Article Per Year (5 Year)
p-Index From 2021 - 2026
14.98
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal technoscientia Jurnal Mahasiswa TEUB Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJID (International Journal on Informatics for Development) Indonesian Journal of Electrical Engineering and Computer Science JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Mechanical Engineering for Society and Industry
Utaminingrum, Fitri
Fakultas Ilmu Komputer, Universitas Brawijaya
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

Published : 193 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 193 Documents
Search

 11   12   13   14   15 
Sistem Guided Following Control pada Smart Wheelchair menggunakan Metode Yolov5 berbasis Nvidia Jetson TX2 Alfianto Palebangan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Smart wheelchairs are becoming increasingly popular for individuals with mobility impairments, allowing them to navigate their environment with more independence and comfort. However, one challenge faced by smart wheelchairs is the ability to follow a designated guide, such as a human or guide rope. In this abstract, the researcher proposes the use of the YOLOv5 (You Only Look Once version 5) method to enable smart wheelchairs to follow a guide. YOLOv5 is a fast and accurate object detection algorithm, making it suitable for real-time applications such as guiding a smart wheelchair. By using YOLOv5 to detect and track a designated guide, the smart wheelchair can smoothly and responsively follow the guide, allowing the user to easily navigate their environment. To demonstrate the effectiveness of the research approach, the researcher conducted a series of experiments on a smart wheelchair equipped with a camera and a YOLOv5-based guide following system. The research results showed that the smart wheelchair was able to accurately follow the designated guide. This study demonstrates the potential of YOLOv5 for guiding a smart wheelchair and the researcher believes that this approach has the potential to enhance mobility for wheelchair users in their daily activities.
Sistem Voice Command pada Kursi Roda Pintar menggunakan MFCC dan CNN berbasis Jetson TX2 Tobias Sion Julian; Fitri Utaminingrum; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Paralysis is a condition in which the movement of a body part is impaired to the point where it is unable to move partially or completely. People with paralysis often need assistive devices to help with their mobility, such as wheelchairs. Smart wheelchairs that use a voice control system can help people who are unable to use their hands to control a wheelchair. This system uses the MFCC feature extraction method, which is the method that most closely approximates human hearing, and the CNN classification method, which has been proven to work well when trained with features extracted using MFCC. The system is run on a Jetson TX2 device and controls the wheelchair using an Arduino Uno by adjusting the pulse width modulation value according to the classification result of the system's command. The dataset used to train the CNN model is the speech_command v2 dataset created by Tensorflow, which contains over 500,000 data for 36 classes. In this research, however, 15,000 data were used for 4 command classes: "Go," "Right," "Left," and "Stop." The results of the system testing show an accuracy prediction value of 85% with a relatively fast average computation time of only 0.37 seconds to make a prediction.
Sistem Pengenalan Gerak Kepala sebagai Navigasi Kursi Roda Pintar dengan menggunakan Metode YOLOV5 berbasis TX2 Riyandi Banovbi Putera Irsal; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

According to data from the 2019 National Socio-Economic Survey (Susenas), there are 26 million people in Indonesia with disabilities or around 9.7% of the total Indonesian population. People with physical disabilities who have difficulty walking require a wheelchair to move around. Some causes of physical disabilities include multiple disabilities in the legs and arms, accidents, quadriplegia, and stroke. An automated wheelchair is a solution to this problem, which is controlled without physical touch and uses the YOLO (You Only Look Once) object detection algorithm to detect the direction of motion of the user's head. The results of several epochs conducted show that YoloV5 small has an f1 score of 0.9 and a smaller loss, so it will be used in the next test. The system created is able to move according to the input given, with a computation time using CUDA of around 65 milliseconds which is relatively fast. Lower power usage and faster computation time are also evident when using CUDA, although there is a discrepancy in the CPU utilization values of cores 2 and 3 which stay at 0%.
