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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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Implementasi Prototype Kapal sebagai Sistem Monitoring Kualitas Air menggunakan Algoritme Naive Bayes Axel Elcana Duncan; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Living things really need water as a source of life. Lots of people use water in the waters for various things. Water quality standards using three characteristics, physics, chemistry, and biology are prerequisites for water quality. Through its very important role for all living things, it is very much needed an embedded system that is equipped with an algorithm for monitoring water quality, given the complexity, and conventionality of existing methods for measuring water quality. A ship prototype system using Arduino Nano, WEMOS D1 Mini, Genuine Analog pH Meter sensor, DS18B20 sensor, Turbidity sensor, JSN-SR04T sensor, L298N motor driver, and 6V DC motor can help from existing problems. This system is in the form of a ship so that it can carry out comprehensive monitoring. This system also uses the Naive Bayes algorithm in classifying through features such as pH, temperature, turbidity, and depth as inputs. Good, medium, and bad results will be the class will be output. In getting these results, this system uses as many as 40 training data and 20 test data. The average error reading of the Genuine Analog pH Meter sensor from 10 tests is 8,233%. The average error of DS18B20 sensor readings from 10 tests is 0.859%. Turbidity sensors have a linear graph, the more turbid the water the smaller the voltage value. The average error of sensor reading JSN-SR04T from 10 times the test is 2.492%. The accuracy of sending data from 10 tests is 100%. The accuracy of the classification using the Naive Bayes algorithm is 90%. The average computational time performed by the system from 10 tests is 3.7055 ms. The accuracy rate on the drive system from 3 times of testing is 100%.
Klasifikasi Arah Gerak Diagonal Mata dan Normalisasi Sinyal Electrooculography dengan Metode Diferensiasi Muhajir Ikhsanushabri; Rizal Maulana; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Communication is very important for people like Amyotrophic Lateral Sclerosis (ALS) patients who are not able to speak and write, but not with eye movements. At present, there are already several techniques that can read eye movements using the method based on video that has a high price that using signal calculations with input of images called image processing. This signal requires a camera that supports images with a high degree of accuracy and consideration factors that cause noises. One of these factors is lighting so that this method will require a very large cost. Therefore, an alternative is needed that can make eye movements using bio-signals in the body that issupported by movements using Electrooculography (EOG) that supports the direction of eye movements. This research discusses the signal normalization use Differentiation Method. The system can classify of eye movement 8 directions sen ton 8 LEDs. Based on the results of accuracy for eye movement direction is about 100% and based on the results of average accuracy the whole system by taking 5 samples of people is about 79.25%.
Sistem Pendeteksi Penyakit Diabetes Melitus berdasarkan Kondisi Urin dan Gas Buang Pernapasan menggunakan K-Nearest Neighbor berbasis Arduino Farah Amira Mumtaz; Rizal Maulana; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 8 (2020): Agustus 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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One of the most dangerous chronic diseases in Indonesia is diabetes mellitus. This disease has symptoms characterized by high levels of glucose in human blood. Examination of diabetes mellitus that currently available is still invasive by taking blood samples using a needle into the patient's finger. The long period of time and the high cost are the constraint in getting the results of the examination. Research to detect diabetes mellitus in a non-invasive manner, which requires a small amount of money, and saves more time, is needed to overcome the existing problems. This research uses features in the form of urine and respiratory gas. In the patient's urine, the color and the level of ammonia gas will be detected. Meanwhile, in the patient's respiratory gas levels, methane gas will be detected. Data processing in the system using the Arduino Mega microcontroller. The processed data obtained from the output of the color sensor and gas sensor. The sensors are TCS3200 as a color sensor and the MQ-135 and MQ-4 sensors as gas sensors. The results of data processing will be classified using the K-Nearest Neighbor or K-NN method into Normal and Diabetes conditions. Testing using 12 test data and 24 training data with a value of K = 3 resulted in an accuracy of 91.67% because there was 1 data mismatch at the time of testing. The average system performance obtained based on 10 tests is 3061.9 ms.
