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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

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Rancang Bangun Sistem Tingkat Kemanisan Buah Sky Rocket Melon menggunakan Metode Gray Level Co-Occurrence Matrix dan Backpropagation Neural Network Kirana Sekar Ayu; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the most popular types of melon in Indonesia is the Sky Rocket Melon. Melon fruit has a sweet flesh taste, yellowish green skin with a texture shaped like a net, and has a distinctive odor. The increasing interest in melons every year must be balanced with melon production. The postharvest sorting process is very important to determine the quality, sales, and prices in the market. One of the sorting processes is based on the level of sweetness of the melon. Measuring the size of the sweetness of melons can use a refractometer brix, but this is not practical because you have to split the melon first. Therefore, an innovation was made to determine the level of sweetness of melons by analyzing digital images based on the texture of the net found on the melon skin. This study uses 5 features of the Gray Level Co-Occurrence Matrix method, namely Dissimilarity, Homogeneity, Contrast, Correlation, and Energy with variations in the distance values ​​d=1,2,3 and θ = 0°, 45°, 90°, 135° and Backpropagation Neural Network to perform classification. This study uses a dataset of 375 data which will be divided into training data and test data with a ratio of 4:1. In testing the number of epochs and the learning rate, the highest training accuracy was obtained at 87% at 80,000 epochs and a learning rate of 0.1, at the values ​​of d = 2 and θ = 135°. The results of the epoch and learning rate tests are used to determine the values ​​of d and to be used in the integration test. The integration test obtained the highest accuracy of 82% on the bottom side of the melon with an average computation time of 2.2967 seconds.
Implementasi Algoritme Faster R-CNN untuk Sistem Pendeteksi Halangan di Luar Ruangan bagi Tunanetra Slamet Arifmawan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

According to WHO, there are about 7 million people who recently blind every year. People who are blind or so-called visually impaired will find some limitations when doing activities, especially outdoor activities. One example of outdoor activities is walking to a certain place. There are many visually impaired people who use a white cane (a stick-like tool which has length of approximately 1.5 meters) when walking. The disadvantage of this white cane is that its range can be assumed to be only 1.5 meters, while outdoor activities require one to be more aware of the surrounding. For example, when walking on the side of the road where there are many motorized vehicles passing by. Motorized vehicles also can move fairly quickly, even in a matter of seconds they can move more than 1.5 meters so that a range of 1.5 meters is not good for outdoor activities. Starting from this problem, the author plans to build a system of assistive device for visually impaired people that adapts neural network and computer vision technology, so that the resulting range is longer than the white cane. The design of this device is a strap equipped with a processing box and a camera that is attached to the user's chest. This device has an accuracy of 91.96% using the Faster R-CNN algorithm. When using CUDA acceleration, the number of frames per second is around 1.7 fps and when not using CUDA acceleration, the number of frames per second is 0.25 fps which is about 6 time slower than using CUDA acceleration.
Rancang Bangun Sistem Deteksi Gestur Tangan untuk Pengendalian Slide Presentasi menggunakan Algoritme You Only Look Once Versi 3 Muhammad Rafi Zaman; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Presentations have become part of the delivery of information that is very often used. However, in controlling the presentation, they still use a third device to control the presentation, such as the keyboard, mouse, and pointer. This is considered to be less effective like a keyboard and mouse because the presenter has to make a presentation by sitting in front of the screen so that it interferes with the presenter's focus and does not look natural like a pointer. Another solution that can be used as a presentation controller is human hand gestures. This study aims to create a hand gesture detection and recognition system to control presentation applications. This system will issue an output in the form of a simulated keyboard and mouse pressure. To be able to recognize hand gestures, the system uses one of the deep learning methods, namely You Only Look Once version 3 with an NVIDIA Jetson Nano device as a test. This system is designed to detect hand gestures within a distance of 1 to 2.5 meters. The training data used in this system are 7080 images of training data with 8 classes of hand gestures and the training data is taken with a predetermined distance. The results of system testing based on distance produce an average accuracy value of 91.18%. And the average computation time is 0.5988 seconds.
