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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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Perbandingan Akurasi untuk Deteksi Pintu berbasis HOG dengan Klasifikasi SVM menggunakan Kernel Linear, Radial Basis Function dan Polinomial pada Raspberry Pi Anugrah Zeputra; 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

The Covid-19 virus pandemic causes new problems in people's lives in Indonesia. The pandemic requires the implementation of all activities to run with a distance limitation or is called social distancing, as a result of which many of the activities that take place are hampered and inefficient. Therefore, developing a system that can work autonomously is an idea that can provide solutions to these problems. an autonomous system that can be used in this problem is a detection system using a Machine Learning algorithm. The detection system uses Computer Vision to get input to detect objects such as open doors, closed doors and walls. this system works autonomously in real time. Computer Vision uses image data as input. Therefore the image data extraction feature using the Histogram of Oriented Gradient (HOG) is a suitable basis for processing image data when juxtaposed with a Support Vector Machine (SVM). SVM has several types of kernels that can be used to classify image data, some of which are Linear, Polynomial and Radial Basis Function (RBF) kernels. the way each kernel works is different from one another to classify image data. Therefore, research to examine the comparison of the SVM kernel to the classification of image data was investigated in order to obtain a more optimal system performance.
Deteksi Kantuk pada Pengemudi melalui Jumlah Kedipan Mata Menggunakan Facial Landmark berbasis Intel NUC Dewi Amalia; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The level of traffic accidents is increasing every year, one of the causes is the driver who is tired and sleepy when driving. Therefore a system will be made to anticipate accidents by giving warnings in the form of writing and alarm. The system uses Intel NUC for processing, the webcam accepts inputs and monitors to see image captured by the camera and see information on the condition of the eyes and sleepy information. The method used is Facial Landmark for detection of eye areas on the face. For under-lighting or uneven lighting, use the feature of image thresholding, namely adaptive threshold gaussian. Detection of the eye area on the face and sleepiness is done with a camera within 30cm, 40cm and 50cm parallel to the shoulder or chest. This detection also uses the range of light intensity 0-49 lux and 50-400 lux. The average accuracy of Facial Landmark for detecting eye areas on the face with light intensity 0-49 lux is 93.33% and for light intensity 50-400 lux is 100%. While the average accuracy of drowsiness detection at 0-49 lux light intensity is 96.66% and for light intensity 50-400 lux is 98.88%. The average system accuracy is 97.77%. The fastest computing time of the system at 0-49 lux light intensity is 0.33 s and at light intensity 50-400 lux that is 0.34 s.
Deteksi Tingkat Kemanisan Buah Melon melalui Ekstraksi Fitur Local Binary Pattern dengan Klasifikasi K-NN berbasis Raspberry Pi 4 Versa Christian Wijaya; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is a country with a subtropical climate and is good at growing fruits and vegetables and other crops. Various variants are easily available, one of which is Sky Rocket Melon. This melon is round and green or yellow in color. Melon fruit tastes sweet and juicy flesh. To assess the sweetness of the melon, it is necessary to separate the flesh and taste of the melon. When buying melons, buyers cannot choose melons correctly because they cannot immediately know the level of sweetness. From these problems, a study was formed to determine the level of sweetness of melons accurately by reading the texture of the skin. The advantage of this research is that you don't need to split and taste the melon first to know the sweetness, but only use the image of the melon skin texture. This study uses the Local Binary Pattern method to perform feature extraction calculations, P=8,16,32 is the number of neighbors to be compared, and R=1,2,4 is the center radius or pixel value of adjacent distances. The results of feature extraction are inputted into the K-Nearest Neighbor or K-NN algorithm, and classified into one of three categories (low, average, high) with values ​​of K=3, K=5, and K= 7. Testing uses up to 360 images of training data, up to 90 images of test data, and the test kit uses 15 melons as images. The results of the P and R tests of the Local Binary Pattern algorithm and the K value on K-NN provide the highest level of accuracy of 91.11%. By using the value of P = 8, the value of R = 1, and the value of K = 3. Testing the hardware system using Raspberry Pi 4, with the result that the accuracy rate is 80% and the average calculation time is 0.22084 seconds.
