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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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Deteksi Pergerakan dan Kedipan Mata, pada Pemilihan Menu Display menggunakan Centroid Analysis berdasarkan Metode Face Landmark Sri Mayena; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Detection is a process of identifying objects or activities that are usually done by humans, in order to facilitate the process of introduction and initialization in initiating all activities. In this section, the detection of eyes movement and eyes blink, used to be able to identify that, a system can be run by only eyes orders only, without the need for direct interaction, requiring that both humans and computers be in touch, Using the face landmark method, ascertained the eye area to produce high accuracy, in the test resulted in an accuracy rate of 93,33% success, in detecting eye features using face landmark, based on distance The nearest 20 cm closest, the research also shows rapid computing results in one-time detection of eyes features and the direction of the eyes movement takes only 0.170 seconds within 20 cm and 0.380 within 40 cm, with the help of digital image processing, the feature of the eyes will be easily processed by the computer and do the menu selection, which is meant to be a menu of the options of activities that can be used as one of the needed or orders of the user, with only activations Using a blink of an eye for 3-10 seconds, and a menu of 70x70 pixels, will be activated, and can be utilized by the nurse in understanding the activity that the user wants to do, especially the stroke sufferer, who can still use the eyes features As input that can be used to access the system.
Deteksi Pergerakan Mata dan Kedipan Untuk Memilih Empat Menu Display Menggunakan Probabilitas Berdasarkan Facial Landmark Akbar Dicky Purwanto; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Person with disability cannot do the normal daily aktivity. In patients with disabilities, the patient cannot move the whole body and therefore needs to be monitored by a household assistant. However, a household assistant cannot keep monitored for 24 hours continuously. To overcome this problem, this research was created to develop a system that helps people with disabilities to help their daily activities. The system was made to detect the eye movements to select menus from the screen. The menu on the LCD screen contains options including to call the nurse, choose a food menu, and go to the toilet. This system is made using the landmark face method combining with the eye movement detection method. Detection of eye movements will be divided into five, namely looking up, down, left, right and forward. The detection will be implemented in the options menu on the LCD screen so that patients can be helped in their daily lives.
Implementasi Metode K-Nearest Neighbors Pada Sistem Pendeteksi Sleep Apnea Dony Satrio Wibowo; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sleep apnea is a medical problem that has a sustained impact and has a high mortality rate, which is a burden on public health services. There are two main types of sleep apnea, that is central sleep apnea and obstructive sleep apnea. In this study I developed a system related to obstructive apnea because it is the most common form of apnea. To help medical staff or someone who has indicated sleep apnea to monitoring this disease, we need a system that can be used as an early detection is. This system uses the K-Nearest Neighbor method as the signal type determination algorithm. The acquisition of ECG signals by AD8232 sensor will acquire R-peak and R-interval data that used as parameters for determining sleep apnea. This system uses Arduino Uno as a microcontroller, AD8232 as an input and Buzzer as an output. The AD8232 sensor has an accuracy rate of 94.56%, the accuracy rate of the K-Nearest Neighbor method which is carried out as many as 15 experiments is 86.6%. And generated a time of 1281.1 ms for the average processing time of the system.
Perhitungan Kecepatan Kendaraan Secara Otomatis Menggunakan Metode Frame Difference Berbasis Raspberry Pi Faris Chandra Febrianto; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 12 (2019): Desember 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The variation of transportation which can be a helper for moving from another place is a part of technology advances. There are many of this transport who attracted the rider itself, with more and more variation vehicle user, sometimes increasing the trespasser of the road. These trespasser can be risk to other rider and increase crash incident, in the public road or freeway. The driver is also able to drive vehicles with different speeds but, many driver mostly do not care with speed limit that already shown clearly. From the explanation above, writer have an idea to make an artifact that does not can be used as CCTV but can known by the vehicle speeds automatic. This study about using frame difference method to detect an object by looking for differences from foreground with background, can resulting detect moving vehicles with an accurate average 90,1%. The advances of this method is only used grayscale color for processing image so it won't burden while processed. For the testing result about vehicle speeds, system has average error by 89,58% with program execution for system to run single video is 10,9 seconds.
