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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 Pengendali Kualitas Air untuk Budidaya Ikan Guppy berdasarkan Suhu dan Derajat Keasaman Air menggunakan Metode KNN (K-Nearest Neighbor) Rizky Widya Mahendra; Eko Setiawan; Rizal Maulana
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

Indonesia, which is an archipelagic country, is home to various types of fish, from edible fish to ornamental fish used to beautify a room. One of the most popular aquarium fish today is the guppy fish, so it's understandable that there are more and more guppy fish fans. Of course, currently guppy fish farming is a promising field of activity for current cultivators because of its high selling value. However, keeping guppy fish is not easy, because the quality of the water used in the guppy fish habitat must be maintained, otherwise the water quality will cause the fish to get sick. This condition can be caused because there are some cultivators who do not carry out routine maintenance for guppies. The most common mistakes are incorrect selection of the aquarium location which causes significant changes in water temperature, as well as improper water management which causes changes in water pH that are too high. So we need an automatic system that can control the condition of the water. By using the KNN (KNearest Neighbor) method, the accuracy value of K is obtained, the K value using K=3, K=5, and K=11 gets an accuracy value of 95%, while the K value using K=7 and K=9 accuracy of 100%, after KNN gets the results of the detection, these results will be useful for regulating the action of the actuator. The use of the system affects the development of guppies, for aquariums that use the system the growth of fish is faster than the aquarium that does not use the system..
Sistem Pendeteksi Penyakit Sinusitis berdasarkan Kondisi Ingus dan Suhu Tubuh menggunakan Support Vector Machine (SVM) Bagas Nur Rahman; Rizal Maulana; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The nose is an important organ in life for humans. In the nose there is a disease that causes the sinus wall to experience inflammation commonly known as sinusitis. Smoking habits, environmental pollution and cold air become other factors that can affect the onset of sinusitis. If sinusitis is not immediately treated and treated properly, it can lead to complications which then lead to infection. So it is necessary to do an initial check to detect sinusitis using Magnetic Resonance Imaging (MRI). In addition, the cost is quite expensive and a long period of time is the basis of this research. Therefore, a tool is needed that can detect sinusitis early. This research will use the MLX90614 sensor for body temperature feature extraction and the TCS3200 sensor for color feature extraction. The two features then be processed by Arduino Uno to carry out the classification process into two classes, namely the Normal class and the Sinusitis class. The classification process in this study uses the Support Vector Machine method. The results of the accuracy of the SVM classification get 85% of the 20 data tested. For testing the computational time obtained an average value of 42 milliseconds from as many as 20 test data used.
Implementasi Wearable Device pada Monitoring Suhu Tubuh, Denyut Jantung dan Saturasi Oksigen dalam Darah menggunakan Low Power Mode Mahesha Bayu Paksi; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Health is measuring the human condition itself based on mental and physical conditions. Health conditions can be determined by measuring their vital signs, namely heart rate and body temperature. One of the most important organs for humans that will be fatal if exposed to disease is the lungs. The use of wearable devices to check these important organs will be easy. The use of wearable devices that are used in daily activities will not last long if they use battery power. For this reason, the system utilizes the low power method to save the power used. This sleep mode feature will be implemented in a system that can monitor the state of the human body through temperature, heart rate and blood oxygen levels. In the heart rate reading test, the percentage of error during normal conditions is 9.42% and during exercise is 5.99%. In the temperature reading test, the error percentage is 2.44%. Sleep mode on the system is able to reduce current by 22.29% from 34.36 mA to 26.7 mA.