Rancang Bangun Alat Pengenalan Papan Nama Ruangan pada Berbagai Kondisi Pencahayaan menggunakan Algoritme YOLOv3 berbasis NVIDIA Jetson Nano Asep Ranta Munajat; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Humans have a sense of sight that is able to obtain visual information in capturing objects far and near, but it is different from blind people. Blind people find it difficult to carry out daily activities such as walking in unfamiliar places and finding a room that is done independently. Several studies have made object detection devices to help visually impaired people through sound using a camera, lighting conditions affect the accuracy of detecting an object. The solution is to create a tool for blind people to recognize room signage in various lighting conditions using the Nvidia Jetson Nano-based YOLOv3 algorithm. This system adapts to 3 lighting conditions, namely dark (0-50 lux), dim (51-100 lux) and bright (101-500 lux) using a distance of 1 to 2.5 meters. The result of recognizing this room's nameplate is in the form of voice output. The test results are based on the LDR sensor response to lighting conditions of 100%, room nameplate recognition voice output of 100%, computational time obtained on LED light response and CLAHE mode of 1.891 seconds and room nameplate recognition of 0.40008 seconds. The average value of the whole system in recognizing the nameplate of the room under lighting conditions is 95.67%.
Deteksi Tangga Naik dan Turunan untuk Notifikasi Keamanan pada Tunanetra menggunakan YOLO Versi 4 berbasis Jetson Nano B01 Muhammad Nazrenda Ramadhan; Fitri Utaminingrum; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Blind people currently use The White Cane to help with their daily activities. However, The White Cane has a drawback where its detection range is limited to the length of the cane. In addition, The White Cane cannot distinguish objects that are in front of blind people. This study aims to develop a Jetson Nano B01-based system that can detect floors and stairs, both going up and down to assist the activities of blind people. With the help of artificial intelligence, this system is expected to be able to notify blind people that there is a stair in front of them by activate a buzzer. Then, to be able to produce the right stair detection, pattern recognition is needed with the You Only Look Once (YOLO) method which has a fast detection speed. When the stairs are identified, the system will give a notification in the form of a buzzer sound to notify that there is a stair ahead. The tests carried out, obtained the results of the classification accuracy of object detection (Floor, Upstairs, and Downstairs) of 90%, the average computation time of 0.177s, and the integration accuracy of YOLOv4 detection with a buzzer of 100%.
Rancang Bangun Sistem Pengklasifikasi Jenis Sampah Organik dan Sampah Daur Ulang menggunakan Resnet50 Paulus Ojak Parasian; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Waste is a universal problem. According to World Bank, there would be 3,4 billion tonnes of waste annually in 2050. To reduce waste in Indonesia landfills there are scavengers that sort the recyclable waste from the organic ones manually, but their capacity is limited. To increase the sorting rate there should be a sorting machine right from the source, which is civilian homes, or offices. To create such machine the writer will produce a sorting machine for organic waste and recyclable waste using the Resnet50 method. To train the Resnet50 model the writer use a dataset from Kaggle which consist of 22500 training and testing data. The Resnet50 model will be trained using 20 epochs with learning rate of 0,001 for the first 10 epoch and a learning rate of 0,0001 for the next 10 epochs, which resulted in a model with 99% accuration, 3% loss, 96% accuration validation, and 12% loss validation. The machine will then be tested with different object to camera lengths starting from 16 cm, 18 cm, 20 cm, 22 cm, 24 cm, and 26 cm. The best accuration is gained from the length of 20 cm and 22 cm with 85% accuration and overall average classification time of 1,17 seconds.
Sistem Pendeteksi Penyakit Sinusitis berdasarkan Kondisi Ingus dan Suhu Tubuh menggunakan Support Vector Machine (SVM) Bagas Nur Rahman; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The nose is an important organ in life for humans. In the nose there is a disease that causes the sinus wall to experience inflammation commonly known as sinusitis. Smoking habits, environmental pollution and cold air become other factors that can affect the onset of sinusitis. If sinusitis is not immediately treated and treated properly, it can lead to complications which then lead to infection. So it is necessary to do an initial check to detect sinusitis using Magnetic Resonance Imaging (MRI). In addition, the cost is quite expensive and a long period of time is the basis of this research. Therefore, a tool is needed that can detect sinusitis early. This research will use the MLX90614 sensor for body temperature feature extraction and the TCS3200 sensor for color feature extraction. The two features then be processed by Arduino Uno to carry out the classification process into two classes, namely the Normal class and the Sinusitis class. The classification process in this study uses the Support Vector Machine method. The results of the accuracy of the SVM classification get 85% of the 20 data tested. For testing the computational time obtained an average value of 42 milliseconds from as many as 20 test data used.
