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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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Face Recognition Untuk Sistem Pengaman Rumah Menggunakan Metode HOG dan KNN Berbasis Embedded Bagus Septian Aditya Wijayanto; Fitri Utaminingrum; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The home security system is one of the features that must be owned and considered for every homeowner who wants to have a home that is safe from theft and avoid other unwanted security disturbances. So we need a support system that is able to increase home security. In this study, the system created uses the face as security data. This system uses a webcam as a face image taker and is integrated with the Raspberry Pi. This system will apply the buzzer, LED, solenoid door lock and SIM800L modules as outputs of the system. This system uses HaarClassifier to detect faces, then Histogram of Oriented Gradient and k-Nearest Neighbor for face recognition. First the system will take the image captured by the webcam, then use face image detection with Haar-Classifier, then the facial image will be extracted using the HOG feature. After the face feature value is obtained, it will then be classified using the k-Nearest Neighbor algorithm. From the results of testing the accuracy of face detection is the best accuracy of 100% at a distance of 40cm. The results of the accuracy of face recognition at a distance of 40cm in total are equal to 87.5%. For testing the accuracy of integration between software and hardware produces an accuracy rate of 100%. The average time needed for the face recognition process is 13,28839 seconds.
Rekognisi Wajah Pada Sistem Smart Class Untuk Deteksi Kehadiran Mahasiswa Menggunakan Metode Viola Jones dan Local Binary Patterns Histograms (LBPH) Berbasis Raspberry Pi Fitrahadi Surya Dharma; Fitri Utaminingrum; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Facial recognition is one of the techniques in computer vision that is able to recognize a person's face from an image. The application of face recognition into the presence system is very important considering that there are still cases of attendance data manipulation by students in the presence system using manual - filling signatures on the attendance sheet. Lack of tight supervision in filling attendance sheets is an event that is vulnerable to cases of manipulating attendance data. Therefore in this study try to present a presence system that uses images to find out the presence of students. The trick is to take pictures using a camera that is placed in front of the class, just above the blackboard facing the student. From the images taken, the system will then detect the faces of students using the Viola Jones method of the OpenCV library combined with YCbCr skin color pixel detection to avoid false detection. And for face recognition students will be using the local binary patterns histograms method from the OpenCV library. Accuracy results obtained by the system showed the level of detection accuracy of 82.33% and recognition accuracy of 50.83% in the morning, 61.11% during the day, and 58.89% at night. The average total computing time for the detection of one student is 0.293 seconds, two students 0.297 seconds, three students 0.317 seconds, four students 0.313 seconds, five students 0.31 seconds and six students 0.307 seconds. While the average total face recognition computing time for one student is 2.17 seconds, two students 2.58 seconds, three students 3.01 seconds, four students 3.38 seconds, five students 3.78 seconds, and six students 4 .12 seconds.
Sistem Pengukuran Tinggi dan Berat Badan Berdasarkan Perhitungan Body Surface Area (BSA) Menggunakan Boundingbox Berbasis Raspberry Pi Mohammad Isya Alfian; Hurriyatul Fitriyah; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

