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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 Tangga Turun, Tangga Naik, dan Lantai menggunakan Gray Level Co-Occurrence Matrix dan K-Nearest Neighbors berbasis Raspberry Pi Ester Nadya Fiorentina Lumban Gaol; Fitri Utaminingrum; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A smart wheelchair is a mobility aid that is easy to operate and aids in its users' independence. In its development, the smart wheelchair design not only pays attention to the aspect of convenience but also in terms of safety. Any obstacles that include accessibility obstacles in the environment must be detectable by a smart wheelchair to increase safety. One of these accessibility obstacles is the sudden change in the level of surfaces such as stairs. This research aims to create a system capable of detecting stairs descent, stairs ascent, and floors using the Gray Level Co-occurrence Matrix (GLCM) method to obtain the extraction of characteristic features from stairs descent, stairs ascent, and floors. Then applies the K-Nearest Neighbors (K-NN) classification method to predict what states are detected. Using the GLCM methods with 4 feature (contrast, dissimilarity energy, and homogeneity), distance (d) = 4, q=90° and K-NN with k=4, the system obtained an average accuracy of 93.33% and an average computation time of 1551ms.
Deteksi Dini Tangga Turun menggunakan Metode HOG (Histogram of Oriented Gradients) dan SVM (Support Vector Machine) berbasis Raspberry Pi Kezia Amelia Putri; Fitri Utaminingrum; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Electric Powered Wheelchairs or EPW has been used for many disable patients and still developing. EPW was developing to achieve flexibility to control its movement. EPW can be moved by eyes and head nowadays. One of the main accidents that often occurred to EPW's user is falling from EPW due to some obstacles that blocking the road or some descents such as stair descent which user did not see before. Hence, EPW needs a system to increase the user's safety. In previous research ultrasonic sensors were used to detect objects. But it needed a lot of sensors to detect obstacles on wide range and did not able to detect descents. Regarding that, researchers began to use camera to detect obstacles. This research use image processing methods to detect stair descent and generate warning sound through a speaker. HOG was used as a method to extract features from data and SVM algorithm as machine learning classifier. Pre-processing such as cropping, resizing, and blurring were used. Total features for each data were 3.780 features which generated from an image with 128x64 pixel size. This system had 80% accuracy of recognizing object and had 0,679672 second average computation.
Sistem Deteksi dan Klasifikasi Jenis Kendaraan berbasis Citra dengan menggunakan Metode Faster-RCNN pada Raspberry Pi 4B Mela Tri Audina; Fitri Utaminingrum; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Vehicles that exceed road capacity will have a negative impact on their surroundings, one of which causes accidents. Examples of cases of accidents that often occur are vehicles traveling in lanes that are not supposed to be, such as vehicles other than the busway crossing the busway lane and when driving on the Pantura highway which has more than 2 lanes, sometimes drivers find it very difficult to pay attention to the lane on the left, if you want. overtaking the vehicle in front of him. Therefore a system is needed to notify drivers to be more careful when driving. In this system there is a notification if there are numbers and types of vehicles in front. This system uses the Faster Regional Convolutional Neural Network modeling made on Tensorflow by processing it on a mini computer or Raspberry Pi 4B. The accuracy result in this system is 0.9025 or 90.25% with an average computation time in the Raspberry Pi 4B of 7,638 seconds per image.
Deteksi dan Pengenalan Plat Nama Ruangan menggunakan Faster-RCNN dan Pytesseract pada Purwarupa Kursi Roda Pintar Muhammad Sulthon Yazid Basthomi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Covid-19 pandemic, which hinders various human activities, makes people keep themselves withdrawn from others. The massive social distancing also applies in hospitals, including between patients and nurses in the use of wheelchairs. The issued autonomous wheelchairs must do another task, that is detecting room plates and recognizing room nameplates. The detection in this research uses Faster Regional Convolutional Neural Network (Faster-RCNN) model made on Tensorflow. Meanwhile, room name recognition will actualize using PyTesseract. Testing was carried out on a smart wheelchair prototype using the Raspberry Pi 4B. The hardware integration result of the buzzer_1 function is 100%, the buzzer_2 function is 100%, the buzzer_3 function is 100%, the buzzer_4 function is 100%, the buzzer_5 function is 100%, and the motor function is 100%. While the integration of room plate detection software was 95% and room name recognition was 81%. Then performed image testing to measure accuracy, prediction ratio, and computation time. The results of detection accuracy using Faster-RCNN are 87%, the predictive ratio for recognition using PyTesseract is 77.73%, and the average computation time for detection is 6.825 seconds per image and for recognition of 2.54 seconds per image.
