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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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Sistem Klasifikasi Diabetes Melitus Berdasarkan Kondisi Urin, Gas Buang Pernapasan, Dan Tekanan Darah Menggunakan Metode Naive Bayes Berbasis Arduino Dwi Fitriani; Rizal Maulana; Hurriyatul Fitriyah
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

Diabetes mellitus is a disease that attacks humans regardless of age. A person will have diabetes mellitus when blood sugar levels in the body increase, resulting in reduced insulin in the body. Diabetes examination can be done using a blood sugar test tool using a blood sample taken from a person's fingers. Examination in this way causes pain and discomfort. With this problem, this study aims to create a system that can be used to detect a person without using blood samples (non-invasive), saving time and costs in the examination. In the study of diabetes mellitus detection system using parameters of urine condition, respiratory exhaust gas and blood pressure. The conditions used are ammonia gas levels in human urine, respiratory exhaust gases used are acetone gas levels from human breath and human blood pressure. Data processing is carried out using arduino uno microcontroller. The data was obtained from the output sensors MQ-135, TGS-822 and MPX5700AP sensors. From the test results obtained the correlation value of sensors MQ-135 and TGS-822 with the output voltage of 99.29% and 98.56%, while for the sensor MPX5700AP known percentage of errors sistole and diastole by 8.90% and 4.64%. The system classifies diabetes mellitus using the Naive Bayes method. It uses 12 test data and 24 training data to determine the accuracy of Naive Bayes classification. Of the 12 test data there is 1 data whose class is not appropriate so that the accuracy value becomes 91.67%. Meanwhile, the average compute time of the system obtained in 10 tests is 1.02 seconds.
Implementasi Sistem Pendeteksi Fibrilasi Atrium berdasarkan Interval dan Gradien QRS menggunakan Metode Jaringan Saraf Tiruan Muhammad Bilal; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Atrial fibrillation is a type of arrhythmia that occurs due to electrical breakdown from the atria so that blood cannot be pumped to the ventricles at the right time. This condition can lead to other complications, such as stroke, palpitations, cardiomyopathy and heart failure. The detection can be done using an electrocardiograph (ECG), holter monitor or electrophysiology, but this action takes a lot of time and money. Based on this, research was carried out to detect Atrial Fibrillation as soon as possible by building a system consisting of sensor AD8232, Arduino Uno and 16x2 LCD. The ECG signal will be acquired by the AD8232 sensor and processed by Arduino Uno to obtain feature values. The features used are the average and the median value of the QRS gradient and interval. The Artificial Neural Network (ANN) method is used as a classifier of these features with 2 conditions, “Normal” and “FA”. A total of 60 datasets were used, 40 of which were used as training data in the ANN training phase with backpropagation algorithm and 20 were used as testing data. From the BPM test carried out by comparing the sensor acquisition value and the manual calculation value, it was found that the accuracy of the sensor in acquiring the ECG signal was 94,55%. Then from the 20 tested data, the classification accuracy of the ANN method is 90% with an average calculation time of 32,09 ms.
Rancang Bangun Sistem Pendeteksi Ventricular Takikardia (VT) Aritmia menggunakan Metode K-NN dengan Fitur Area Under QRS dan Interval RR Dien Nurul Fahmi; Rizal Maulana; Mochamad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The heart is a vital organ that pumps blood throughout the body. A healthy heart beats neither too fast nor too slow, normally 60-100 times per minute. A heart that beats too fast up to 120-300 times a minute is called tachycardia. Ventricular tachycardia (VT) is a tachycardia that occurs in the ventricles if it lasts for a long time, can lead to congestive heart failure and death. Deaths caused by heart and blood vessels are in the highest rank in the category of non-communicable diseases, based on The Institute for Health Metrics and Evaluation (IHME). To prevent an increase in deaths from heart disease, the public is expected to be vigilant by checking their heart health from an early age. Cardiac examinations can only be done at health care service, therefore the aim of this study is to build a system that can detect VT that can be done alone anywhere. To build the system, the AD8232 module is used as a signal conditioner and Arduino Uno as a microcontroller. VT heart defects can be seen from the shape of the QRS signal and the rate of the heartbeat. The parameters to be used are the RR interval and the area under QRS. The classification method uses k-NN because this method is suitable if only a few parameters and data are used. From the research results obtained 90,47% accuracy for k = 3 and k = 5, 95,23% for k = 7.