BSA (Body Surface Area) is used as a way to get information on a person's body condition. Paramedics use these calculations in patient data collection and require a long time. In previous studies using image processing techniques to speed up data retrieval. Data retrieval is carried out front and side view then processed using pixels by cropping the image. From these studies, researchers have the idea of using a mini pc to further speed up data retrieval and be efficient. BSA calculations are included in the mini pc Raspberry Pi and assisted by webcam cameras as image takers. This tool is useful to help paramedics to collect data on body weight and height, faster and more efficiently. The object of the research that was sampled was 20 people, male and female, consisting of children and adults. Data collection was carried out 22 times each in children and adults. The data obtained is calculated by the Bounding Box accuracy value. The results of height accuracy in boys were 91.4%, girls were 87.8%, male adults were 98.34%, and female adults were 98.2%. While the results of accuracy for weight are based on the k value of each object. The k value for boys is 1.34 with an accuracy of 75.32%, girls 1.34 with an accuracy of 79.76%, male adults 1.26 with an accuracy of 95.6%, and adult women 1 , 22 with an accuracy of 92.38%.
Deteksi Arah Pergerakan Kepala Sebagai Kendali Motor Servo Menggunakan Area Mata Facial Landmark Aliffandi Purnama Putra; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Disability is defined as a disability or inability to do something. There are various kinds of people with disabilities, one of whom is a person with a physical disability. In controlling a device with physical disabilities in the hands and feet it will have an impact on their difficulty in mobilizing so that they still need help from other people or other devices. These problems can be solved by using a camera-based control control system instead of the joystick or remote as a control tool. This study was designed to detect eye position based on head movement through image processing using cameras and facial landmarks as a method of detection. Processing on this system uses Raspberry Pi 3B + and trigonometric analysis. The output implementation on this system uses a servo motor. Tests on this system by testing 8 subjects using different time and distance from the position of face objects. The results of this study are the best time to detect the eye at any position of head movement in the morning and afternoon. The percentage of the best head movement is straight, right angled, left and left angled with a 100% accuracy. While on the right tilt movement get a percentage of 98.96%. System computing time is determined by the distance of the subject in front of the camera. The farther the subject from the front of the camera, the computing time of the system will be smaller. Conversely, if the subject is getting closer, the time for computation is getting bigger. For accuracy System integration with hardware is based on the classification output on the system. If based on the original movement, the accuracy is the same as the accuracy in detecting head movement based on the eye position.
Sistem Pengenalan Pergerakan Lengan Menggunakan Exponential Moving Average Dengan Metode Decision Tree Berbasis EMG Aufa Nizar Faiz; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Humans can carry out their work in a healthy condition, so health is the most important thing in life. But many people are unable to do their jobs due to physical limitations, commonly called persons with disabilities. Biomedical engineering is medical science that uses medical science and design engineers to solve health problems. Electromyograhy (EMG), one of the biomedical sciences that can detect signals generated by contractions in muscles, using EMG can make the system of detecting signals of muscle contractions, especially in the arm muscles. This system will help detect arm muscle contractions for people with disabilities in the arms. Detection is performed on changes in the degree of the arm, the degrees detected are 0, 30, 60, 90, 120, 150, and 180 degrees. Signals received by EMG have noise that can interfere with detection, so signal refining is required in the form of an exponential moving average (EMA) method. Exponential moving average has a weighting value to make a reward, the value used is 0.1 and 0.3. After refining the signal, the detection of degree changes is performed using the decision tree classification method. Then the results of the classification will be displayed on the LED and LCD.
Klasifikasi Golongan Kendaraan Berdasarkan Fitur Histogram of Oriented Gradients (HOG) Menggunakan metode K-Nearest Neighbors (K-NN) Berbasis Raspberry PI 3 Lilo Nofrizal Akbar; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The queues that occur when making toll road payments are still a problem in Indonesia, one of which is the problem because there is no system that automatically classifies vehicles passing on the toll road, so that large vehicles such as trucks that are divided into 5 classes must be manually differentiated by toll gate officers. One effort to overcome these problems, in this study a system was designed to be able to automatically classify vehicles passing the toll road into 5 classes according to the class applicable to the Indonesian toll road, so the classification process that was previously done manually can be done automatically, this of course bring benefits to the payment transaction time for each vehicle that is getting shorter, which in turn can reduce the potential for congestion that occurs at the toll gate. This system works based on image processing, the classification process begins with vehicle video capture using a webcam, then the vehicle video is processed on Raspberry Pi 3 to extract image features from the vehicle using the Histogram of Oriented Gradients (HOG) method, then the features obtained are processed The classification uses the k-Nearest Neighbors (k-NN) method to determine the class of vehicle, then results of the classification are displayed on the LCD screen. From the tests conducted on the system using 25 test data, with each class as many as 5 test data, the results obtained for system accuracy in class 1 by 80%, class 2 by 80%, class 3 by 60%, class 4 by 60%, and class 5 by 60% .
Deteksi Pergerakan Kepala Berdasarkan Analisis Deteksi Tepi Wajah Berbasis Raspberry Pi Untuk Implementasi Pemilihan Menu Display Budi Atmoko; 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

In the computer science both the operating system and the display during installation, there is a feature called a menu. This feature used to access functions on the running system. Has 2 basic display are GUI and CLI, most menus use human hand for navigation. The problem is that the compilation menu can't be accessed by persons with disabilities and person who get accident that cause a problem in their hand to move an object or hardware for navigation a menu. The solution offered uses detection of face edges using human head with raspberry pi single board. RGB convert to YCbCr and HSV color space, then forming a bounding box with parameters x, y, w, h and also quadrant area . By determining the value of h / w and centroid distance and the number of pixels in each quadrant as a determinant of movement classification. The results obtained based on lux 1500-1800 reached 96% and lux 1250-1400 reached 92% while for distances at 40,50,60 and 80cm had the highest accuracy at 60cm distance with 96% accuracy . With a good result in visual display and oval shape is also good in accordance with the expectations of researchers.
Rancang Bangun Sistem Klasifikasi Kemurnian Susu Sapi dengan menggunakan Metode Naive Bayes Dimas Rizqi Firmansyah; Dahnial Syauqy; 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

