USTC SmokeRS memperlihatkan bahwa klasifier ensemble memperoleh hasil yang baik dengan akurasi rata-rata tertinggi sebesar 97.06 %.
Crowd navigation for dynamic hazard avoidance in evacuation using emotional reciprocal velocity obstacles Fachri, Moch; Prasetyo, Didit; Damastuti, Fardani Annisa; Ramadhani, Nugrahardi; Susiki Nugroho, Supeno Mardi; Hariadi, Mochamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1371-1379

Abstract

Crowd evacuation can be a challenging task, especially in emergency situations involving dynamically moving hazards. Effective obstacle avoidance is crucial for successful crowd evacuation, particularly in scenarios involving dynamic hazards such as natural or man-made disasters. In this paper, we propose a novel application of the emotional reciprocal velocity obstacles (ERVO) method for obstacle avoidance in dynamic hazard scenarios. ERVO is an established method that incorporates agent emotions and obstacle avoidance to produce more efficient and effective crowd navigation. Our approach improves on previous research by using ERVO to model the perceptive danger posed by dynamic hazards in real-time, which is crucial for rapid response in emergency situations. We conducted experiments to evaluate our approach and compared our results with other velocity obstacle methods. Our findings demonstrate that our approach is able to improve agent coordination, reduce congestion, and produce superior avoidance behavior. Our study shows that incorporating emotional reciprocity into obstacle avoidance can enhance crowd behavior in dynamic hazard scenarios.
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.
Pencak Silat Movement Classification Using CNN Based On Body Pose Rahmawati, Vira Nur; Yuniarto, Eko Mulyanto; Nugroho, Supeno Mardi Susiki
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 7, No 2 (2023): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v7i2.369

Abstract

Pencak silat, besides from being useful for self-protection, also has many other benefits, such as increasing physical strength, maintaining posture, and maintaining heart health. Due to the recent pandemic, practicing pencak silat is difficult to do together. Even when there is study material on pencak silat at school, it is difficult for the sports teacher to teach the movements directly. Pencak silat exercises that are practiced alone without a coach can cause injury if the movements are not correct. Therefore, this study builds a system to recognize pencak silat movements. The system was built using the bodypose-based CNN method. Bodypose estimation is used to detect human body keypoints, then these keypoints are used as a feature for input to CNN to recognize movement in each frame. This system uses CNN because it requires fewer parameters and less computing power so that it can be more easily applied for further studies. The accuracy obtained reaches 77% when tested on data that has never been used. This model can be used as a starting point for creating an easy-to-use system to help people practice pencak silat with more recognizable moves.
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.
Co-Authors Abd Kadir Mahamad Achmad Affandi Adhi Dharma Wibawa Aditiya, Fajar Adlan Hakim Ahmad Ahmad Dwi Arianto Ahmad Dwi Arianto Ahmad Zaini Aji Prasetya Wibawa Alan Luthfi Alfonsus Haryo Sangaji Anang Kukuh Adisusilo Arianto, Ahmad Dwi Binti Umayah Cahyo Darujati Cahyo Darujati Citra Ratih Prameswari Damastuti, Fardani Annisa Deny Kurniawan Eko Mulyanto Yuniarno Endroyono Endroyono Endroyono, E Fachri, Moch Fachrul Kurniawan Fachrul Kurniawan Feby Artwodini Muqtadiroh Fresy Nugroho Fresy Nugroho Handayeni, Ketut Dewi Martha Erli Hervit Ananta Vidada I Ketut Eddy Purnama I Ketut Eddy Purnama Jihad Hammad Khothibul Umam Khrisna Aprianto Koesoemaningroem, Novi Kurniawan, Fachrul Lukman Hakim Mauridhi H Purnomo Mauridhi Hery Purnomo Maydlinne Liudyvia Moch Fachri Mochamad Hariadi Mochammad Hariadi Mochammad Hariadi Muhammad Reza Pahlawan Muhammad Rifqi Mukhtar Muharman Lubis Munir Munir Nugrahardi Ramadhani Prasetyo, Didit Prioko, Kentani Langgalih Putu Gde Ariastita Putu Gde Ariastita Ariastita Rachmadi, Reza Fuad Rahman, Muhammad Daffa Abiyyu Rahmat Fauzi Rahmawati, Vira Nur Reza Fuad Rachmadi Riris Diana Rachmayanti Sartana, Bruri Trya Sharifah Saon Sri Wiwoho Mudjanarko, Sri Wiwoho Surya Sumpeno Susi Juniastuti Syahri Mu'min Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Yuhana, Umi Laili Yuniarto, Eko Mulyanto Yunifa Miftachul Arif Yuri Pamungkas Zaini, Ahmad