Navigasi Menu Berdasarkan Arah Pandangan Mata pada Kursi Roda Pintar menggunakkan Fusion-CNN berbasis Jetson TX2 Blessius Sheldo Putra Laksono; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

As time progresses, technology also experiences rapid advancement. However, this advancement is not accompanied by an improvement in the interaction between computers and humans. This is certainly a significant impact on users of technology, particularly those with mobility disabilities. Smart wheelchair technology has already been widely used to assist people with disabilities, but the problem of interaction with computers still reduces user comfort. Some new types of interaction have been offered in previous research, including using voice, but this method is considered less effective due to the need for a minimally noisy environment. Another offered method of interaction is using eye gaze estimation using conventional algorithms or machine learning. For this, conventional algorithms are considered less effective due to their low adaptability with different users. CNN-based algorithms are chosen in this research because of their ability to extract features from images, allowing the algorithm to adapt to new data. The accuracy of the model in this research was 96% with a loss of 0.02 during the training phase. The system can run the algorithm in 0.16 seconds using CUDA acceleration. The system only uses 12 watts of electricity, making it possible to run the system using a battery. From the testing that was carried out and the results obtained, it can be concluded that the system runs well to estimate the direction of the user.
Sistem Deteksi Halangan Arsitektural pada Kendali Kursi Roda Pintar menggunakan HOG dan ANN berbasis TX2 Muhammad Ibrahim Kumail; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

People with disabilities in Malang City reached 687 of the population based on data from the Badan Pusat Statistik (BPS) Malang in 2020 data. The disabled person with quadriplegia is a condition with limitations to walking normally, so a wheelchair is needed. The development of electric wheelchairs is one of the focuses of developing this research to be able to walk automatically because, under certain conditions, disabled people have limited arms that cannot control a wheelchair independently. However, an intelligent wheelchair safety feature is needed against the existing architectural obstacles in public facilities. This study applies digital image processing using the Histogram of Oriented Gradient (HOG) method as a feature extraction method to obtain special features on objects and the Artificial Neural Network (ANN) method as an object class classification, with the implementation of image recording around the research site, the results of obstacle detection are obtained. Architectural design with an average accuracy of 79.72% in conditions of recording distance of 4m, 3m, and 2m detection of pillar objects, while at the duplicate recording distance detection of stairs objects get 73.89% and if the condition is detected as an obstacle object, then the condition of the chair the wheels change to a stop slowly with the PWM value decreasing in decrement to 0 to ensure the intelligent wheelchair avoids obstruction.
Rancang Bangun Sistem Klasifikasi Kualitas Minyak Goreng Dengan Parameter Kecerahan Dan Warna Menggunakan Metode Random Forest Dzakwan Daffa Ramdhana; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Cooking oil is also a raw material that many Indonesians use to cook various types of processed food. Oil has many functions for the human body, including as a source and solvent for vitamins A, E, K, and D, as well as a more effective source of energy when compared to carbohydrates and protein. In society, the oil that is often used is packaged oil and used cooking oil. The use of cooking oil is increasing, causing people to save money by using used cooking oil. The use of oil repeatedly causes quality damage and is very dangerous for human health, one of the diseases is carcinoma, which is cancer cells or malignant tumors of epithelial cells. This happens because oil that is used repeatedly will make peroxide compounds increase in the oil content. The higher the peroxide number, the more concentrated the liquid. There are various ways of testing to determine the quality of oil. First, physical testing methods, one of which is the water content in oil. Then chemically, one of which is the determination of the peroxide number. In addition, physically it can also be seen through the brightness and color of the oil. The system design in this study uses a TCS3200 sensor and a Light Dependant Resistor (LDR) sensor used to measure the color and brightness of the oil respectively. The classification results in the form of less feasible and feasible classes can be seen on the LCD and serial monitor. There are 8 test data from 25 available oil datasets. From the 8 test data, the accuracy of random forest classification is 87.5% and the average computation time is 26.9ms.
