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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Rizal Maulana
Teknik Komputer, Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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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 Pengendalian Gerak Sendok Menggunakan Sensor Mpu6050 Dan Metode Pid Berbasis Arduino Ahmad Fahmi AdamSyah; Hurriyatul Fitriyah; Rizal Maulana
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

Difficulty eating is a condition in which a person is unable to eat normally or comfortably. Researchers want to propose a spoon system that can be controlled its stability. The system of this research is the movement of the spoon to be read using the MPU6050 sensor. The value obtained from the sensor will be forwarded to the Arduino Nano processing unit for calculations using the PID controller method where the Kp, Ki, and Kd variables are searched using the trial and error tuning method. The results of these calculations will be used as a spoon mover. Based on the tests that have been carried out, there are conclusions on the test results, first on testing the accuracy of the sensor angle, it can be seen that from 7 experiments from an angle of 0 ° to 180 ° with a multiple of 30 ° on both axes, namely pitch and roll, the percentage error is 0. 59% or less than 1%. The second test of the accuracy of the servo angle based on the setpoint is done, it can be seen that from 7 experiments from an angle of 0 ° to 180 ° with a multiple of 30 ° on both axes, namely roll and pitch, the percentage error is 0.8. Finally, the system speed test has reached stable or commonly called the settling time that has been carried out, it can be seen that from 10 experiments, the initial speed of the system reaches the settling time, which is the average time of both axes, namely 0.99 seconds or under 1 second.
Sistem Klasifikasi Kesegaran Daging Sapi berdasarkan Citra menggunakan Metode Naive Bayes berbasis Raspberry Pi Habib Muhammad Al-Jabbar; Hurriyatul Fitriyah; Rizal Maulana
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

Beef is one of the commodities that has contributed to the improvement of public nutrition, particularly the need for animal protein. Fresh beef is meat that is fresh red in color, starting from being cut up to 10 hours. So far, evaluation of freshness and identification of meat composition has been done manually by means of human visual observations. Due to human limitations, there are often different perceptions of each observer. On this basis, as an effort to obtain beef freshness accurately, this research has made a tool that can detect the freshness of beef with the help of digital image computing. By using the Raspberry Pi as a mini computer, a camera as a sensor and image processing which is then classified by Naive Bayes, this system can work properly, it can be proven by the output of the accurate classification of beef freshness. The choice of the naive Bayes method is based on the fact that this method is a very good classification method in which the class of freshness types is known from the start. This method can also work even though it only uses a little training data. When there is a slight change in training data, the naive Bayes method also adapts quite well. The results of the beef color conversion process are then classified at the color level based on SNI standards. From 40 training data and 20 tested data, an accuracy of 95% and an average computation rate of 0.009094 seconds.
Implementasi Sistem Pendeteksi Uang pada Celengan Pintar menggunakan Metode Jaringan Syaraf Tiruan Andre Ananda Pratama; Rizal Maulana; Rakhmadhany Primananda
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 waste of wealth that is owned by the desire to spend it on needs which causes the behavior of a consumptive lifestyle. (C. Lyons, 2004) argues that early education on money management and savings is beneficial for people who will form intelligence and intellectual characteristics in money management as an adult. Therefore, an effective way to avoid this behavior is to save money so that children can achieve financial success in the future. The existence of this problem encourages the author to realize a solution so that it can encourage children to save by making IoT (Internet of Things) -based piggy banks. This smart piggy bank can detect both types of money (metal and paper) by utilizing the TCS3200 sensor as an RGB color detector for money and a loadcell sensor as a sensor to weigh the weight of money. The infrared sensor is also used to detect objects entering from the piggy bank hole. The features used for this implementation will be classified using an Artificial Neural Network (ANN) classification algorithm. MQTT protocol is used to implement an IoT-based system to display the result of the classification from the system to an android application. The training data that used in the study were 96 data. The TCS3200 sensor tested has an accuracy rate of 93.93% and the loadcell HX71 sensor tested has an accuracy rate of 99.61%. Also, testing with the ANN method which was tested on 24 test data showed an accuracy rate of 91.7%. Meanwhile, the time test in determining the nominal class of 10 tests obtained an average value of 13,48 milliseconds.
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%.
Sistem Klasifikasi Langit Cerah dan Berawan menggunakan Gray Level Co-Occurrence Matrix dan K-Nearest Neighbor berbasis Raspberry Pi Marrisaeka Mawarni; Hurriyatul Fitriyah; Rizal Maulana
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

