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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 Bola Mata untuk Pemilihan Empat Menu Menggunakan Metode Facial Landmark dengan Ekstraksi Fitur LBP dan Klasifikasi K-NN Rhaka Gemilang Sentosa; Fitri Utaminingrum; Eko Setiawan
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

The more sophisticated technology makes all electronic devices can be made through joysticks, remote controls, and so on. This is a challenge for stroke sufferers because it limits them to move the member must make it able to rotate the device. Then the menu selection system needs to be made on the display that is able to make a sound to facilitate sufferers in communicating. Stroke sufferers have limitations to move members but can still move the two balls. This research was made to overcome their limitations by using the K-Nearest Neighbor classification method to classify the value of features resulting from ball motion detection using digital image processing with the face landmark method to convert eye areas and using the LBP method to extract features in the eye area. This system produces an accuracy of 100% and in dim lighting produces an accuracy of 60%, 20%, 100%, and 100% for moving the eyeball forward, right, left, and up. 100% accuracy results. The results of computational time are 399.7 ms, 398.4 ms, 398.4 ms, 396.8 ms for moving the eyeball forward, right, left, and up.
Sistem Presensi Mahasiswa Berdasarkan Pengenalan Wajah Menggunakan Metode LBP dan K-Nearest Neighbor Berbasis Mini PC Meidiana Adinda Prasanty; 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

Every institution such as the education system in Indonesia, even for offices certainly requires a system that can record the entire community of it's members. But in today's modern era, where technology has advanced rapidly it turns out that in some institutions in Indonesia still rely on the old presence system that is manually, such as using paper and initialing. This research makes it possible to reduce fraud by utilizing digital imagery that is face recognition in order to make a presence so that it becomes more practical, efficient, fast and certainly safe and does not happen to the detriment of any institution. In this study, a student presence system was developed based on face recognition using Local Binary Pattern and K-Nearest Neighbor method. By using the Logitech C270 webcam and the Alcatroz Stealth 5 mouse as an input producer, the Intel NUC5i7RYH Mini PC as the main processor, and 7-inch Waveshare monitor as output. Webcams produce images of students sitting in class and then processed by a Mini PC for the detection and facial recognition of each student. Obtained the names of facial recognition results that can enter the attendance list if the user (lecturer or researcher) presses the presence confirmation button on the application using the mouse. The average system accuracy of all experiments in face detection using Haar Cascade Classifier is 88.88%, in face recognition using Local Binary Pattern and K-Nearest Neighbor for k = 3 value is 78.125%, for k = 5 value is 74.375%, and for the value of k = 7 which is 68.125% so that the highest accuracy can be achieved using the value k = 3. The average computational time of all experiments in face detection is 26.2 ms while for face recognition is 371.675 ms.
Sistem Klasifikasi Kualitas Daging Ayam menggunakan Metode K-Nearest Neighbors berbasis Arduino Zamaliq Zamaliq; Fitri Utaminingrum; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Chicken meat is one of the most abundant sources of animal protein consumed by the community. Chicken meat consumption needs will always increase due to several factors, namely, the price of chicken meat that is relatively affordable compared to beef, has good nutritional quality, and is easily processed into various types of cuisine. Chicken meat sellers in the market or on the side of the road sometimes their sales results are not fully sold in the hands of consumers for various economic reasons, storability and ignorance of the public using hazardous additives and preservatives may be done some examples of abuse on food products is the use of synthetic preservatives for example formalin and borax. In overcoming this problem the determination of the classification of tiren chicken meat, rotten chicken, and formalin chicken, then the right method is needed to do the classification. K-NN method can work independently on each object features to be classified. In this system, several components are used: Arduino Mega Mini microcontroller to process data and perform calculations, the TGS2602 sensor is useful for detecting aroma in chicken meat, the accuracy of the error generated by 3.42% is placed in a container, the Ph BNC Electrode sensor Probes with an accuracy error level of 25.89% are useful to measure acid base levels in chicken meat. For classification using the K-NN presentation method the accuracy was found to be 80.95%.
Sistem Kontrol Kecepatan Motor Berdasarkan Enam Arah Pergerakan Kepala Menggunakan Facial Landmark Mata Gagana Ghifary Ilham; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Persons with disabilities are normal people with limitations in doing something. One type of disability is a person with a physical disability, while a person with a disability does everything that needs help from others. Wheelchairs, for example wheelchairs. In this wheelchair can facilitate mobility of people with disabilities, but at the time when the compilation of disabled people appears not only defects in the legs due to defects in the hands as well, so to use this wheelchair will also feel difficult and still need help from others. This problem can actually be above by using a remote or joystick in order to control the wheelchair. The research was carried out in order to help people with physical disabilities intended for people with multiple physical disabilities and was designed to utilize head movements using digital image processing using cameras and facial landmark methods. In processing data on the system using Arduino nano and implementing output using dual L293d motor. The test was carried out using 6 people using different times and distances from the face objects on each head movement. The average test result for movement based on distance and time for all test subjects is 100%. Whereas in the movement of looking up at night the distance of 50 cm and 30 cm, and during the day at a distance of 30 cm also get an average gain of 98.75% in one of the test subjects. The effect of time and distance is very important in conducting this research.