Sistem Identifikasi Label Ruangan menggunakan MobileNet SSD dan OCR berbasis Raspberry Pi 3B+ dan Intel Neural Compute Stick 2 Zhuliand Rachman; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

A blind person generally has limitations in visual sensing and uses aids such as a cane to assist their mobility but still has obstacles, especially when they are in a building and want to find the room they are aiming for. Room labels that are usually printed and cannot be recognized by a blind person can be identified with the help of a portable device that performs computing in the field of computer vision. By relying on the MobileNet SSD algorithm, which can detect the presence of room labels with a fast computation, and Optical Character Recognition (OCR) which can recognize the name of the room label, users can hear the name of the room spoken through the speakers. In short, the system converts visual information into audio information that a blind person can receive. Even though the primary processor is an edge device such as the Raspberry Pi 3B+, an additional Intel Neural Compute Stick 2 accelerator device can help the detection process go faster because the detection algorithm involves a computationally intensive deep neural network. Based on the tests carried out in this study, the room label detection test using MobileNet SSD resulted in an accuracy rate of 80% with an average computation time of 68.44 ms. While for recognition using OCR, it produced an accuracy value of 93.65% with an average computation time of 263.05 ms. In addition, the integration results based on digital image input with sound output obtained an accuracy rate of 50% because the sound is only pronounced if the recognition results match the name of the existing room label.
Sistem Deteksi Permukaan Jalan pada Kursi Roda Pintar dengan Metode MobileNetV2 Muhammad Arga Farrel Arkaan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

The problem of wheelchairs is becoming more serious now as many disabled people need wheelchairs for mobility. The varied condition of the road surface in Indonesia can cause discomfort and the risk of accidents for wheelchair users. Wheelchair users currently have to control speed manually, but the Image Recognition feature for automating electric motor speed can improve comfort and reduce the risk of accidents. Therefore, the proposed solution is to develop a smart wheelchair system that uses the MobileNetV2 Image Recognition method to control electric motor speed according to the condition of the road surface. The developed smart wheelchair system is able to identify the type of road surface traveled using the MobileNetV2 method and adjust the wheel's speed according to the needs. The results of the testing of this system in the form of percentage of prediction accuracy percentage of 97% with a computing time of 0.24-0.25 seconds.
Deteksi Plat Nama Ruangan untuk Kendali Kursi Roda Pintar menggunakan YOLOv5 dan EasyOCR berbasis TX2 Muhammad Fadhel Haidar; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Disability in Indonesia reaches 5% of the Indoensian population, including physically and visually impaired people. Due to the limited mobility experienced by people with disabilities, it is necessary to develop assistive devices that can helmp people with disabilities in their activities. Eleectric wheelchair has the advantages of being easy to control and maneuver, theri ability to go over slopes and ease to transport to realitively distant places. This study aims to develop a system that can be used to automatically detect room name plate on a smart wheelchair. In this system, the object of the room name plate captured by the camera will be the target of the automatic movement of the wheel chair. the captured camera object will be processed using the YOLOv5 and EasyOCR. this study can prove that the accuracy for the name plate detection is 60% and 100% accuracy for the character recognition. This allows the integration ability of the object detection and room character recognition systems in the autonomous system to trigger the PWM (Pulse WIth Modulation) value, which can be interpreted as if the system cannot recognize the character of the room name plate, resulting in the wheelchair continuing to move forward until it;s able to ding the desired room name.