Implementasi Sistem Otomasi Takaran Biji Kopi Pada Tempat Penyimpanan di Mesin Penggiling Kopi Fachrur Febriansyah Manangkalangi; Rizal Maulana; Gembong Edhi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 9 (2020): September 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Entrepreneurs who have established small coffeeshop business for a long time, using coffee grinder machine which has no automatic scale tchnology inside, absolutely wish that kind of technology exist in their machines without replacing with the new ones. From that problem statement, the researcher will provide solution by creating coffee beans automatic measuring system in the storage of the grinder which there is not any automatic scale technology. This system will use load cell censor hardware with HX711 module as an object reader, in this case is coffee beans in the hopper. This system will also use stepper nema 17 motor whose end has already connected with artificial component in the form of auger and driver A4988 which is used as actuator to the coffee beans in the hopper. There is also 16x2 LCD with 12C module used as whole information display of the process, from the beginning until the end. It has three push buttons utilized by users to choose three mass measuring target of the system and it use servo motor as a open-close valve between the prototype tool and grinding machine. Then, the analysis result for this system testing can be concluded that there is 16,9% error to the digital scale tool, whereas for monitoring analysis, the whole information dislpayed on LCD monitor gain good system testing result in displaying all the system information. The next analysis is motor stepper actuator to the coffee beans capacity in the hopper and the result of motor stepper movement is 100%. It means that the movement towards the coffee beans capacity in the hopper is good. It moves very well. The conclusion of the final testing analysis to the entire system is the researcher gain 100% for accuracy of the entire system and it can be stated that it can work properly just as the purpose of this research.
Sistem Pendeteksi Dehidrasi berdasarkan Warna dan Kadar Amonia pada Urine, Detak Jantung dan Pernapasan berbasis Arduino dengan Metode K-Nearest Neighbor Muhammad Rheza Caesardi; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Untuk dipublikasikan di Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN 2020)
Perancangan Sistem Monitoring Tanda Vital Pada Tubuh Manusia Secara Real Time Dengan Tampilan Desktop Ariq Monetra; Mochammad Hannats Hanafi Ichsan; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Vital Sign monitoring design is a medical technology system that will be designed to measure 4 Vital Signs in the human body, namely body temperature, heart rate, blood pressure, and oxygen saturation level in the blood, then send the data obtained using the LoRa module and make classification decisions using J48 Decision Tree. This Vital Sign monitoring design is designed because in general medical services still use traditional methods such as measuring blood pressure manually and then writing it on paper. The focus of this research is to get the value of 4 Vital Signs, send data, and make decisions. At the time of testing the system performance will be programmed with several menu selections between numbers 1, 2, 4, and 5 which have their respective functions. In sending the data, it will be tested at a certain distance using 2 Nodes, namely 1 Transmitter Node as the sender of the measurement results, and 1 Receiver Node to receive the measurement results.
Implementasi Sistem Pendeteksi Atrial Fibrillation Berbasis Arduino Uno Menggunakan Metode Support Vector Machine Renita Leluxy Sofiana; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Many cases of coronary heart disease that cause sudden death because the patient has a history of arrhythmias. In addition, 1 in 6 stroke patients are also caused by arrhythmias. According to cases that often occur in hospitals, Atrial Fibrillation arrhythmias are one of the factors that cause stroke because patients with Atrial Fibrillation arrhythmias have a five times greater risk of stroke because it can cause blockage of blood vessels. Prevention for this disease can be done by conducting early examinations so treatment can be done quickly. Currently, atrial fibrillation arrhythmia examination can only be done in a hospital, which is quite expensive and this examination cannot be done independently. This study uses the AD8232 sensor to generate ECG signals, the Arduino Uno as a data processor, and the LCD to display the results of the diagnosis, "Normal" or "Atrial Fibrillation". The system will use the BPM feature, mean RR interval, and median RR interval to perform Support Vector Machine classification. Starting with 24 training data to produce a hyperplane. Furthermore, data testing is carried out to classify. The results obtained from the BPM accuracy test were 12 times the result of 95.42%. The classification using Support Vector Machine method resulted an accuracy, training time, and testing consecutively 83.33%, 219.30 ms, and 0.09 ms using 12 test data.