Deteksi Masker dan Suhu Tubuh untuk Kendali Portal Otomatis Menggunakan CNN sebagai Pencegahan Penularan SARS-CoV-2 Ichsan Ali Rachimi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 can be spread through droplets from the nose or mouth that come from a patient infected with COVID-19. If people breathe in the splash with a person who is infected with this virus, they may be infected with COVID-19. Therefore, it is very important for us to maintain a minimum distance of 1 meter and always use a mask to prevent the spread of coronavirus. These splashes can stick to other objects and surfaces, such as tables, door handles and handrails. Because of the importance of using masks during the Covid-19 pandemic, this study will apply the Convolutional Neural Network method to detect mask users, so that in its implementation the system can detect when someone is not wearing a mask and has a body temperature above the normal number, which is above 37.5 ° C. then the system will automatically close the latch, this is intended so that people always use masks during the COVID-19 pandemic and care about the spread of the virus. The average error value is 1,48% on the infrared sensor and the accuracy at the integration testing stage of the mask detector and infrared temperature sensor MLX90614 at the 9G micro servo output gets an accuracy of 100%.
Sistem Deteksi Hama Babi menggunakan CNN (Convolutional Neural Network) berbasis Raspberry Pi Aditia Reza Nugraha; Fitri Utaminingrum; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is an agricultural country, part of which contains the agricultural sector, such as maize, rice, oil palm and other food crops. The area of ​​agricultural land increases every year by cutting down forests as an additional resource. Forest encroachment for the agricultural sector has an impact on the environment in which agriculture is located, where agriculture is in direct contact with forest areas. Agricultural areas adjacent to plantations cause wild animals such as wild boar to become pests that can damage plantation products and are also dangerous if they come into direct contact with humans who are present while doing activities in the area. As a pest repellent prevention, it is usually carried out by conventional means such as pig traps, poison, or nets in the affected area. As an innovation using smart technology, a solution to this problem requires a system that functions as a security supervisor for plantations. The system that will be created serves to monitor the situation when the plantation is invaded by pests such as wild boars. The surveillance system uses a webcam that is attached to the Raspberry pi model 4 which functions to see the state of the garden when there are objects of pig pests. The monitoring system on the camera will classify wild boar and farmer objects on plantations using the Convolutional Neural Network method by YOLOv3 architecture with an accuracy rate of the data model for a value of 97%. The system will also be built using a buzzer notification as a notification when a pest is detected on hardware with a success of 100% buzzer output.
Sistem Deteksi Dini Rambu Petunjuk Arah Otomatis berdasarkan Optical Character Recognition (OCR) berbasis Raspberry Pi Mochammad Bustanul Ilmi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The car is a means of daily transportation which has become a primary need in carrying out daily activities. Not only is a public car used by someone for transportation, but many people use private cars for their transportation needs. In today's private car many security measures have been applied, such as parking sensors, airbags, dash cameras, parking cameras, door lock alarms, etc. The research will discuss the dash camera which in this study will improve the function of the dash camera. In this research, we will upgrade the dash camera so that it can also function as a direction sign detector, which usually the driver must lift his head to see the sign. But this system will help the driver see the sign just by looking at the LCD monitor in the car or just by listening to the sound that will be issued by the car sound. The system uses shape detection, optical character recognition (OCR), and text to speech which are collaborated to detect these signs which will then be displayed on the LCD monitor of the car and will output sound to indicate where it has to take the direction of the intended path. From the test results, almost all sample images can detect the direction signs, but if the signs are in poor lighting, the image crop position is not symmetrical and not straight, then the sign will not be successfully read and processed by the system. And according to the results of the test, the resulting system results get the percentage of success for the detection of arrow directions of 87.5% success and 12.5% ​​fail or error. Then for the accuracy value of the detection results using optical character recognition (OCR) get a percentage of 57.45% as the average accuracy value of the 15 detected destination directions.
Implementasi Algoritme Faster Regional Convolutional Neural Network pada Sistem Pendeteksian Objek Halangan di dalam ruangan bagi Penyandang Tunanetra Andika Bayhaki Al Rasyid Syah; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The reflection of light from objects to the eyes is used by humans to visualize an object. The role of the eye organ is very important for humans because human daily mobility requires visualization of objects that are visible to the eye. However, not everyone has optimal vision in their eyes. A person who loses his ability to see is called a blind person. The World Health Organization estimates that more than 7 million people are blind every year. Blind people usually use a walking stick. However, the use of a stick is less effective because the use of a stick can only detect objects with a relatively low distance according to the length of the stick and the use of a stick cannot classify objects that are in the way. Therefore, the researcher wants to design a system that is used as another alternative for the visually impaired for object detection aids using computer vision technology. The algorithm used is Faster R-CNN using NVIDIA Jetson Nano device and detected objects in the form of walls, doors, tables and chairs. When the system detects the presence of the object, the system will issue a notification in the form of a sound obtained from the buzzer. Based on the tests carried out, the accuracy of the Faster R-CNN algorithm training results is 95% at the number of steps 140,000. Then test the system detection time when applied to the NVIDIA Jetson Nano for 0.77 seconds for testing on images and the resulting Frame Per Second is 1.30 for real-time testing. Then the object detection accuracy is 81.25% at a distance of 2 meters and testing the accuracy of system integration with the hardware used as the system output, namely the buzzer of 100%.
