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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Edita Rosana Widasari
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Metode K-Nearest Neighbor untuk Sistem Deteksi Covid-19 berdasarkan Suhu Tubuh dan Kadar Oksigen Graciella Fiona Br. Panjaitan; Edita Rosana Widasari; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Covid-19 disease is a contagious disease, so it is necessary to avoid direct contact between humans to minimize exposure to this virus. Examination to the hospital can allow people to be exposed to the Covid-19 virus because direct contact with some people is still carried out in an invasive way. So research is needed to detect the symptoms of Covid-19 non-invasively and does not require a lot of money and time. In this study the detection of body temperature used the MLX90614 sensor by facing the hand towards the front of the sensor so that the body temperature value was obtained. To detect oxygen levels using the MAX30100 sensor by placing your index finger on the sensor then waiting until the oxygen level value is obtained. The two values ​​from the sensor readings will be classified using the K-NN method. The output will be displayed on the LCD in the form of sensor measuring value text and classification results. The test results in this study obtained the accuracy of the sensors used. For measuring body temperature using the MLX90614 sensor, an accuracy of 99.56% was obtained, then for measuring oxygen levels using the MAX30100 sensor, an accuracy of 98.77% was obtained. In the classification test, it is determined by three distances k, namely k=3, k=5, and k=7, where k=3 gets an accuracy of 100%, k=5 gets an accuracy of 90%, and k=7 gets an accuracy of 80%. and from this classification, the average computation time is 2.38 ms.
Sistem Deteksi Hipoksia menggunakan Metode Decision Tree berdasarkan Detak Jantung dan Kadar Oksigen Elisabeth Agustina; Edita Rosana Widasari; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypoxia is a condition where there is a continuous lack of oxygen either in the short or long term according to a period. If hypoxia occurs in the short term, what is caused is acute hypoxia. The symptoms found in hypoxia are very fast heartbeat (tachycardia), rapid breathing, dizziness, weakness. Based on the existing problems, the authors want to conduct research to detect hypoxia. The tool designed for this study can detect hypoxia based on heart rate and oxygen saturation. This detection is very easy to use, just by placing your index finger on the MAX30100 infrared sensor, after that you get the measurement results which will be displayed on the LCD screen. The results of these measurements will be the input data for classification. This classification uses the Decision Tree method where this method is very accurate and fast in carrying out the classification process. Classification results will be displayed on the LCD screen. Testing of this tool was carried out 10 trials in detecting heart rate and oxygen saturation. The accuracy obtained when doing the classification is 100%. The MAX30100 sensor when measuring heart rate obtains an accuracy of 97.66% and an error rate of around 2.34. Then, the accuracy obtained when detecting oxygen levels is 98.75% with an error rate of 1.25%
Sistem Deteksi Daun Busuk pada Pakcoy Hidroponik menggunakan Metode Thresholding pada Warna Hue dan Saturasi berbasis Raspberry Pi Rifqi Imam Ramadhan; Hurriyatul Fitriyah; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Agriculture has an important role in Indonesia's economic development as one of the contributors to the state budget which continues to increase every year. In the post-covid 19 pandemic, there were problems that disrupted the economy, impacted on other fields such as agriculture, where the problem of the food crisis would become a problem for Indonesia if it was not handled properly. Agricultural land tends to be increasingly limited because they have to compete for various uses, while people working in agriculture in absolute terms continue to increase causing land ownership to become increasingly narrow. An effective pakcoy planting solution can be planted using hydroponic techniques, so there is a lot of interest from farmers to cultivate pakcoy plants but these plants are susceptible to disease. This research was conducted to detect disease in Pakcoy. The process of detecting Pakcoy disease focuses on knowing the disease of Rotten Pakcoy leaves (Phytoptora sp.) based on the Color Space Hue Saturation Value or HSV. Implementing a simple image using image processing taken using a webcam camera then processed on the Raspberry Pi 4 Model B for detection of pakcoy disease then displayed on the LCD16x2. Based on the research implementation process from start to finish it is able to work as expected. The accuracy of the pakcoy disease detection system resulted in an average accuracy value of 85% for 2 types of classes and an average computation time of 0.001213 seconds for 10 tests.
Klasifikasi Rumah Sehat dengan Metode Jaringan Syaraf Tiruan berbasis ESP32-S Noveriko Noveriko; Dahnial Syauqy; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A home is a place to return to, rest, and take shelter. Most people spend a large portion of their time at home. The amount of time we spend in our homes makes it important for us to pay attention to the health conditions of our home. A home that is not well-maintained can become a breeding ground for diseases. The solution that I propose is to create a system that can determine a home's health score and also measure the criteria in each room. The system that I have created uses a Artificial Neural Network algorithm and an ESP-32 microcontroller as the processor unit. The system will take in features captured by each sensor, including light intensity (BH1750), air temperature (DHT11), air humidity (DHT11), and carbon monoxide levels in the air (MQ-7). The results of the algorithm and measurements will be displayed on a 20x4 LCD screen, showing the measured features, the obtained score, and the confidence of the classification. The training data used in this system consists of a total of 345 data from 4 classes, with each class consisting of approximately 80 data. The testing was carried out in rooms in a volunteer's home, with a total of 20 rooms. The results of the testing show that 18 were correct and 2 were incorrect, resulting in a system accuracy of 90% with an average computation time of 0.148 seconds.
