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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Abdi Masyarakat Indonesia
Fitriyah, Hurriyatul
Universitas Brawijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Sistem Klasifikasi Kualitas Ikan Tongkol Beku Berdasarkan Fitur Nilai Warna HSV Menggunakan Metode Naive Bayes Faizal Andy Susilo; Hurriyatul Fitriyah; Gembong Edhi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The factor of fish quality can be affected by storage procedure and processing treatment is still done manually. Of course, it can make the fish quality will decrease and the sorting process will be wrong. From this problem, it is needed some research and system that can reduce errors to classify fish quality. On this research, we are using image processing and Bayesian method to classify fish quality. Fish will be placed on a styrofoam box that has been equipped with a webcam camera and lamp as lighting. Image processing is used to convert an image from RGB space to HSV space, and we crop the image to get the head section. And after that, we use the hue histogram colour information for the parameter to classify. so the value of bin1, bin 2, and bin 3 and also the standard deviation from histogram value are using as input for classification using Naive Bayes and will process in Raspberry Pi 3 and finally, we can get the fish quality. We are doing some testing. From testing how to implement image processing for this system we get some conclusion that the image which uses the lighting from 5 Watt lamp with white fabric clothes has a good image result, and the result for hue value information from images has to be added. And from testing Naive Bayes methods accuracy was 72.727% and the computation time was 468.864 ms.
Sistem Klasifikasi Aktivitas Manusia Menggunakan Sensor Accelerometer dan Gyroscope dengan Metode K-Nearest Neighbor Berbasis Arduino Fadhilatur Rahmah; Hurriyatul Fitriyah; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Human activity recognition technology allows a system to detect simple activities by humans, such as standing, sitting, lying, walking, running and others using a camera or sensor. The camera-based human activity recognition system has a lack of adaptability to light so that the accuracy obtained is not good, while wearable sensor-based systems that use multiple sensors cause discomfort when used and battery life problems. In this study a system can be made that can classify simple activities carried out by humans using the MPU6050 sensor which has an accelerometer and gyroscope sensor and uses the k-Nearest Neighbor classification method. Input from this system is the value of the accelerometer and gyroscope sensor readings sent using the NRF24L01 wireless communication module to Arduino Mega as a device that classifies and displays the classification results in Serial Monitor Arduino IDE. In this study the test was carried out using one sensor and two sensors. From the results of the tests performed, obtained the highest accuracy results of 93.75% for systems that use one sensor with sensor placement on the thighs and 96.25% for systems that use two sensors with sensor placement on the thighs and waist. For testing the computation time of the k-Nearest Neighbor method in classifying human activities, the average time taken was 173.6 milliseconds for classification using one sensor and 353.2 milliseconds for classification using two sensors.
Pengontrolan Derajat Keasaman (pH) Air Secara Otomatis Pada Kolam Ikan Gurame Menggunakan Metode Fuzzy Mamdani Dimas Guntoro; Gembong Edhi Setiawan; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Water treatment is the main thing that must be considered when gurame fish culture and the degree of acidity plays a very important role for the health of underwater ecosystems, because if the degree of acidity is not available as needed then it can be toxic to fish. Based on these problems, the need for research related to the automation system to control the value of acidity degree in accordance with the needs of gurame fish. In this research there are 2 sensors namely pH meter SEN0161 sensor and Ultrasonic sensor HC-SR04 with arduino microcontroller using Fuzzy Mamdani method. The Fuzzy Mamdani method was chosen to control the acidity of the water according to the needs of the gurame fish by adding as much water as determined from Fuzzy Mamdani calculation as the center point z. From the results of several tests performed known error percentage reading pH meter SEN0161 is 2,569% and the error percent reading Ultrasonic sensor HC-SR04 is equal to 2,992%. In testing the water acidity control system using Fuzzy Mamdani done 10 times, 80% accuracy with average computation time of 0,693 seconds.