Cow's milk is a popular food and beverage consumed by the public. The benefits generated from cow's milk are numerous, because they contain protein, vitamins, and minerals needed in the body. Sales of cow's milk are often found in rural, urban, tourist attractions, roadside, to restaurants. Because there are so many people who need cow's milk, there are often bad sellers or sellers selling impure cow's milk. Because by falsifying cow's milk, naughty sellers benefit very much. From the falsification of the purity of cow's milk, there are a lot of losses felt by consumers, including consumers being a loss, so that the worse is consumers can be hospitalized because falsified milk is included ingredients that are not suitable for food. Therefore, to help the public not to get caught buying cow's milk which has been mixed with water by an individual, tools are needed that are able to test the purity of cow's milk directly and quickly. Because of this problem, a research was carried out to build a tool that could detect cow's milk, mixed milk or pure milk. This research requires a TCS3200 color sensor which is used to detect color in cow's milk, and also a pH sensor to obtain the acidity value in cow's milk. For the classification results using the Naive Bayes method calculation. The choice of using the Naive Bayes classification is because the method can be used to process biased data and accurate calculation results. Based on the test results, obtained an accuracy of Naive Bayes calculation of 90% taken from 20 times the test, and the test there are 2 results that are not appropriate. While the speed of calculating the device starts from the taking of the value by the sensor until the tool can issue an average classification result of 6932 ms.
Deteksi Objek Penghalang secara Real Time berbasis Aplikasi Mobile dengan Metode Gray Level Co-Occurrence Matrix dan K-Nearest Neighbor bagi Penyandang Tunanetra Rizky Haris Risaldi; 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

Blind people have a condition that their sense of sight is not functioning properly. The condition causes the blind have difficulties in mobility. Solution for these conditions is use a stick. Mobile devices can be a new solution for this problem because mobile devices are capable of many processes. This system was built using a camera from a mobile device as a substitute for the sense of sight. The results of the camera are then extracted features using the GLCM (Gray Level Co-Occurence Matrix). Once the feature is obtained, the classification is done by algorithms KNN (K-Nearest Neighbor). The classification used to determine these features is the floor or obstruction. If the result of the classification is an obstacle, the next process is to turn on the buzzer as a sign to the user that a obstacle has been detected. . This system has good detection accuracy when using a small ROI (120x213 pixels) of 90% compared to ROI (360x640 pixels) of 60%. In real time the system has a 100% accuracy in obstacle detection for white floor objects, 82% for obstacles with wooden door objects, 93% for white obstacles and 93% if detection is done on 2 different objects in 1 video. Integration of hardware and software on this system has an accuracy value of 88.8%. For computing time this system has an average value of 248.8 ms, a minimum value of 183 ms and a maximum value of 582 ms.
Rancang Bangun Sistem Pengaturan Kecepatan Otomatis Jumlah Tetesan Infus Pada Pasien Berdasarkan Uji Linieritas Mohammad Sezar Nusti Ilhami; 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

The development of medical science and increasingly sophisticated technology has led to demands for convenience. Errors in giving intravenous fluids can be bad for patients. Currently the regulatory system carried out by medical personnel to regulate each drip infusion in patients is still done manually, this condition will certainly require the attention of a nurse or nurse seriously because the possibility of an error will be large. The nurse also must constantly monitor the drip drip whether it is still stable or not. Sometimes the condition of the infusion is not in accordance with the specified dose even though the administration of an inappropriate infusion dose can cause negative effects on the patient. An intensive care unit recently found 47% of side effects were due to medication and wrong dosage. Based on these problems, we need an Automatic Speed ​​Management System for Number of Infusion Droplets in Patients Based on Linearity Tests that can help nurses to monitor the condition of the infusion drops whether it is still the same as the dose set or has changed. In this system, several components are used: the NodeMCU microcontroller to process data and perform Linear Equation calculations, a photodiode sensor that is useful for detecting whether droplets are present or not, stepper motors as movers and regulators for infusion droplets. resulting from the linear equation is linear or not when applied in the system.
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