Sistem Deteksi Kematangan Cabai Hidroponik menggunakan Metode Thresholding pada Warna Hue, Saturation, dan Value Cut Fahrani Dhania; Hurriyatul Fitriyah; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the agricultural industry, the main sector in the economic world, digital image processing is needed which aims to increase work productivity and time efficiency to choose the quality of crops to be marketed to the public due to lack of autonomy which results in high production costs and high work costs. Chili is the plant included Solanceae genus that can be implanted in a hydroponic planting system that makes water a substitute for soil media. This hydroponic chili plant uses the Dutch Bucket planting system because it can be used in narrow environments and can be used flexibly. This detection is assisted by a simulator robot that has been designed with a certain height and distance, namely a height of 20 cm and a distance of 30 cm and 20 cm with the help of a Lux Meter to measure the intensity of ambient light which is then designed using Qt Designer as a Graphical User Interface platform which will display the results of hydroponic chili detection through the bounding box. The accuracy value on the successful detection of hydroponic chili maturity resulted in a percentage of 93% and a computational mean value of 3.841 seconds.
Implementasi Robot Manipulator menggunakan Sinyal Electromyography berdasarkan Pergerakan Kaki Manusia Andre Adikusuma; Edita Rosana Widasari; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Technological developments in the medical field have developed rapidly, one example is the manipulator robot. For example, in some cases there are people suffering from stroke who have difficulty moving their limbs. Due to the limited number of rehabilitators, the rehabilitation process took longer and they had to wait in line to carry out rehabilitation. The movement of the robotic manipulator's leg will be based on the movement of the human leg by reading electromyography (EMG) signals generated from the leg muscles through the electrodes. Then an exponential filter is used to reduce the noise from the EMG signal. After filtering the signal obtained will be classified using a decision tree classification. Then the AX-12A servo is used to move the legs of the manipulator robot which has 4 degrees of freedom. The results of the decision tree classification have amplitude values for each movement, which include 0V to 1.995V as a squatting movement with an angle of 450; 1.995V to 2.985V as a 90° seated motion; 2.985V to 5V as a standing movement with an angle of 1800. The test results obtained determine the movement of the leg robot has an accuracy of 70.667% for the total average accuracy. The subjects used were 10 with 5 movements at each angle.
Sistem Deteksi Insomnia berbasis Elektrokardiogram menggunakan Fitur Mean RR dan Standar Deviasi NN dengan Metode K-Nearest Neighbours M. Yunior Dwi Ashari; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Insomnia is a sleeping sickness in which the time and quality of human sleep is not sufficient due to difficulty getting to sleep or difficulty maintaining sleep. Early diagnosis and treatment of insomnia is necessary to prevent chronicity and death resulting from untreated insomnia. The body's required ECG signal can be detected by installing a sensor on the human body. The ECG signal has several points, namely P, Q, R, S and T points. There are PR intervals, PR segments, QRS complex, ST segments, and QT intervals as areas in the ECG signal. In this study, we will use the R-R interval of human ECG signals taken when the sleep condition is for 2 hours which already represents one person's sleep cycle. The features to be used are the Mean RR and SDNN features. The K-Nearest Neighbours method classifies new data by finding the shortest distance from the training data, this makes this method suitable for this study because the training data used has significant differences between one class and another class. The tools that will be used to detect insomnia are the Arduino Uno microcontroller and the AD8232 module which are used to detect and filter the detected signals. AD8232 is a module used to acquire ECG signals. The use of K-Nearest Neighbours as a classification method has an accuracy of 86% for K = 3 and obtain an average computation time of 79.9 ms.
Sistem Pendeteksi Kantuk Pengemudi berbasis Eye Aspect Ratio dan Mouth Opening Ratio menggunakan Algoritme C-LSTM Auliya Firdaus; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

There were 103,645 traffic accidents in Indonesia in 2021, an increase of 3.6% from the previous year. The second leading cause of accidents was freight transport, at a percentage of 12%. According to the National Committee for Transportation Safety of the Republic of Indonesia (KNKT), 80% of accidents were caused by driver fatigue, which resulted in microsleep. To address this problem, a system for early detection of driver fatigue is needed. This system uses the eye aspect ratio (EAR) and mouth opening ratio (MOR) as the main parameters for detecting microsleep and yawning as a sign of fatigue. With an adaptive threshold, the accuracy of the system in detecting microsleep is 97%. The system's detection of yawning uses a Convolutional Neural Network (C-LSTM) model. The C-LSTM model was chosen because it is a combination of CNN for better feature recognition and LSTM for sequential learning. The accuracy of the yawn detection system is 98%. It can be concluded that this system works well in detecting driver fatigue.