Deteksi Jarak Bola Pada Robot Kiper Sepak Bola Menggunakan Hough Circle Transformation Berbasis Raspberry Pi Tunggal Manda Ary Triyono; Hurriyatul Fitriyah; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The goalkeeper robot is part of robosoccer team which is in charge of catching the ball correctly and also effectively. To make the movement of goalkeeper robot correct and also effective, the goalkeeper robot must be able to know how far the distance between the ball and itself. Therefore, ball distance detection system on the goalkeeper robot using Hough Circle Transformation based on Raspberry Pi was made. This research used webcam Logitech C525 and Raspberry Pi 3 Model B. The images captured by camera will be processed to obtain binary images with minimum noise. Then cartesian coordinates and diameter of the ball will be obtained by using Hough Circle Transformation. The distance between ball and goalkeeper robot is calculated using comparison between ball diameter that detected by system and diameter at the references distance. Ball coordinate in the frame can also be used to get position and angle direction of the ball. Based on the test, best angel of view of camera for distance and angle detection is 47,2o with the accuracy of distance detection is 97,69% and accuracy of angle detection is 94,69%. The average computation time is 158.54 ms.
Implementasi K-Nearest Neighbor untuk Klasifikasi Ekspresi Wajah Berdasarkan Data Muscle Sensor dan Berbasis Arduino Aprilo Paskalis Polii; Hurriyatul Fitriyah; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Human facial expressions are formed by face muscles. Therefore, as an interest to develop Human-computer interaction, the system of human facial expression classification based on face muscles' movement is made for those reasons. The output from facial muscles is obtained by the muscle sensor. The classification in this research has been done by using K-Nearest Neighbor Algorithm system. The Muscle sensor is connected to the face by using electrodes. Then, the sensor's output is processed in Arduino and shows the result on LCD Monitor as an output. By the testing of sensor's functionality, it is found that the sensor responds according to the muscle performance. The sensor's value is increased along with the number of gained loads. Besides that, by the testing of LCD monitor's functionality, the result is obtained that LCD Monitor works well by displaying the output in accordance with the command. Then by the accuracy testing, the best the result is from K equals to 3 with 81% of accuracy level. By the computation time testing, the result of taking the output from sensor, processing, and display the classification takes 1.68 seconds as the average time.
Sistem Penghitung Jumlah Orang Melewati Pintu Menggunakan Metode Background Subtraction Berbasis Raspberry Pi Diego Yanda Setiawan; Hurriyatul Fitriyah; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The center state of the crowd such as shopping centers, libraries and so on are the places that many people visit. This data is very important because it can be used as interest indicator. In this research is needed a system which can provide an information about the number of visitor so manager can do monitoring of the place. Possible technologies in making this system is to uses digital image processing and computer vision. Background Subtraction Method useful to detect moving objects. From the test results, the Background Subtraction method can detect moving objects very well, opening and closing can improve image results. The success rate when one person enter is 87,5% and exit is 87,5%, The Accuracy when two persons enter at the same time is 87,5% and exit is 100%. Overall the Average accuracy obtained by this system with a certain angle camera when one person enter is 75% and exit is 78.75%, The Accuracy when two persons enter and exit at the same time is 71% and 71%. Also the best camera angle while capture images when one person pass is 70°,80°, 90°. when two person pass at the same time 50°, 70°.
Sistem Klasifikasi Jenis Karat Menggunakan Metode Decision Tree Berbasis Raspberry Pi Denis Andi Setiawan; Hurriyatul Fitriyah; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A place to collect is one of the management of an object that is no longer used or rusty. At the place of the clumping, the corroded object is sorted. However, rust detection is done manually through pieces with the naked eye. This method is vulnerable to human error. Based on these problems, it is necessary to have a system that can sort out the zinc automatically to facilitate the owner. In making this system, the image taken is zinc which has been corroded. This system takes the image of rust using a webcam. Rust from the image is detected using the thresholding method, then classified into mild rust or heavy rust which results will be displayed via LCD. The percentage limit of the rust classification will be determined by the decision tree method. Testing is done to find the percentage of system accuracy, and it can be concluded that the zinc painted in the rusty section has a percentage difference of 0.02 when compared to original rust, and original rust has class accuracy of 90% compared to the original class that has been determined by experts, and the execution time of this program is around 0.59.