Deteksi Hipoksia Berdasarkan Detak Jantung, Saturasi Oksigen, Volume Dan Irama Pernafasan Menggunakan Metode K-Nearest Neighbor Leina Alimi Zain; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 1 (2021): Januari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Symptoms of hypoxia are a condition caused by a lack of oxygen in the cells and tissues of the body and this condition can cause damage to the nerves of the brain, liver and other organs which will lead to death. The use of technology in the medical field has created a system to detect hypoxic symptoms using the K-Nearest Neighbor method. The detection system using the K-Nearest Neighbor method can be carried out in knowing the condition of a person's body without injuring the body or it is called non-invasive. Retrieval of heart rate and oxygen saturation data using the MAX30100 sensor by placing the index finger on the red LED component and the IR photodiode component. It takes 20 seconds and the finger must not move during the take to get the optimal value. In taking the volume and rhythm of breathing is done using a Flex sensor. The hardware used is the Arduino Mega, the MAX30100 sensor and the Flex sensor. The level of accuracy on 10 tests on the MAX30100 sensor is 97.07% and the accuracy level obtained on the Flex sensor is 92.77%. In classifying using the K-Nearest Neighbor method, there is a level of accuracy at the k = 3 value of 90% k = 5 by 80% and k = 7 by 70% and there is a computational average of 3.37 ms in 10 tests.
Implementasi Sistem Pendeteksi Premature Ventricular Contraction (PVC) Aritmia menggunakan Metode SVM Ahmad Rizqi Pratama; Rizal Maulana; 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

Premature Ventricular Contraction (PVC) Arrhythmia is a condition in which the heart beats irregularly. The PVC condition causes the heart to beat too fast or be too slow than it should be. PVC Arrhythmias appear in the lower part of the heart or commonly called the ventrical (chambers) of the heart. Patients with PVC Arrhythmia have a risk of experiencing heart failure, coronary heart disease, and other heart diseases if the PVC Arrhythmia condition occurs continuously. Currently, PVC Arrhythmia can only be detected in the hospital at a cost that is quite expensive. Therefore, research in detecting PVC Arrhythmias is needed in order to solve the cost problem required. There are several parameters that can be used in detecting PVC Arrhythmias, in this study the QRS Interval, QT Interval, and ST segment were selected as parameters. These parameters are in each cycle of the ECG signal which will be read using the AD8232 sensor. Reading the ECG signal requires 3 electrodes attached to the user's body. The placement of the electrodes is on the chest by attaching 2 electrodes and on the stomach by attaching 1 electrode. The ECG signal obtained by the AD8232 sensor will then be processed on the Arduino Uno to reduce the noise obtained and classified by the Support Vector Machine (SVM) method. The use of training data as much as 46 heart data with 23 types of PVC data and 23 types of normal data will be embedded in the SVM method. SVM classification testing was carried out with 20 cardiac data. Classification test results are in the form of class "Normal" or "PVC". Every 3 SVM classification results will be used to determine the type of heart condition. Types of heart conditions include "Normal", "Bigeminy", and "Trigeminy" which will later be shown on the LCD so that users know the condition of their heart. In testing the SVM accuracy level, it was obtained a value of 85% with 1.79 seconds of average training time and 2.49 seconds of average testing time.
Implementasi Sistem Pendeteksi Obstructive Sleep Apnea berdasarkan Parameter Interval QT dan Interval PR menggunakan Metode Naive Bayes Iqbal Koza; Rizal Maulana; Hurriyatul Fitriyah
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

Obstructive Sleep Apnea is a condition in which breathing stops momentarily during sleep and repeats several times. If this disorder is not treated further, it can cause complications in the form of lack of sleep, fatigue and eye problems. For now, sleep apnea examination can only be checked in a hospital and is expensive. Therefore, in this study a system for detecting obstructive sleep apnea was created which did not require too much money. The tools to be used are the Arduino Uno microcontroller as a place for the system program, the ECG AD8232 sensor to detect electrical activity in the heart which is attached to the chest using 3 electrodes, and a 16x2 LCD to display the final result. This study uses the Naive Bayes classification in classifying the electrical activity of the heart. The features in the classification of the naive Bayes method are the QT Interval and the PR Interval, the results of which will be displayed on the LCD in the form of "Normal" or "Sleep Apnea". There were 24 test data taken and 48 training data used in the Naive Bayes classification test. The results of the accuracy test using Naive Bayes were 87.5%. And the results of computational time testing were carried out 24 times with an average value of 1,044.2083 ms.
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