Implementasi Sistem Pendeteksi Myocardial Ischemia menggunakan Metode Support Vector Machine (SVM) Ezra Maherian; Rizal Maulana; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Myocardial Ischemia is a condition occurs when the blood flow to the heart is reduced, making the heart muscle lack of oxygen supply. The reduced blood flow occurs due to blockage inside the coronary arteries which could be the build-up of cholesterol or the clotting of the blood. The blockage could build-up even more from time to time, blocking the blood flow entirely and making the person with the condition more prone to a heart attack. Commonly, diagnosing Myocardial Ischemia is done by medical professionals at the hospital. AD8232 sensor kit and Arduino Uno Microcontroller are used to detect Myocardial Ischemia. The detection of heart condition is based on the slope of ST-segment and the peak of T wave that will be classified by Support Vector Machine classifier into either Myocardial Ischemia or Normal class. As many as 40 data were used to train the system and as many as 20 data were used to test the system. Sensor accuracy test shows sensor's accuracy of 95.56%. Test of SVM computation time gives a result of 3912.30 ms for average training time and 0.061 ms for average testing time. The accuracy of SVM classification tested on 20 data gives an accuracy rate of 85%.
Deteksi Penyakit pada Daun Cabai berdasarkan Fitur HSV dan GLCM menggunakan Algoritma C4.5 berbasis Raspberry Pi Shafa Sabilla Zuain; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Chili plants are plants with great economic potential in Indonesia. Nevertheless, every year chili production has decreased, one of which is due to disease. Observation of conditions in chili plants can be seen in the changes that occur in chili leaves. Disease detection in chili leaves is needed to minimize the risk of crop failure in chili plants and as a strategic control effort. The number of types of diseases in chili plants is quite a lot and knowledge about the symptoms of the disease is not enough to make it quite difficult for farmers to determine the type of disease that attacks. Therefore, a system that is able to detect diseases in chili leaves is needed. Disease Detection System on Chili Leaves Based on HSV and GLCM Features Using the C4.5 Algorithm Based on Raspberry Pi is used to detect types of diseases on chili leaves. This research uses Hue, Saturation and Value (HSV) color features and Gray Level Co-occurence Matrices (GLCM) texture features. The HSV color feature was used to analyze diseased leaf discoloration. Texture features are used to analyze changes in the texture of chili leaves with the help of five features from GLCM, namely correlation, dissimilarity, homogeneity, contrast, and energy with four variations of angles, namely angles 0, 45, 90 and 135. The classification method used is using a decision tree from C4.5 algorithm with classification results in the form of sercospore spot disease, curly mosaic and normal conditions. Detection of disease in chili leaves using this method using 21 test data to get an accuracy of 86%. The average execution time required by the system to detect is 1.045 seconds.
Implementasi Deteksi Dini dan Klasifikasi Jenis Urine dengan Metode K-Nearest Neighbor (KNN) pada Pasien Operasi Althaf Banafsaj Yudhistira; Rizal Maulana; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Urine produced by each person may vary based on its physiological. It was happened by some reason like daily diet, gender, condition of exrectory system, and so on. These factor may lead physiological change of urine like color and turibidity. That is why urin often used to determine a person's health condition. In other side doing traditional urine analysis error often occurs because it only rely on analyzer sight. Analysis of a person's condition through urine physical conditions is also very much needed in the operation process and it is not possible if periodic analysis is carried out continuously during the operation. Therefore we need a tool that can perform automatic analysis to minimize errors in analysis and taking patient handling actions. This study uses the TCS3200 sensor to extract features in the form of color and an IR Proximity sensor for urine fluid turbidity. The two features will be processed by Arduino Uno to carry out the classification process. The urine will be divided into three classes, namely: Normal Urine, Blood Urine, Pus Urine. The classification process will use the K-Nearest Neighbor method with varying K values ​​starting from K = 3, K = 5, and K = 7. The system was able to recognize urine with an accuracy of 86.7% then 86.7% and 80% respectively