Sistem Pengenalan Plat Nomor Kendaraan untuk Akses Perumahan menggunakan YOLOv5 dan Pytesseract berbasis Jetson Nano Muhammad Rizky Rais; Fitri Utaminingrum; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Security is important for housing. In general, housing now uses rfid to enter through the gate. In addition to the use of rfid, there are human workers who help secure access to housing. However, access to housing using rfid and human labor has the disadvantage that rfid can be lost and human labor can feel tired. With the existing problems, a system is needed that can help cover these weaknesses. A vehicle license plate detection system was created for residential residents using the YOLOv5 and jetson nano-based pytesseract methods. YOLOv5 (You Only Look Once) is a fast and accurate new object detection algorithm that is suitable for real-time applications. This system will recognize the vehicle number plates of residential occupants starting from training data and license plate detection using YOLOv5 and then, when the vehicle number plate has been detected, the contents of the number plate will be read using the pytesseract ocr (optical character recognition) method so that it can open the housing access door latch. . This recognition system uses training data of 502 images of vehicle license plates. The result of this test is the movement of the servo motor for housing access when the number plate is detected in the data file. The accuracy obtained in this study was 100% for the number plate detection system and 100% for the identification of vehicle occupants' vehicle license plates.
Co-Authors Abadi, Dendy Satria Abiyyu Herwanto Achmad Dinda Basofi Sudirman Achmad Jafar Al Kadafi Adam Ibrahim, Muhammad Adharul Muttaqin Adinugroho, Sigit Aditia Reza Nugraha Afdy Clinton Afrizal Rivaldi, Afrizal Agung Setia Budi Agung Setia Budi Agung Setia Budi, Agung Setia Agus Wahyu Widodo Ahmad Wali Satria Bahari Johan Ahmad Wildan Farras Mumtaz Ainandafiq Muhammad Alqadri Akbar Dicky Purwanto Akbar Wira Bramantya Akbar, Muhammad Danar Al Amin, Nisrina Fairuz Hafizhah Al Huda, Fais Alfan Rafi'uddin Ardhani Alfianto Palebangan Alhamdi, Achmad Fahri Aliffandi Purnama Putra Alrynto Alrynto Alvin Evaldo Darmawan Amalia Septi Mulyani Amaliah, Ichlasuning Diah Andika Bayhaki Al Rasyid Syah Andika Kalvin Simarmata Andrika Wahyu Wicaksono Anugrah Zeputra Arthur Ahmad Fauzi Asep Ranta Munajat Asfar Triyadi Audrey Athallah Asyam Fauzan Aufa Nizar Faiz Auliya Firdaus Awalina, Aisyah Bagas Nur Rahman Bagus Septian Aditya Wijayanto Barlian Henryranu Prasetio Beryl Labique Ahmadie Blessius Sheldo Putra Laksono Budi Atmoko Burhan, M.Shochibul Cahyo, Muhammad Pandu Dwi Candra, Alvin Choirul Huda Constantius Leonardo Pratama Dahnial Syauqy Danudoro, Kevin Daris Muhammad Yafi Desy Marinda Oktavia Sitinjak Dewi Amalia Dharmatirta, Brian Aditya Dimas Rizqi Firmansyah Dony Satrio Wibowo Duwi Purnama Sidik Dzakwan Daffa Ramdhana Eko Sakti Pramukantoro, Eko Sakti Eko Setiawan Eko Setiawan Enny Trisnawati, Enny Ervin Yohannes Ester Nadya Fiorentina Lumban Gaol Faris Chandra Febrianto Farrassy, Muhtady Fatwa Ramdani, Fatwa Fernando, Leo Luis Figo Ramadhan Hendri Fikri, Aqil Dzakwanul Fitra Abdurrachman