Sistem Pendeteksi Dini Lubang pada Jalan menggunakan Gray Level Co-Occurrence Matrix berbasis Raspberry Pi Audrey Athallah Asyam Fauzan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Nowadays, the land transportation mode is the most popular. This is indicated by the data of motorized vehicles in Indonesia is increasing. Damaged roads have a huge impact in driving, because it can cause inconvenience to the driver and it can cause the effect of damage to the vehicle. Based on the causes of road accident data, infrastructure and environment factors are one of the causes of accidents, and potholes are one of them. The solution to the problem is to create a system that can detect a pothole. This research uses Gray Level Co-Occurrence method to obtain the feature characteristic of the pothole and Support Vector Machine to classify whether the detected object is a pothole or not. The system requires a camera to capture an image that will be detected and perform an object recognition. If the system can detect a pothole, the driver will get a notification sound from the speaker. Tests were carried out several times based on the d and theta values in the GLCM feature extraction and based on vehicle speed ranges between (0-30 km/h and 30-60 km/h). Based on the test, the best d and theta values are d=2 for theta =90. The best accuracy value is obtained when the speed range is (0-30 km/h) with an accuracy value 81,70%. The accuracy of the harware detection integration test is 87,5%. In testing the average computation time of the system to recognize the pothole is 134,17 ms.
Alat Pendeteksi Uang untuk Tunanetra menggunakan Metode Histogram of Oriented Gradients dan K-Nearest Neighbor Nico Dian Nugraha; Fitri Utaminingrum; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Banknotes that distributed by scammers would causes restlessness in society, including blind people. With impairment vision, blind people would hard to distinguish between genuine and fake money. From that problem there would be a research about nominal and authenticity detection system for blind people. The detection system consists of camera as a sensor device to detect picture from the banknote, followed by ultraviolet lamp to tell about the genuine banknote, and speaker as the output from this system. Output would generate voice as the nominations and tell if it is genuine or fake banknote. Code program on this system were written in Pyhton language with Raspberry Pi hardware, Webcam sensor camera, and ultraviolet lamp. Detecting banknotes would use Histogram of Oriented Gradients method and using K-Nearest Neighbour method to classify banknote. Around 3370 data training were used to detect about authenticity of the banknotes and the detections were tried for 56 times. implementation of K-Nearest Neighbor method using k=3 obtained an accuracy result of 98.21% with an average compute time of 3608 ms.
Pengenalan Gesture Tangan Untuk Otomatisasi Switching Saklar Menggunakan Metode KNN Berbasis Raspberry Pi Misran Misran; Fitri Utaminingrum; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 5 (2021): Mei 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Research on switching automation has been done a lot, both by using smartphone control, infrared sensors to using voice commands. The switching automation process used to turn lights on or off can also be done using hand gestures. By using the K-Nearest Neighbor method the computer can understand human interaction quite well using the method of decision making from existing patterns. In this study, the K-Nearest Neighbor method was used to translate hand signals or hand gestures into a command to control the LED. The test was carried out using 5 volunteers, each of whom tested each hand gesture given. To get the results of gesture recognition, there are several steps that must be taken, namely skin detection, preprocessing process, Feature Extraction, K-NN, and finally the system output. 3. The accuracy produced by the system is very good, where by conducting several experiments, the accuracy results obtained for five volunteers is 80%. Research on switching automation has been done a lot, both by using smartphone control, infrared sensors to using voice commands. The switching automation process used to turn lights on or off can also be done using hand gestures. By using the K-Nearest Neighbor method the computer can understand human interaction quite well using the method of decision making from existing patterns. In this study, the K-Nearest Neighbor method was used to translate hand signals or hand gestures into a command to control the LED. The test was carried out using 5 volunteers, each of whom tested each hand gesture given. To get the results of gesture recognition, there are several steps that must be taken, namely skin detection, preprocessing process, Feature Extraction, K-NN, and finally the system output. 3. The accuracy produced by the system is very good, where by conducting several experiments, the accuracy results obtained for five volunteers is 80%.