Rancang Bangun Sistem Klasifikasi Tipe Permukaan Jalan menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan Support Vector Machine (SVM) berbasis Raspberry Pi Akbar Wira Bramantya; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Roads are a pathways made to facilitate traffic access from one place to another. Indonesia has a roads as far as 537.838 km. However, all of these road didn't have same condition, there are still many road in bad condition. On the other side, our current transportation is just evolve to the Smart Car era, where this technology focuses on autonomous driving systems and driver safety. Therefore, needed a system that can overcome these various road conditions. So, in this research will do a testing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) to classify the roads are in asphalt condition, rocky condition, or paved condition. The GLCM feature used in this research is the best 4 features from total 6 features to be tested namely dissimilarity, correlation, homogeneity, contrast, ASM, and energy. This test is started using SVM linear kernel, polynomial kernel, and RBF kernel, the value of d = 1, and value of θ = 0o, 45o, 90o, 135o. After all of these tests, the best result were obtained. They are GLCM using dissimilarity, correlation, contrast, and energy features, the value of d = 1, and combination from all of angles, angle 0o, 45o, 90o, 135o simultaneously also for SVM using linear kernel. The accuracy obtained from this combination reaches 97% in SVM training set and 98,3% in manual testing using test data. System integration testing using camera input got 88,6% in accuracy and system integration testing on electric motors performance got 87% in accuracy.
Implementasi Fuzzy Logic pada Sistem Monitoring Kualitas Air Kolam Renang dan Aplikasi Android Mohammad Andy Purwanto; Mochammad Hannats Hanafi Ichsan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Every living thing need water to life. One of the uses of water is to fill swimming pools. Swimming pool cleanliness becomes a problem, such as pollution of swimming pool water with urine. Not only that, swimming pool water must also pay attention to the temperature, pH and turbidity of the water so that the quality is maintained. However, the quality monitoring process seems slow and is not carried out regularly and continuously. Sometimes, the monitoring process carried out gets inaccurate results and impratical in practice. Therefore, a swimming pool water quality monitoring system was created with a fuzzy logic algorithm, assisted by the eFLL (embedded Fuzzy Logic Library) library which is used to determine whether the water quality is good or bad by processing the data obtained by the DS18B20 temperature sensor, pH -4502C and also the dfrobot turbidity sensor. Arduino Nano microcontroller is used with Atmega328 as a data processor obtained from the sensor. Then, with serial communication via bluetooth, users can see the monitoring results on an android smart phone. The test is carried out by measuring the quality of the swimming pool water in the morning and evening with an interval of 8 hours to get 40 test data. The results obtained are in the form of monitoring and storing data on smart phones. The accuracy of the DS18B20 temperature sensor is 98.75% with an average error of 1.25%, the pH-4502C sensor gets an accuracy of 97.52% with an average error of 2.48% and also the turbidity sensor test results that when the water is getting cloudy, the voltage value will be smaller . In addition, the accuracy of the fuzzy logic algorithm compared to MATLAB is quite high that is 98.49% with an average error of 1.51%..
Rancang Bangun Deteksi Kemanisan Buah Semangka menggunakan Metode Gray Level Co-Occurrence Matrix dan Backpropagation Neural Network berbasis Raspberry Pi Qurrotul A'yun; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Watermelon in Latin Citrullus lanatus is a fruit with green skin characteristics with red or yellow arrays and flesh color. Watermelon is a type of fruit favored by the people of Indonesia. The high market demand for watermelon must be balanced with watermelon production. which continues to increase as well. Watermelon that has been harvested needs handling to classify the quality of watermelon on the market, one of which is the classification of watermelon sweetness. Measuring the size of the sweetness of watermelons can be destructively using the Brix refractometer, but this is not practical because you have to split the fruit first. Therefore, an innovation is needed to detect watermelon sweetness with digital image processing. In this study the sweetness of watermelon will be divided into 3 classes, namely low, average and high. This study uses the Gray Level Co-Occurrence Matrix method for feature extraction using 6 features, namely Dissimilarity, Homogeneity, Contrast, Correlation, Energy, and Angular Second Moment (ASM) with values ​​of d= 1, 2, 3 and angle = 0°. , 45°, 90°, and 135°. For sweetness class classification using Backpropagation Neural Network method. In Epoch and learning rate testing, the best training accuracy is 86% at Epoch 12,000 and learning rate is 0.01 with values ​​d=1 and = 0°. Then the best value is used in the system integration test and the highest accuracy is obtained, namely 85.7% at a distance of 15cm and the average computation time required is 10.05997 seconds.
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