Weather is a condition in the atmosphere that occurs in an area in a short time. Weather can be predicted by observing weather elements, such as clouds. This research will perform a weather classification based on sky and clouds conditions in the form of sunny and cloudy weather. Sunny weather occurs when the sun shines brights, and there are no clouds in the sky. Cloudy weather occurs when sunlight is covered by clouds that contain water vapor in the atmosphere. The system used fisheye images so that we have a wider field of view images than panoramic images. The classification process for clear and cloudy skies uses the Gray Level Co-occurrence Matrices (GLCM) method to extract the texture features. The features for the classification of sky conditions in the GLCM method are energy, contrast, correlation, homogeneity, and dissimilarity with four angular variations, namely 0,45,90, and 135. The sky classification process is using the K-Nearest Neighbor method with k = 3,5,7, and 9. The Raspberry Pi 4 is used for the whole data processing, and then the classification results are displayed on the LCD. the Accuracy testing was carried out using k-fold with 5 fold, obtaining an average accuracy result of 100% and an average computation time of 2.3516 seconds.
Implementasi Sistem Pendeteksi Sleep Apnea Berdasarkan Interval QRS Dan Durasi Gelombang P Menggunakan Metode Support Vector Machine Muhammad Jibriel Bachtiar; Rizal Maulana; Dahnial Syauqy
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

The pumping of blood throughout the body is carried out by the most important organ in the body, namely the heart. Because the heart is the most important organ, the higher the risk of a disorder, one of these disorders is Sleep Apnea. Sleep Apnea is a respiratory disorder that causes breathing to stop momentarily for several times during sleep. Sleep apnea detection can be done in various ways, one of which is by means of a device called an Electrocardiogram (ECG). The way this tool works is by recording the signal issued by the heart when it beats. Currently, to detect Sleep Apnea requires a large amount of money and can only be done in a hospital. For this reason, a sleep apnea detection study was created which allows users to not have to pay expensive fees and can be done anywhere. The features for the detection of Sleep Apnea in this study use the value of the QRS Interval and the Duration P of the ECG signal generated. With the AD8232 sensor and 3 electrodes attached where 2 are attached to the chest and 1 electrode on the abdomen, the ECG signal will be detected. Signal processing and classification are carried out when signal data is obtained from the sensor using the Support Vector Machine (SVM) classification on the Arduino Uno microcontroller. A total of 48 training data and 24 test data were used for determining and testing the accuracy of the SVM. 20 normal data and 28 Sleep Apnea data were used as training data and 10 normal data and 14 Sleep Apnea data were used as test data. The “Normal” class or “Sleep Apnea” class is obtained and displayed when the SVM classification results have been assigned. 83.3% of accuracy is obtained from the SVM classification trial which has an average computational training time of 9.78 seconds and an average computation testing of 0.7 seconds.
Analisis Struktur dan Pola Berjalan Robot Humanoid Menggunakan Metode Inverse Kinematic pada MATLAB Muhammad Prabu Mutawakkil; Rizal Maulana; Agung Setia Budi
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

Today, the development of robot was growing so quick, one of them is Humanoid Robot. As like the name, Humanoid Robot have feet that have walks and shaped like human does. This robot has two feet that consisted of three DoF (Degree of Freedom) for one of each foot. Humanoid Robot use Inverse Kinematic method to walk which using end of effector as the coordinate. The result that suggested from Inverse Kinematic method were the coordinate destination. The implementation of Humanoid Robot we do it on MATLAB and Simulink to test about the mechanism from the robot if it were applied on software. The difference was shown for the avability of the hardware and its tools and the result sometimes would be different if applied in real world. There are some differences when robot got implemented compared with physical ones. From this research, there are 11 trials to measure about success rate for robot to able to walk successfully from the robot if their variables from physical appereances were modified. As the result, there are 54,54% success rate compared with the original variables that got from trial and error.
Sistem Pendeteksi Atrial Fibrilasi Berdasarkan Fitur Mean, Median, Standar Deviasi, Min, dan Maks Interval RR menggunakan Metode K-NN Hani Firdhausyah; Rizal Maulana; Eko Setiawan
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