Sistem Pendeteksi Premature Ventricular Contraction (PVC) berdasarkan Lebar QRS dan Gradien R menggunakan Metode FK-NN Desy Marinda Oktavia Sitinjak; Edita Rosana Widasari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Heart is a vital organ and is the last line of defense for life and is a part of human body that has a role as a center for circulating blood. The heart's job is to pump blood to all body parts, where there is a rhythmic pattern of heartbeats when the heart pumps blood to all parts of the body. In a normal adult heart, it has a heartbeat ranging from 60-100 beats per minute. Adults who have a heart rate of fewer than 60 beats or more than 100 beats per minute mean there is a disturbance of their heartbeat or arrhythmia. One of example of an arrhythmia is Premature Ventricular Contraction (PVC). PVC conditions are common in humans, but if occur continuously it can increase the risk of heart disease. PVC can be prevented by early detection of heart disease, where an examination will be carried out using an ECG machine. However, the costs required to carry out an examination using an ECG machine are quite expensive. Regular early measurements are needed PVC using QRS Complex and R Gradient. The results of the AD8232 ECG sensor acquisition test get an error value of 7.14% with 5 tests. The accuracy results using the Fuzzy K-Nearest Neighbor (FK-NN) classification get 90% of the 20 test data used. For system computation time, it managed to reach 286.06 milliseconds.
Sistem Deteksi Stres berdasarkan Detak Jantung dan Kelenjar Keringat menggunakan Metode K-Nearest Neighbours Mohammad Hafidh Wildan Maulana; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stress is a state of pressure on a person due to something that happens not as expected or unwanted. Stress can risk disrupting a person's health, both physical aspect and mental aspect, and reduce their quality of life as well if not treated immediately. A person's heart rate and GSR (Galvanic Skin Response) can be used as features to detect stress. Both features are influenced by sympathetic nerves that regulate a person's response to emotions so that they can be used to identify a person's stress level. The K-Nearest Neighbor (k-NN) method is used as a classification method because it has advantages in accuracy using data with few parameters and a large amount of data. The tool uses Arduino Uno as a microcontroller, MAX30102 sensor to measure heart rate, Grove GSR sensor to measure GSR, and LCD to display the output. Testing was carried out using 10 subjects as test data, and using 70 training data. The results of system testing in this study are based on the accuracy of the tool's readings compared to the results of the subject's questionnaire. The accuracy produced by this stress detection system is 70%. For the results of testing the k-NN method on this system has an accuracy of 64.29% for 70 training data. While the results of testing the computation time of the system get an average computation time of 13.3 ms.
Co-Authors Achmad Ghifari Adharul Muttaqin Adhitya Bhawiyuga Agung Setia Budi Ahmad Haris Wahyudi Ali Zhafran Daffa Andhika Nino Pratama Andre Adikusuma Andrika Wahyu Wicaksono Andy Hartono Anethasia Putri Prasetyowati Angger Dimas Bayu Sadewo Aulia Zhafran Auliya Firdaus Barlian Henryanu Prasetio Barlian Henryranu Prasetio Batara Dolly Nababan Cut Fahrani Dhania Dahnial Syauqy Denis Reza Ramdani Desy Marinda Oktavia Sitinjak Dody Kristian Manalu Dwinanda Romolo Dzakwan Daffa Ramdhana Eko Setiawan Elisabeth Agustina Fathul Abdillah Khosin Fatkhurrozi Fatkhurrozi Fitriyah, Hurriyatul Graciella Fiona Br. Panjaitan Hafid Ilmanu Romadhoni Harry Mulya Henry Trenggana Hery Julianto Situmorang Izzati Firsta Wijayanti M. Yunior Dwi Ashari Moch Rizki Cahyadi Mochammad Hannats Hanafi Ichsan Mohammad Fauzin Amin Mohammad Hafidh Wildan Maulana Muhamad Fauzan Alfiandi Muhammad Ammar Hassan Muhammad Fadhil Sadeli Muhammad Nur Arssy Muhammad Risyat Nashrullah Muhlis Agung Saputro Noveriko Noveriko Nur Syifa Syafaat Onky Soerya Nugroho Utomo Putri Laras Rinjani Qonita Luthfiyani Rakhmadhany Primananda Rejeki Puspa Dinasty Reza Hastuti Rifki Pinto Hidayat Rifqi Imam Ramadhan Rizal Maulana Rosa Mulyanis Chan Sabriansyah Rizqika Akbar Salsabila Salsabila Sasmita Eko Raharjo Selina Kusmiawati Syifa' Hukma Shabiyya Thomas Oddy Chrisdwianto Tri Oktavia Mayasari Utaminingrum, Fitri Zhafran Wadiansyah Zhuliand Rachman Zonnete Bryllian Dheo