Pengembangan Sistem Rekognisi Rambu Kecepataan Menggunakan Circle Hough Transform dan Convolutional Neural Network Berbasis Raspberry Pi Asfar Triyadi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Motor vehichle is a kind of transportation mode that are popular among people in Indonesia. Annually the increase in number of drivers is growing rapidly. Safety always comes first in driving for the sake of avoiding accidents. Accidents occur because of many driver's negligence factors such as drowsy when driving, bad facilities and infrastructure, using device that takes away your attention from the road and ignorant of the speed limit sign. For sake of helping the drivers to manage their speed according to the sign, it is needed of a system that can help remind the driver of the existence of the sign. Before fulfilling the task of reminding the drivers surely the system would need to be able to detect and recognize the speed sign. In this research the writer would want to propose the usage of Circle Hough Transform as the method of detection dan Convolutional Neural Network as the method of recognition with the purpose of knowing the performance of both method in doing their task. Both of the methods are in the field of study of Digital Image Processing and Machine Learning respectively that is well-known with its big computational need. The big computational need is the reason minicomputer Raspberry Pi is chosen as the base processing unit of the system compared to microcontroller. The result of testing for detection and recognition are 61.3% and 75% respectively. Looking from the results the methods that are proposed are not great but the writer believe that for the future research there is still room for improvement of the Circle Hough Transform and Convolutional Neural Network.
Implementasi Algoritma Hough transform Pada Object Following Menggunakan Ar.Drone Quadcopter Muhammad Tri Buwana Zulfikar Ardi; Gembong Edhi Setyawan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Quadcopter is one type of unmanned aerial vehicle (UAV) or unmanned aircraft that has 4 rotors. At present the quadcopter control system still uses radio control. Along with the development of quadcopter technology at this time has been equipped with various features such as GPS and also a camera to take pictures or digital images from the quadcopter's point of view. Ar.Drone is an output quadcopter from the Parrot brand. Ar.Drone uses the Linux operating system so that it can be used to conduct research on quadcopter development. On the quadcopter an intelligent algorithm can be applied related to image processing so that it can be developed and one of them is object following or tracking. By using hough transform researchers are expected to be able to create an automatic navigation system for quadcopter that can be used to detect spherical objects as a basis for navigation. With the automatic navigation system can be petrified if you want to take a picture or video needed to record an object without having to do manual navigation using radio control. The navigation system automatically runs based on the position of the ball in the video streaming frame. for that frame streaming is divided into 9 frames to find out what movements the quadcopter has to do to be able to move along with the object. There are several things that must be done before the image from the front camera quadcopter can be used for the implementation of the hough transform algorithm. The process is image pre-processing, namely digital image processing including segmentation using HSV, blurring, and canny edge. In testing the position of the spherical coordinates on the frame, it can be concluded that the quadcopter can track the movement of the ball with reference to the sphere midpoint coordinate position. From the distance test, the maximum distance for detection is 4m with the percentage of successful detection of all colors at 85%.
Sistem Pengenalan Rambu Pembatas Kecepatan menggunakan Metode Histogram of Oriented Gradients dan Klasifikasi K-Nearest Neighbor berbasis Raspberry Pi Nugraheny Wahyu Try; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 2 (2020): Februari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In Indonesia, the dominant use of transportation at present is road-mode land transportation compared to other transportation namely sea and air transportation. Accident rates were found to be increasing, due to lack of awareness of driving safety and security. The dominant human error factor is the cause of an accident. One of the accident factors is caused by drivers who lose control, because they ignore the maximum and minimum speed limiting signs. The solution to this problem is to create a system that can recognize maximum and minimum speed limiting signs. The research applies the Histogram of Oriented Gradients (HOG) method to obtain the characteristic feature extraction from the signs, then classifies the signs using the K-Nearest Neighbor (KNN) method. The system requires a raspberry pi camera to capture images for detection and object recognition. If the system manages to recognize the signs according to the actual conditions traversed by the driver, it will get notification of speed sign figures in the form of sound from the speakers. System testing is done based on a varied distance that is a distance of 3m, 5m, 7m, and 9m. The four distances that are the best distances in detecting speed limiting signs are 5 meters. The average result of recognition / recognition recognition accuracy using HOG method based on the best detection distance is 97.91%. Classification testing using K-NN consists of 650 training data and 48 test data obtained k = 1 and k = 2 accuracy values ​​of 97.91%, accuracy of k = 3, k = 4, and k = 5 values ​​of 95.83 %. The average time of computing the system to recognize objects 897 milliseconds.