Deteksi Orang Bermasker untuk Akses Pintu berdasarkan Rasio Bounding Box Wajah dan Roundness menggunakan Naive Bayes berbasis Raspberry Pi Aditya Rafly Syahdana; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 is a disease caused by the corona virus and it has caused a global pandemic since December 2019. The use of masks is highly recommended to stop the spread of the corona virus, especially in closed rooms with many people inside, such as the classrooms and the office spaces. Detection of people with mask to access door based on the ratio of face Bounding Box and Roundness using Naive Bayes on Raspberry Pi is used to ensure people wearing masks who want to enter classrooms and office spaces. Webcam is used to capture images of people who want to enter. The image is processed and classified on the Raspberry Pi 4. Image processing begins with converting RGB to YCbCr and performing the morphological dilation, the morphological opening, and the morphological closing of morphology. Image processing aims to segment the human faces and remove their backgrounds. Human facial features were extracted using the ratio of Bounding Box and Roundness analysis which aims to determine the detected human face. The method for classifying faces is the Naive Bayes method. The solenoid lock opens when the classification result uses a mask, and it will be locked when the classification result does not use a mask. In the process of testing the Naive Bayes model using 60 data, the highest accuracy is 90%. To prove the accuracy of the Naive Bayes model, a test was carried out by inputting images directly into the system at 5 different distances, namely at 0.5 meters, 1 meter, 1.5 meters, 2 meters, and 2.5 meters. The test at each distance got an average accuracy of 86.6%. The average execution time required for system to detect masker is 8.82412 milliseconds.
Implementasi Sistem Kontrol Suhu dan Kelembaban Gudang Penyimpanan Biji Kopi menggunakan Arduino Uno dan Protokol MQTT Rahadian Sayogo; Mochammad Hannats Hanafi Ichsan; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In 2010 - 2014 the agricultural sector occupied the first position in terms of non-oil and gas exports in Indonesia, it explained that the Indonesian agricultural sector greatly supports the country's economy. One of the results of the agricultural sector is coffee plants that have been recognized by the global market as evidenced by occupying the fourth position in international coffee exports. The number and quality of Indonesian coffee caused Indonesia's coffee exports to rank fourth after Brazil, Vietnam and Thailand. Because 90% of coffee farmers in Indonesia who play a role are small-scale farmers who have agricultural areas between 1-2 hectares and coffee requires temperatures between 20 ℃ - 28 ℃ and humidity between 50% - 70% so that the flavor and quality of coffee produced is satisfying. That is why Indonesian coffee production is not optimal because it lags behind in terms of good coffee processing and technology from other countries. Therefore, to help small-scale coffee farmers in terms of increasing the quality of coffee production, a temperature and humidity control system for coffee bean storage is made using Arduino UNO and the MQTT protocol. The purpose of this system is to detect changes in the temperature and humidity of the coffee bean storage warehouse if the condition exceeds the system parameter point by automatically stabilizing it so that it does not damage the quality of the coffee.
Implementasi Sistem Pendeteksi Premature Atrial Contraction berbasis Mikrokontroler Arduino Uno berdasarkan Interval QTc dan Durasi Gelombang T menggunakan Metode Support Vector Machine Dipatya Sakasana; Rizal Maulana; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Untuk dipublikasikan ke Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Implementasi Quick Response Code dan Filter Unsharp dalam Deteksi Objek untuk Pemindahan Benda dengan Integrasi Database SQL menggunakan Robot Manipulator Refsi Ilham Cahya; Rizal Maulana; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The increase in the number of E-commerce retail users is currently growing very rapidly coupled with the effects of the covid-19 outbreak which has made the number of users increase. Therefore, an automation system is needed to help support the improvement of the E-commerce business that can perform tasks efficiently without the need to increase production costs. This system will use the camera to identify objects with images but this image will have noise that affects the identification process. The results of taking pictures of the object will be processed by the Raspberry Pi4 with an Unsharp filter to eliminate noise, then the process of identifying information data from the QR code on the object and determining the initial coordinates of the object will be carried out. The results of this data will later be forwarded to OpenCM to move the manipulator robot using the Inverse Kinematics function. In addition, this system will also send information data to the database for data storage. The use of the Unsharp Filter and QR Code methods in this system produces an accuracy value of 100% with a computation time of 801 ms in 40 times of object reading testing. As for the inverse method, this system has an error of 9.12% to reach the coordinates in 10 times of testing. And to move an object the system requires a computation time of 11 seconds for each type of object for scanning, moving, and storing information from the object.
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