Sistem Identifikasi Penyakit Gagal Ginjal melalui Bau Mulut, Warna Urine dan Tekanan Darah dengan Metode Support Vector Machine Lia Safitri; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The human body has a pair of kidneys that are shaped like the seed of a pea. Although it has a shape that is not so big, the kidneys have some crucial function in the human body. One of the functions of the kidneys is detoxification, which is removing metaboic waste that will become toxic if not removed from the body. One of the diseases that can attack the kidneys is kidney failure. Kidney failure is a condition when the kidneys cannot perform their functions and work properly. Kidney health should not be underestimated, then we need a system that can identify kidney conditions as early as possible to anticipate kidney damage in the human body. In this study, there are three parameters used to determine whether the kidneys are in normal condition or have kidney failure. First. To calculate the level of ammonia gas from bad breath with the MQ-135 sensor. Second, to calculated the RGB value of urine color using TCS3200 sensor and thridly, to measure blood pressure using the MPX5700AP sensor. These three parameters were choosen because they can beidentified using sensors. The data processing system in this system uses Arduino Uno. In addition, the selected classification method is support vector machine (SVM). After testing 10 times on 20 input training data. The level of accuracy of the test that have been done is 80% and the average computation time required is around 37.10 second.
Pengembangan Sistem Deteksi Diabetes Mellitus Tipe 2 berbasis Photoplethysmography menggunakan K-Nearest Neighbor Nobel Edgar; Rizal Maulana; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes Mellitus is an incurable disease related to the metabolism of insulin This disease has the potential to lead one to complication such as cardiovascular diseases or kidney diseases without early precaution. There are two type of Diabetes Mellitus, with type two considered to have higher potential to lead to danger due to the often mild or even non-existent symptoms until one reaches chronic point. Currently, the golden standard to detect Diabetes Mellitus is done through HbA1C tests, which calculates the average blood sugar level among the lifecycle of a red blood cell. Beside HbA1C test, home sugar level test system are also used to monitor blood sugar level during the testing time. Both approach requires the subject to harm themselves in order to obtain the data, hence making this an invasive approach. This research is done in order to develop an alternative system capable of detecting type two Diabetes Mellitus with non-invasive approach, using Photoplethysmography sensor GY-MAX30100, Arduino Nano v3, and K-Nearest Neighbor Algorithm. It is shown that using 10 data for testing, result shows an accuracy of 80% using K=9, Augmentation Index, and Ratio of Amplitude Systolic toward Pulse Interval features. It is also shown that system returns precision of 71%, sensitivity 100%, and F1 score of 83% for healthy class, while system returns precision of 100%, sensitivity 60%, and F1 score of 75% for type two Diabetes Mellitus class.
Implementasi Metode K-Nearest Neighbor pada Purwarupa Jemuran Otomatis berdasarkan Sensor Hujan dan Intensitas Cahaya Pabela Purwa Wiyoga; Rizal Maulana; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia, which has a tropical climate, gets sunshine all year round. It is very useful for people to dry household or industrial needs. Examples of industries that use sunlight are the production of crackers, salted fish, coffee, and unhulled rice and others. However, apart from getting the continuous heat of the sun, there is also rain that is present erratically. So leaving things outdoors will be risky, especially during the rainy season. Therefore, the researchers created a sun dry system that can be put outside automatically when the outdoor conditions are sunny and there is no rain, and inserted when the outdoor conditions are rainy or there is no sunlight. These conditions will be taken using a raindrop sensor and a light dependent resistor sensor. Arduino Nano as a microcontroller used to control sensors and actuators. To control the system using the K-Nearest Neighbors method. After testing, obtained good results. The best value of accuracy and computation time in using the K-Nearest Neighbors method is at k=3 with the average accuracy of 98% and the computation time is 37.173 microseconds. When compared with the if-else method, the if-else method has an accuracy value of 100% and a computation time of 13.232 microseconds. The K-Nearest Neighbor method is feasible to implement in this system, but a simple if-else method is more profitable due to better accuracy and simpler source code.