Sistem Deteksi Gejala Hipoksia Berdasarkan Saturasi Oksigen dan Detak Jantung Menggunakan Metode Fuzzy Berbasis Arduino Dian Bagus Setyo Budi; Rizal Maulana; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of intelligent systems is highly developed, one of them in the health or medical fields. In the medical field, a tool is needed to observe the patient's condition in a noninvasive way without injuring the patient. If it is ignored continuously then hypoxia can interfere with the function of the brain, liver, and other organs quickly. So that in this study a hypoxic early symptom detection tool that uses a noninvasive method using the Max30100 sensor that is clipped to the fingertip can be made to determine the results of the initial symptoms of hypoxia. To detect the initial symptoms of hypoxia in this tool, the Sugeno fuzzy method is used so that output is obtained according to the existing rules. Sugeno fuzzy method will process data taken from the Max30100 sensor. There are 3 hardware devices that are on this device, the Arduino microcontroller as the controller, the Max30100 sensor to get the input and Bluetooth for sending data to the smarthphone. Software uses the Arduino IDE to program detection devices and APP inventors to program android applications so they can display data. In this study, the test results were obtained and the results of the test obtained a tool error of 2.96% for oxygen duration and 2.86% for heart rate obtained. From the fuzzy method on 12 data experiments, 100% accuracy was obtained and the Sugeno fuzzy method was able to process the input data properly.
Sistem Penghitung Protein Telur Berdasarkan Volume Menggunakan Komputasi Citra Metode Cakram Berbasis Raspberry Pi Dan Perangkat Android Muhammad Riyyan Royhan; Hurriyatul Fitriyah; Agi Putra Kharisma
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

Inappropriate daily intake of protein can cause various diseases. Information about protein content of a food is very useful because with that information, people can know and regulate the level of proteins that they consume. Therefore, chicken eggs as a cheap and easy source of animal protein are important foods to know about their protein value. Research on eggs that have been widely made makes information about egg protein content can be known from the weight of the egg itself. Egg weight can also be found based on its volume. Egg volume can be calculated from the surface area of the egg using disk method. The surface area, and volume of egg can be known by computing the image of the egg. The Image computation to find egg volume can be done by using Raspberry Pi which is assisted with an Android device as an image taker, and also as a system interface. This research embodies a system that is capable of processing egg images to calculate the value of volume, weight, and protein content of the eggs. The system is tested for its accuracy based on its measurement results of the volume and the weight of the eggs, compared to the measurement results of ordinary measuring instruments. The final results of the test show that the system has an accuracy value of 95.76% for measurement of egg weight and 93.94% for measurement of egg volume.
Implementasi Support Vector Machine Berdasarkan Ciri Histogram of Oriented Gradients Untuk Verifikasi Citra Tanda Tangan Berbasis Raspberry Pi Mohammad Lutfi Zulfikri; Hurriyatul Fitriyah; Wijaya Kurniawan
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

Signature is a personal attribute that has long been widely accepted as a tool for verification of personal identity. But signatures are also easy to fake to be misused. To avoid this, a system is created to verify signatures. This system uses the image of the signature captured by the camera as an input triggered by the push button, Raspberry Pi as a digital image processing unit, and LCD 16x2 as the system output. This study uses the Histogram of Oriented Gradients (HOG) feature descriptor with precedence of image preprocessing. The output of the HOG method is a feature vector that represents the signature characteristics of the image, this feature vector which will be classified with the Support Vector Machine (SVM) classifier for data training and data prediction. There are two main parts of system software, the training data section, and the testing data section for signature verification. The implementation results obtained that the system can verify signatures with an accuracy of 87.33%. System requires 1.45 seconds in average to train data on each signatory name and for the verification process, the average system takes 0.238 seconds for the genuine signature and 0.242 seconds for forgery signatures.