Implementasi Facial Landmark dalam Pengenalan Wajah pada Sistem Pembayaran Elektronik William Hutamaputra; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 5 (2021): Mei 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The technology is developing rapidly with various sophisticated features in every branch of science. One of the developing branches of science is the payment system. The face of people being has a specific personal identification that has its own characteristics. Therefore, faces can be used as objects as personal identification marks to increase the level of security. The working principle of Face Recognition is to detect faces first before carrying out the recognition to be compared with the database. The method used for Face Recognition is facial landmark. Facial landmark is a method of localizing the prominent points on the face. Before the image is processed, the image must be detected whether there is a face. The face detection method uses haar cascade. The results of facial landmarks will be classified based on training data with k-Nearest Neighbor. The results of these studies resulted in facial recognition accuracy levels of 76.47%, 64.71%, and 47.06% and computation times of 0.5016, 0.1322, and 0.1298 seconds with values of k = 5, 7 and 9. In the integrated system between facial recognition and electronic payment systems with the value of k which has the highest accuracy, namely 5, has a system integration level of 71.43%. In the field of technology, there are many things that need to be resolved to make human activities easier. Therefore, this thesis will discuss an electronic payment system based on Face recognition in trading with the facial landmark method.
Lima Fitur Gray Level Co-Occurence Matrix Untuk Deteksi Kemanisan Buah Semangka Tanpa Biji Dengan Klasifikasi Support Vector Machine Berbasis Raspberry Pi Amalia Septi Mulyani; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Watermelon in Latin from Citrullus Lanatus Tunb is one of the vines that is developing very rapidly in Indonesia, besides that Indonesia is also the largest producer of watermelons in the world. It was found that the interest of many people is fond of watermelon. It can be seen that watermelon yield data in 2014 was 653,974. The type of watermelon that is often in demand is watermelon which has no seeds, which has a soft texture, does not contain too much water, and has a paler flesh than the seeded watermelon. This research carried out feature extraction on the texture of watermelon rind using the Gray Level Co-Occurrence Matrix (GLCM) method, then using the Support Vector Machine (SVM) to classify the sweetness or unsweetness of watermelon fruit without seeds with a distance of 10-11 cm. This research requires a camera on the system to be able to take images for detection and class classification can be done. If the system can detect the sweetness of watermelon without seeds, it will be displayed on 16x2 LCD. The GLCM features used in this study are correlation, contrast, homogeneity, energy, and dissimilarity. Tests conducted using the SVM kernel, namely linear, RBF, and polynomial kernels. To get the best d and θ values ​​in this study, several trials were carried out with values ​​of d=1,2 and θ=0⁰, 45⁰, 90⁰, 135⁰. The best d and θ values ​​after testing the detection of the sweetness of watermelon without seeds were d=2 and θ=45⁰ with an accuracy of 95%. Hardware integration testing is carried out from various sides of the image taking, namely from the front, back, and top. Obtained the best accuracy in testing the integration of hardware using the best d and θ values, which is 80% on the back side using a polynomial kernel. In testing the computation time of the system with a value of d=2 and θ=45⁰, the best kernel was obtained, namely the polynomial kernel to detect the sweetness of watermelon without seeds, which took 13.13 seconds.
Rancang Bangun Sistem Deteksi Kemanisan Buah Melon dengan Metode Gray Level Co-occurrence Matrix (GLCM) dan Support Vector Machine (SVM) Noza Trisnasari Alqoria; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Melon fruit has many benefits and vitamin content that is useful for health. There are various types of melons that are superior in Indonesia, one of which is the Sky Rocket Melon. This type of melon is round and has a thick, green rind. The entire surface of the skin is covered with gray nets and has greenish yellow flesh. Currently, there is no technology that can determine the sweetness level of a melon. To find out, it is necessary to split or slice some of the flesh. This method is considered very impractical, so it takes a technological innovation only with digital image analysis. This research uses the Gray Level Co-occurrence Matrix and Support Vector Machine methods. In this study, using variations in the value of d = 1, 2 and angular direction θ = 0 °, 45 °, 90 °, 135 ° with 5 features, namely contrast, homogeneity, energy, dissimilarity and correlation. The detection of melon fruit classes is divided into sweet and unsweetened classes using the Support Vector Machine (SVM) by testing 3 kernels, namely Linear, RBF and Polynomial. This study used a Raspberry Pi camera to take pictures of melons with a distance of 10 cm detected by the ultrasonic sensor. Melon detection results will be displayed on the 16x4 LCD. In ultrasonic sensor testing, an average error of 1.97% was obtained with an ultrasonic sensor accuracy of 98.03%. In testing this system, the highest accuracy was obtained by the RBF kernel with a variation of the distance d = 2 and the angular direction θ = 45 °, which was 86% with an average computation time of 8.5403 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