Implementasi Metode Optical Flow untuk Pemilihan Menu Display pada Rancang Bangun Sistem Deteksi Pergerakan Kepala Nyoman Kresna Aditya Wiraatmaja; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Ministry of Health has collected data on persons with disabilities for 2018 which through Riskesdas in 2018 obtained 3 categories for persons with disabilities. First, there are people with disabilities aged 5-17 years who have disabilities reaching 3.3% throughout Indonesia. The second category is adults with disabilities whose age range is from 18 - 59 years which 22% experience disability in all of Indonesia. The last category is the elderly category, where people with disabilities are someone who is over 60 years old, it is stated that 22% of the elderly experience mild obstacles to carrying out their activities, 1.1% have moderate barriers, 1% have severe barriers, and 1.6% elderly are totally dependent. In this case, it is very difficult for people with disabilities to carry out normal activities or need assistance to operate electronic devices. So this study aims to create a head movement detection system with the optical flow method for selecting the navigation menu screen so that it can be used by people with hand disabilities without making direct contact. This system works by detecting the movement of the user's head, namely with broken movements to the left, right, head movements up, down, and head movements to the right. The output of the system is that the user can select the available navigation menus without making direct contact with the system. Based on the tests, the system obtained an accuracy of 94.6% and a computation time of 2.43104s in dim lighting. While in bright lighting the system gets an accuracy of 98.6% and the computation time is 2.41244s.
Early Warning Sistem Rambu Pembatas Kecepatan menggunakan Histogram Oriented Gradient dan Klasifikasi SVM berbasis Raspberry Pi Masyita Lionirahmada; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Traffic accidents happened frequently due to the lack of public attention to traffic sign regulations. Along with the many cases of accidents and the high number of deaths caused by public negligence in understanding the meaning of traffic signs correctly, an early warning is needed to understand the traffic signs listed on the road by making an early warning system for speed limiting signs. In this study, a system for detecting speed limiting signs was developed using feature extraction Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) classification to classify the types of speed limiting signs. To carry out the detection process for speed limiting signs, this system uses a Pi camera to take the video of the speed limit signs to be detected. When the speed limiting signs is detected, the speaker will release a warning sound to make it easier for drivers to drive and comply with traffic regulations. The system testing process is carried out by looking at the results of the implementation system that can detect the signs and the level of system accuracy when it detects speed limiting signs. The average accuracy of the system from the detection results consisting of signs limiting the maximum speed of 20km, 25km, 30km, 40km, 50km and the minimum of 20km is 86.08%. In addition, system testing is carried out by driving following the speed of the vehicle listed on the speed limit sign to determine whether the system can detect it properly by following the speed direction on the speed limiting sign.
Deteksi Helm untuk Keamanan Pengendara Sepeda Motor dengan Metode CNN (Convolutional Neural Network) menggunakan Raspberry Pi Ikhsan Rahmad Ilham; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Traffic accident factors are caused by 3 factors such as human negligence, the state of the vehicle and also environmental factors. According to WHO (World Health Organization) the use of helmets for motorcycle riders can reduce the risk of death by up to 40%. The use of helmets as safety for motorcycle riders is still considered not so important that it is ignored, therefore motorcycle accidents, especially motorcyclists who do not use helmets and the level of traffic violations are high. The fatigue factor for the police in monitoring traffic causes ignoring motorists who violate by not wearing a helmet while driving. Therefore helmet detection for motorcycle riders is very important, namely using technology to obtain information on motorcycle riders who violate the rules. Based on these factors, after knowing the causes of motorcycle accidents in traffic, one solution can be reduced, namely using computer vision for helmet detection as motorcycle riders' safety in driving and facilitating the work of police officers in guarding traffic by way of notification. notification of traffic violations of motorists who are not wearing helmets with buzzer alerts. The author proposes the CNN (Convolutional Neural Network) method as a detection of motorcyclists who violate traffic such as not wearing a helmet, thereby reducing traffic accidents. The results of the tests that have been carried out by the system can detect objects of people not wearing helmets with an accuracy of 90% using the confusion matrix on the test results.
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