Implementasi Histogram of Oriented Gradients dengan Support Vector Machine dalam Rancang Bangun Tempat Sampah Kantor Otomatis Marsha Nur Shafira; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 9 (2020): September 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Irregularities that occur in the waste buildup causes the handling of waste that's not well controlled. The office workers tend to be busy which causes them to be careless about waste they produce every day. The solution to the problem can be done by recycling the waste and categorize the waste by its type. Therefore a system was made in a form of the Automatic Office Trash Bin which is able to categorize the three types of offices waste. The main component that is used in this research are Raspberry Pi, camera and infrared proximity. The process of this system is assisted by Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The result of the system will produce an output which are determination the type of the waste and measured based on the full amount of waste in the trash. . The average examination result of the accuracy in detecting plastic bottles and papers reach 90.63%. Cans got the best result of this examnination, it has reach 96,88%. The average examination result of the computing time reach 3,45 s for plastic bottles, 3,496 s for cans and 2,529 s for paper. The examination of the infrared proximity censor in detecting the amount of waste in the trash can reach 100%.
Sistem Pendeteksi dan Pengenalan Rambu Pembatas Kecepatan menggunakan Jaringan Syaraf Tiruan berbasis Raspberry Pi Alvin Evaldo Darmawan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 9 (2020): September 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Speed ​​limit traffic signs are signs that used to limit vehicle speed. But many drivers break the sign rule because intentionally or unintentionally, resulting in accidents that endanger yourself or others. So, we need a tool that can detect and give a warning to drivers. The system developed in this study detects speed limit traffic signs and perform recognition. The system will detect the color and shape of traffic signs to detect these signs by using color segmentation and Hough Circle Transform. The average accuracy of the system in detecting signs is 94.45%. Next is character detection to separate each character. Character detection is done by color segmentation and Bounding Box for each character. The average accuracy in detecting characters is 88.89%. Furthermore, the character image will be extracted using the division area and the ratio of the foreground pixel to pixel area in each area. The feature extraction results obtained 25 features as input from the Artificial Neural Network to do recognition using the Back-Propagation training algorithm. The average accuracy of the system in doing character recognition is 88.89%. While the average total system computing time is 0.227 seconds.
Deteksi Pergerakan Arah Mata menggunakan Convolution Neural Network berdasarkan Facial Landmark Muhammad Amin Nurdin; Randy Cahya Wihandika; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The movement of the human eye can be useful in various fields, for example in security systems, health, transportation and design interface. In the design interface systems, eye movement used as an interactive system. The system can interact and responses to users by using eye movements. The video-based eye tracking method has the advantage of being practical and convenient during the detection process. This study uses the Convolution Neural Network (CNN) algorithm because it will utilize the advantages of the CNN method to classify and have the most significant results in object recognition. The results of this study indicate that the CNN model that good to use in the classification of eye direction based on facial landmarks is with 2 layers contain 32 filters and 64 filters, batch size 16 in image augmentation with 20 fully connected layers resulting loss value of 0.08, with an accuracy of 0.98 and 8.62 seconds in training time. Test results on videos taken 50 frames randomly three times, resulting in an average accuracy 0.95.
Pengembangan Sistem Rekognisi Rambu Kecepatan Menggunakan Circle Hough Transform dan Convolutional Neural Network Rizky Teguh Nursetyawan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 11 (2020): November 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 on Day 80% and night 70% . 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 Sistem Pendeteksi Atrial Fibrillation Berbasis Arduino Uno Menggunakan Metode Support Vector Machine Renita Leluxy Sofiana; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Many cases of coronary heart disease that cause sudden death because the patient has a history of arrhythmias. In addition, 1 in 6 stroke patients are also caused by arrhythmias. According to cases that often occur in hospitals, Atrial Fibrillation arrhythmias are one of the factors that cause stroke because patients with Atrial Fibrillation arrhythmias have a five times greater risk of stroke because it can cause blockage of blood vessels. Prevention for this disease can be done by conducting early examinations so treatment can be done quickly. Currently, atrial fibrillation arrhythmia examination can only be done in a hospital, which is quite expensive and this examination cannot be done independently. This study uses the AD8232 sensor to generate ECG signals, the Arduino Uno as a data processor, and the LCD to display the results of the diagnosis, "Normal" or "Atrial Fibrillation". The system will use the BPM feature, mean RR interval, and median RR interval to perform Support Vector Machine classification. Starting with 24 training data to produce a hyperplane. Furthermore, data testing is carried out to classify. The results obtained from the BPM accuracy test were 12 times the result of 95.42%. The classification using Support Vector Machine method resulted an accuracy, training time, and testing consecutively 83.33%, 219.30 ms, and 0.09 ms using 12 test data.
Deteksi Hipoksia Berdasarkan Detak Jantung, Saturasi Oksigen, Volume Dan Irama Pernafasan Menggunakan Metode K-Nearest Neighbor Leina Alimi Zain; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Symptoms of hypoxia are a condition caused by a lack of oxygen in the cells and tissues of the body and this condition can cause damage to the nerves of the brain, liver and other organs which will lead to death. The use of technology in the medical field has created a system to detect hypoxic symptoms using the K-Nearest Neighbor method. The detection system using the K-Nearest Neighbor method can be carried out in knowing the condition of a person's body without injuring the body or it is called non-invasive. Retrieval of heart rate and oxygen saturation data using the MAX30100 sensor by placing the index finger on the red LED component and the IR photodiode component. It takes 20 seconds and the finger must not move during the take to get the optimal value. In taking the volume and rhythm of breathing is done using a Flex sensor. The hardware used is the Arduino Mega, the MAX30100 sensor and the Flex sensor. The level of accuracy on 10 tests on the MAX30100 sensor is 97.07% and the accuracy level obtained on the Flex sensor is 92.77%. In classifying using the K-Nearest Neighbor method, there is a level of accuracy at the k = 3 value of 90% k = 5 by 80% and k = 7 by 70% and there is a computational average of 3.37 ms in 10 tests.
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