Deteksi Pergerakan Kepala Menggunakan Metode Perbandingan Jarak Facial Landmark Untuk Pemilihan Menu Display Zulfina Kharisma Frimananda; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Disability is every person who has limitations. One type of disability is a physical disability that is an abnormality in the hands, feet, or both. In general, all electronic devices such as joysticks, remotes, menu selections on the LCD, etc. still utilize the intermediaries of the hands to operate them. This is a problem for people with physical disabilities. This problem can be overcome by creating a system that can help persons with disabilities in selecting menus through the display by utilizing the movement of the head as a substitute for the function of the hand that cannot be used because of its disability. This system uses the Raspberry Pi as the processing unit while the implementation of the output of this system is the selection of menus on the display. Testing the accuracy of the movement and computing time of this system is carried out on ten people. The system produces an accuracy of 90% in the detection of head-to-right movements, accuracy of 100% in the detection of head-to-left movements, accuracy of 100% in the detection of head-up movements, accuracy of 100% in the detection of head-down movements, average the accuracy of each movement of the head to the right, left, up, and down to the integration of the menu display + model selection is 97.5% and the average computational time value on the condition of the head to the right movement is 770 ms, to the left is 776.9 ms, upwards is 771.8 ms, and to the left is 768.1 ms.
Deteksi Kantuk Menggunakan Kombinasi Haar Cascade dan Convolutional Neural Network Rahma Tiara Puteri; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of traffic accidents is getting more numerous, especially in the time approaching the Lebaran season where people will go home to their villages. Based on data in 2017, there were 73 accidents in the six days before Eid, and when compared to 2016 there were 63 accidents, the number increased quite a lot by 16%. The main factor causing the accident is due to fatigue or drowsiness, because most events are at 21.00-24.00 then followed at 03.00-06.00.. Therefore, we need a system that can detect the state of the driver when he is tired or sleepy. This research developed a drowsiness detection system using Haar Cascade and Convolutional Neural Network. Input to the system is obtained from the Logitech C310 Webcam which will capture an image of a face. The main processing system uses Intel NUC5i7RYH which is used for image processing. The output of the system is the sleepy warning on the monitor when the driver is sleepy and there is an alarm sound for warning. The average accuracy of the system for face detection using Haar Cascade is 100%, the average accuracy for detecting open and closed eyes at a distance of 30-50 cm is 97.23% and the average accuracy for the detection of drowsiness is 97.23%. This system has an average computing time of 0.2075 s which will make it easy to detect drowsiness quickly.
Sistem Pengontrol Presentasi Menggunakan Pengenalan Gestur Tangan Berbasis Fitur pada Contour dengan Metode Klasifikasi Support Vector Machine Muhammad Hafid Khoirul; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In recent years, presentation has been a part of our lives. Presenter is limited by mouse, keyboard, or any other devices to control presentation. By using those devices conclude that less effective, less efficient, and less natural. On another hand, further method to control presentation uses hand gesture. It improves thinking process since the method can facilitate to give visual guide and deliver the presentation more effective, more efficient, and more natural. The developed system can recognize hand gestures and give output to control presentation based on the recognized hand gesture. Contour features such as Hu moments, circularity,convexity, aspect ratio, ratio of contour area, ratio of centroid height and width, and white pixel ratio used to distinguish every hand gestures. HSV and YCbCr color spaces used for skin segmentation. To recognized hand gestures used Support Vector Machine classification method. As a result, by using contour features and SVM to recognize hand gestures, system obtained average accuracy time of 88.15% and average computation time of 0.1567s.
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