Bachtiar Fitrahadi Surya Dharma Fitria Indriani Fitriyah, Hurriyatul Fitriyani, Rahma Nur Gabe Siringoringo Gagana Ghifary Ilham Gembong Edhi Setyawan Guruh Adi Purnomo Haikal, M. Fikri Hassadiqin, Hasbi Hendry Y. Nanlohy Herman Tolle Hernanda Agung Saputra Hilman Syihan Ghifari Hilmy Bahy Hakim Hisdianton, Oktavian Huda Ahmad Hidayatullah Hurmuzi, Abdan Idza Hurriyatul Fitriyah Ichsan Ali Rachimi Ida Yusnilawati Ikhsan Rahmad Ilham Imam Cholissodin Imam Faris Intan Fatmawati Irnayanti Dwi Kusuma Irsal, Riyandi Banovbi Putera Issa Arwani Jawahir, Asma Kamilah Nur Joan Chandra Kustijono Juniman Arief Kabisat, Aldiansyah Satrio Kelvin Himawan Eka Maulana Kezia Amelia Putri Kirana Sekar Ayu Kohichi Ogata, Kohichi Krisna Pinasthika Lailil Muflikhah Laksono Trisnantoro Laksono, Blessius Sheldo Putra Larasati, Anindya Zulva Leina Alimi Zain Lilo Nofrizal Akbar Linda Silvya Putri Lita Nur Fitriani LUTHFATUN NISA M. Ali Fauzi M. Fiqhi Hidayatulah M.Shochibul Burhan Marianingsih, susi Marsha Nur Shafira Masyita Lionirahmada Maulana Yusuf Meidiana Adinda Prasanty Mela Tri Audina Misran Misran Mochammad Bustanul Ilmi Mochammad Hannats Hanafi Ichsan Mohammad Andy Purwanto Mohammad Isya Alfian Mohammad Sezar Nusti Ilhami Muchlas Muchlas Mufita, Aulia Riza Muhadzdzib, Naufal Muhamad Fauzan Alfiandi Muhammad Amin Nurdin Muhammad Arga Farrel Arkaan Muhammad Fadhel Haidar Muhammad Hafid Khoirul Muhammad Ibrahim Kumail Muhammad Nazrenda Ramadhan Muhammad Rafi Zaman Muhammad Raihan Wardana Budiarto Muhammad Rizky Rais Muhammad Tri Buwana Zulfikar Ardi Muhammad Wafi Muzammilatul Jamiilah Nico Dian Nugraha Niko Aji Nugroho Noza Trisnasari Alqoria Nugraheny Wahyu Try Nyoman Kresna Aditya Wiraatmaja Olivia Rumiris Sitanggang Onky Soerya Nugroho Utomo Paulus Ojak Parasian Permana, Frihandhika Pratama, Aimar Abimayu Pratama, Wildan Bagus Priyanpadma, Sulthon Purboningrum, Fadhila Putera, Muhammad Reza Dahri Putra Pandu Adikara Putra, Firnanda Al Islama Achyunda Putra, Reza Qonita Luthfiyani Qurrotul A'yun Rachmad Jibril Al Kautsar Rahma Tiara Puteri Rahmatul Bijak Nur Kholis Rahmawati, Athirah Naura Rakhmadina Noviyanti, Rakhmadina Ramadhani, Roihaan Randy Cahya Wihandika Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renaldi Primaswara Praetya Renita Leluxy Sofiana Rhaka Gemilang Sentosa Ringga Aulia Primahayu Riyandi Banovbi Putera Irsal Rizal Maulana Rizal Maulana, Rizal Rizdania, Rizdania Rizka Husnun Zakiyyah Rizky Haris Risaldi Rizky Teguh Nursetyawan Rizky Yuztiawan, Fachrie Robbani, Ihwanudien Hasan Rochmawanti, Ovy Samuel Andika Sasongko, Listyawan Dwi Shaleh, Achmad Rizqi Ilham Shih, Timothy K. Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siswanti Slamet Arifmawan Sri Mayena Surga, Itsar Irsyada Syahrul Yoga Pradana Syaifuddin, Tio Tiara Sri Mulati Tibyani Tibyani Tibyani Tobias Sion Julian Tsani, Farid Nafis Versa Christian Wijaya Vikorian, Eldad Virza Audy Ervanda Wahyu Adi Prijono Wayan Firdaus Mahmudy Widasari, Edita Rosana Wijaya Kurniawan Wijaya, Waskitha William Hutamaputra Willy Andika Putra Wisik Dewa Maulana Yazid Basthomi Yoke Kusuma Arbawa Yongki Pratama Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Zamaliq Zamaliq Zhuliand Rachman Zulfina Kharisma Frimananda