91% of sudden deaths in Indonesia are due to Arrhythmias. Atrial Fibrillation is one type of arrhythmia that occurs most often among other types. Atrial fibrillation is characterized by an irregular heartbeat due to abnormal electrical activity. Blood collects in the atria and is not supplied to the ventricles sufficiently. So that the heart fails to pump enough blood to the lungs. For the examination, it is quite expensive, especially in the hospital. Therefore a system was developed to be able to detect AF without injuring the body. The system consists of several functions, namely the ECG signal generator using the AD8232 sensor, data processing using the Arduino Nano board, and displaying the classification or diagnosis results of the class "AF" or "Normal" using an LCD. In the classification process, statistical features in the form of Mean, Median, Standard Deviation, Min and Max are used as test data from the K-Nearest Neighbor Method. This method is used because it does not require a training process. Each test was carried out as much as 20 times. The test results are in the form of 96.83% for the sensor accuracy level. Accuracy of 95%, 90%, and 85% for the accuracy of K-NN classification based on k = 3, 5, and 7 so that the best accuracy is achieved with k = 3.In addition, there is also a relatively short computation time of 16.44 ms so that users it does not take long to find out the results of the classification or the results of the diagnosis.
Sistem Pendeteksi Premature Ventricular Contraction Berdasarkan Fitur Geometri Segitiga Dan Amplitudo R Menggunakan Metode Jaringan Syaraf Tiruan Tedy Kurniawan; Rizal Maulana; Eko Setiawan
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