Co-Authors Abdurrahman Diewa Prakarsa Abimanyu Sri Setyo Achmad Baichuni Zain Aditia Reza Nugraha Aditya Rafly Syahdana Afflatuslloh Adi Salung Agi Putra Kharisma Agif Sasmito Agung Setia Budi Ahmad Fahmi AdamSyah Ahmad Fatchi Machzar Ahmad Haris Wahyudi Ahmad Wildan Farras Mumtaz Alfatehan Arsya Baharin Ali Ilham Ainur Rahman Allif Maulana Ananda Ribelta Andhika Rizky Fariz Andi Dwi Angga Prastya Andy Hartono Aprilo Paskalis Polii Aries Suprayogi Bagus Sawung Timur Barlian Henryranu Prasetio Belsazar Elgiborado Giovani Djoedir Bilawal Haesri Bimo Dimas Nugraraga Boris Wiyan Pradana Chandra Gusti Nanda Putra Cut Fahrani Dhania Dahnial Syauqy David Isura Dede Satriawan Denis Andi Setiawan Dewi Pusparini Dian Bagus Setyo Budi Diego Yanda Setiawan Dimas Bagus Jatmiko Dimas Dwi Saputra Dimas Firmanda Al Riza Dimas Guntoro Dipatya Sakasana Dody Kristian Manalu Dwi Fitriani Edhi Setyaw, Gembong Eko Ardiansyah Eko Setiawan Erdano Sedya Dwiprasajawara Esa Prakasa Fadhilatur Rahmah Faizal Andy Susilo Fajra Rizky Falachudin Akbar Fatchullah Wahid Afifi Faza Gustaf Marrera Fikriza Ilham Prasetyo Gembong Edhi Setiawan Gembong Edhi Setyaw Gembong Edhi Setyawan Gunawan Wahyu Andreanto Habib Muhammad Al-Jabbar Hafizh Hamzah Wicaksono Hamdan Zuhdi Dewanul Arifin Hamzah Attamimi Handi Handi Handy Yusuf Herwin Yurianda Ichwanul Muchlis Imam Pratama Setiady Indera Ulung Mahendra Iqbal Koza Irham Manthiqo Noor Issa Arwani Ivana Agustina Julisya Thana Khriswanti Khairul Anwar Komang Candra Brata Lashot Ria Ingrid Melanika Lintang Cahyaning Ratri Luqmanul Halim Zain M Ilham Fadilah Akbar M Nuzulul Marofi M. Fiqhi Hidayatulah Marrisaeka Mawarni Mimi Hamidah Moch Zamroni Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Mohamad Abyan Naufal Fachly Mohamad Misfaul May Dana Mohammad Isya Alfian Mohammad Lutfi Zulfikri Muh. Syifau Mubarok Muhamad Delta Rudi Priyanto Muhamad Ichwan Sudibyo Muhammad Ammar Hassan Muhammad Daffa Bintang Nugroho Muhammad Fatham Mubina Akbar Muhammad Irfan Reza Muhammad Junifadhil Caesariano Muhammad Raihan Al Hakim Muhammad Rifqi Radifan Masruri Muhammad Riyyan Royhan Muhammad Rizki Chairurrafi Muhammad Rizky Rais Muhammad Rizqi Zamzami Muhlis Agung Saputro Musada Teguh Andi Afandi Nafisa Nafisa Nashir Umam Hasbi Nico Dian Nugraha Nur Aini Afifah Isbindra Nur Syifa Syafaat Okky Nizka Pratama Oktaviany Setyowati Olivia Rumiris Sitanggang Pandy Aldrige Simanungkalit Pramandha Saputra Putra Wijaya Putri Harviana Raden Galih Paramananda Rakhmadhany Primananda Rando Rando Refsi Ilham Cahya Rejeki Puspa Dinasty Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Damayanti Rian Ari Hardianyah Ricky Zefani Aria Zurendra Rifqi Alvaro Rifqi Imam Ramadhan Rizal Maulana Rizka Ayudya Pratiwi Rizqy Maulana Rosa Mulyanis Chan Sabriansyah Rizqika Akbar Salsabiil Hasanah Samuel Lamhot Ladd Palmer Simarmata Satyaki Kusumayudha Septian Mukti Pratama Shafa Sabilla Zuain Sulthan Ghiffari Awdihansyah Syarief Taufik Hidayatullah Tatit Kisyaprakasa Thomas Oddy Chrisdwianto Tibyani Tibyani Tri Oktavia Mayasari Tunggal Manda Ary Triyono Utaminingrum, Fitri Wahyu Hari Suwito Widasari, Edita Rosana Wijaya Kurniawan Wildo Satrio Wisnumurti Wisnumurti Xavierro Lawrenza Yusuf Hendrawan Zultoni Febriansyah