Sistem Monitoring Postur Tubuh Lansia berbasis Wearable Embedded System dan Metode Klasifikasi Naive Bayes Cahyanita Qolby Rahmarta Rizaputri; Dahnial Syauqy; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 5 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Posture is a movement made by humans, either standing, walking, face down, sitting or lyiing down. Movement of the body is also a process that requires complex integration of the limbs. Walkiing is the same as moving and both require balance control in the body. Posture will change with age and several factors are considered. The older you get, the more prone your body will be to injury. The Elderly Posture Monitoring System Based on the Wearable Embedded System and the Naive Bayes Classification Method was created to monitor the movement of an elderly person, usiing several determined axis points. The goal is to get a value from the angle of the body at the top and bottom. The installation of the point axis is at the top of the right and left calves and on the back as the main axis. This system uses the Naive Bayes classification method with 65 training data taken from 5 people and divided into 5 classes. In the test, it has obtained an error percentage of 10% to 20% and got an overall success with an accuracy percentage of 90% which was tested 10 times. The system will also send an Alert to the telegram bot if it occurs due to rapid changes from one class to another with a time frame that is limited to 10 seconds
Implementasi Robot Lengan Pemindah dan Penghitung Jumlah Barang menggunakan Metode Deteksi Objek Histogram of Oriented Gradient (HOG) dan K-Nearest Neighbor (K-NN) Adhly Hasbi Fadhlillah; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

All production sectors currently have warehouses that are used as storage of objects. The occurrence of errors in the process of moving objects from one location to another very often occurs which causes objects to get damaged and disrupt activities in the warehouse. To overcome errors that often occur, the creation of a robotic arm that is capable of moving objects is accompanied by the ability to know the number of objects that have been moved. Input for processing in the system will later be obtained from the inframerah sensor and camera. The inframerah sensor is used to detect the availability of an empty final place. The camera functions as a tool used to obtain digital images of objects. The image will be selected using HSV to get the color of the object, which will then be carried out feature extraction using the Histogram of Graident (HOG). The features obtained will be distinguished using K-Nearest Neighbor (K-NN) in order to obtain the final result in the form of the shape and color of the object. In the next step, the object will be moved by the hand robot to its final place by means of reverse kinematics. Inverse kinematics is a way to get the values ​​of each joint from the robot hand by changing the location coordinates. When the robot successfully moves the object, it will automatically add value to the LCD screen. The results of the tests that were run 10 times on the system as a whole got a value of 90% on a successful object movement, a perfect score on the calculation of items that had moved and a calculation time of 9811 microseconds.
Prediksi Bobot Segar pada Tanaman Hidroponik berdasarkan Kondisi Daun menggunakan Metode Pengolahan Citra Digital dan Jaringan Syaraf Tiruan Mohamad Abyan Naufal Fachly; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Based on data from the Central Statistics Agency, the production of mustard plants in Indonesia, especially in East Java Province in 2020 reached 77,176 tons. Where this production increased compared to the production in 2019 which reached 74,395 tons . Mustard plants in Indonesia are cultivated using several techniques, one of the techniques is hydroponics. Hydroponics is a system of cultivating plants using water that contains nutrients and minerals without soil. Plants produced from hydroponics have better quality than conventionally grown plants and also hydroponic plants can prevent plants from pests. Besides having advantages, hydroponic cultivation of mustard plants also has disadvantages such as the amount of electricity used, water discharge, solution concentration, and moss which can affect plant conditions like fresh weight. Fresh weight is one indicator of plant growth, where if the plant growth is good, the fresh weight produced will be high. Therefore, researchers want to predict the condition of hydroponic plants based on their fresh weight using digital image processing methods and artificial neural networks. This system works using a RaspberryPi as hardware that is connected to a webcam. Later the camera will capture the image of the leaves from the hydroponic mustard plant, then provide output in the form of fresh weight information on a 16x2 LCD. Based on the tests carried out on the system using 10 mustard plants to predict the fresh weight of the plant, it resulted in an an average MAPE of 0.67%. In addition , two computational time tests were also carried out , which consisted of the computation time on the system and the computation time for the Artificial Neural Network method . Where the computational time on the system produces an average time of 4.884 seconds and the computation time of the artificial neural network method with an average time of 0.192 seconds.
Implementasi FIR Filter pada Sistem Monitoring Suara Jantung dan Paru-Paru Nadi Rahmat Endrawan; Barlian Henryranu Prasetio; Rizal Maulana
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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Dipublikasikan di SIET 2022
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