The heart is the center of the circulatory system for humans. In the heart, there are diseases or conditions that cause abnormal heart rhythms known as arrhythmias. Premature Ventricular Contraction (PVC) is an example of an arrhythmia that occurs when the ventricles have an unnaturally additional heart rate. If PVC occurs regularly, it can cause several other diseases including heart failure, coronary heart disease, and others, so it is necessary to check the health condition of the heart, namely by using an Electrocardiogram (ECG). In addition, the price of ECG medical equipment which is quite expensive and difficult to reach for some people is also the basis of this research. The author will use the triangular geometry feature and calculate the amplitude R value. The classification method applied is Artificial Neural Network (ANN) using the backpropagation algorithm. The test results of the ECG AD8232 sensor acquisition get an error value of 4.47% with 10 tests. This test compares the Beat Per Minute (BPM) value on the AD8232 and BPM sensors in real conditions. The results of the accuracy using the JST 3 hidden layer classification get 90% of the 20 test data used. For testing the computation time the system gets an average value of 235.5 milliseconds from the 20 tested data.
Co-Authors Abdullah Asy Syakur Abdurrahman Arif Kasim Addin Miftachul Firdaus Adhly Hasbi Fadhlillah Adinugroho, Sigit Adit Ilham Nugroho Aditya Rafly Syahdana Agung Setia Budi Ahmad Fahmi AdamSyah Ahmad Rizqi Pratama Alfatehan Arsya Baharin Alfatehan Arsya Baharin Alfaviega Septian Pravangasta Ali Ilham Ainur Rahman Allif Maulana Althaf Banafsaj Yudhistira Amelio Eric Fransisco Amri Yahya Ananda Ribelta Anata Tumonglo Andre Ananda Pratama Anggi Fajar Andana Aras Nizamul Aryo Anwar Ariq Monetra Aufa Nizar Faiz Axel Elcana Duncan Bagas Nur Rahman Bambang Gunawan Tanjung Barlian Henryanu Prasetio Barlian Henryranu Prasetio Boris Wiyan Pradana Bramantyo Ardi Cahyanita Qolby Rahmarta Rizaputri Chandra Gusti Nanda Putra Chikam Muhammad Dadang Kurniawan Dahnial Syauqy Dian Bagus Setyo Budi Didik Wahyu Saputra Dien Nurul Fahmi Dipatya Sakasana Dony Satrio Wibowo Dwi Firmansyah Dwi Fitriani Dwiki Nuridhuha Eko Setiawan Ezra Maherian Fachrur Febriansyah Manangkalangi Fajar Miftakhul Ula Falachudin Akbar Farah Amira Mumtaz Farid Aziz Shafari Fauzan Rivaldi Fauzi Awal Ramadhan Fikri Fauzan Fikriza Ilham Prasetyo Fitrahadi Surya Dharma Fitriyah, Hurriyatul Galang Eiga Prambudi Gembong Edhi Setiawan Gembong Edhi Setyawan Govinda Dwi Kurnia Sandi Gusti Arief Gilang Habib Muhammad Al-Jabbar Habib Zainal Sarif Hafid Ilmanu Romadhoni Hafiz Nul Hakim Hafizhuddin Zul Fahmi Hamdan Zuhdi Dewanul Arifin Handoko Ramadhan Hani Firdhausyah Hanif Yudha Prayoga Hanifa Nur Halimah Hendriawan Dwi Saputro Hurriyatul Fitriyah Ichwanul Muchlis Ihsanurrahim Ihsanurrahim Imam Syafi'i Al Ghozaly Iqbal Koza Irham Manthiqo Noor Issa Arwani Istiqlal Farozi Izza Febria Nurhayati Jodie Putra Kahir Kezia Amelia Putri Kiki M. Rizki Lamidi Lamidi Leina Alimi Zain Lia Safitri M. Ali Fauzi M. Sandy Anshori M. Sifa'un Ni'am Mahesha Bayu Paksi Mario Kitsda M Rumlawang Marrisaeka Mawarni Mhd. Idham Khalif Misran Misran Moch Zamroni Mochamad Hannats Hanafi Ichsan Mochammad Hannats Hanafi Ichsan Mochammad Hannats Hanafi Ichsan Mohamad Abyan Naufal Fachly Mohamad Muhlason Nur Aziz Mohammad Ali Muhsin Muhajir Ikhsanushabri Muhamad Ichwan Sudibyo Muhamad Irfanul Hadi Muhamad Taufiq Firmansyah Muhammad Bilal Muhammad Eko Lutfianto Muhammad Fatikh Hidayat Muhammad Jibriel Bachtiar Muhammad Kholis Fikri Muhammad Prabu Mutawakkil Muhammad Raihan Al Hakim Muhammad Rheza Caesardi Muhammad Yaqub Muhammad Yusuf Hidayat Nadi Rahmat Endrawan Nobel Edgar Nugraha Pangestu Octavian Metta Wisnu Wardhana Octavian Metta Wisnu Wardhana Oktaviany Setyowati Pabela Purwa Wiyoga Pinandhita Yudhaprakosa Priyo Prasetyo Putri Laras Rinjani Rachmat Eko Prasetyo Rahadian Sayogo Rahmat Yusuf Afandi Rakhmadhany Primananda Randy Cahya Wihandika Refsi Ilham Cahya Renita Leluxy Sofiana Ricky Zefani Aria Zurendra Ridzhal Hachim Wahyunanto Rifqi Alvaro Rifqi Anshari Riko Andianto Rimas Oktama Rint Zata Amani Rioadam Sayyid Abidin Riski Kurniawan Rizki Septiansyah Rizky Widya Mahendra Romario Siregar Rosyana Lencie Mampioper Sabitha Wildani Hadi Sabriansyah Rizqika Akbar Salsabiil Hasanah Satyaki Kusumayudha Shafa Sabilla Zuain Sulthan Ghiffari Awdihansyah Sutrisno Sutrisno Syahriel Diovanni Yolanda Tatit Kisyaprakasa Tedy Kurniawan Tezza Rangga Putra Tibyani Tibyani Tio Haryanto Adi Putra Tri Putra Anggara Upik Jamil Shobrina Utaminingrum, Fitri Vatikan Aulia Makkah Widasari, Edita Rosana Wijaya Kurniawan Willy Andika Putra Yanuar Enfika Rafani Yohana Angelina Sitorus Yohana Kristinawati Yurliansyah Hirma Fajar