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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Abdi Masyarakat Indonesia
Fitriyah, Hurriyatul
Universitas Brawijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Rancang Bangun Sistem Deteksi Hipoksia berdasarkan Detak Jantung dan Saturasi Oksigen menggunakan Low Power Mode dengan Metode Naive Bayes Ahmad Haris Wahyudi; Edita Rosana Widasari; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypoxia is a condition when the tissues in the body lack oxygen so that their functionality is disrupted and can be fatal, such as organ damage and even death. One of the causes of hypoxia is environmental factors such as being at high altitudes with minimal oxygen. Therefore, hypoxia is also a frightening specter for mountain climbers, where there are minimal medical facilities to check the condition of climbers. Therefore, in this study designed a portable system to detect hypoxia in climbers. Hypoxia detection was carried out using heart rate (BPM) and blood oxygen saturation (SpO2) parameters whose data were acquired using the MAX30102 sensor. After that, the data was processed and classified using the naive Bayes method on the Arduino Mega 2560 microcontroller. This system uses a battery for its power source so that it is more portable and can still be used in mountain areas where there is no power source. Due to very limited battery power, a low-power mode is implemented in the form of sleep mode so that it can save battery power consumption. In testing the BPM and SpO2 readings with the MAX30102 sensor, an accuracy of 98.03% and 97.62% was obtained. In the naive Bayes classification test results obtained an accuracy of 93.33% with a computation time of 829.2 µs. As well as for power efficiency with low power mode, a decrease in current consumption reaches 70.13%.
Klasifikasi Kelayakan Susu Sapi UHT berdasarkan PH, Warna, dan Aroma menggunakan Metode Naive Bayes berbasis Arduino Muhammad Daffa Bintang Nugroho; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Ultra High Temperature cow's milk is a type of milk that is widely consumed by humans from children to adults because milk has a myriad of health benefits, but if the cleanliness of the processing is not maintained, milk quickly becomes unfit for consumption and can cause various adverse effects for a person who consume it. There are many ways to determine the appropriateness of consuming milk, such as by looking at its color, smelling the aroma, or tasting the milk, but these methods are ineffective and raise doubts about whether milk is still suitable or not suitable for consumption. This study designed a system to determine the feasibility of Ultra High Temperature cow's milk based on pH parameters, ammonia gas content, and color. To determine the value of each parameter, a PH 4502C sensor, MQ135 gas sensor, and TCS3200 color sensor are used to carry out the process of determining the feasibility of milk using the Naive Bayes classification method which is processed with the Arduino Mega 2560 microcontroller. The results of the accuracy of the system test to determine the feasibility of milk Ultra High Temperature cattle based on pH parameters, ammonia gas content, and color with the Naive Bayes classification method using 40 training data and 20 test data is 85% with a computation time of 1,911 seconds.
Deteksi Penyakit pada Daun Jagung berdasarkan Hue menggunakan Metode K­Nearest Neighbor berbasis Raspberry Pi Handy Yusuf; Hurriyatul Fitriyah; Sabriansyah Rizqika Akbar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Indonesia is an agricultural country where the majority of the population works in the agricultural sector. Corn (Zea mays) as one of its commodities, is widely used as food, animal feed, industry, oil, and beverages. However, the increase in demand for corn was not supported by an increase in production. This was initiated by several factors, one of which was pests and diseases in corn, such as downy mildew and leaf blight. This study aims to detect corn leaf disease based on Hue using the Raspberry Pi-based KNN method, implement the KNN method for classifying diseases on corn leaves, and determine the accuracy of the system in the classification process. The process of detecting diseases on corn leaves based on Hue uses the Raspberry Pi-based KNN method supported by hardware including the Raspberry Pi 4 model B, webcam camera, laptop, 10 I2C, 16x2 LCD, and power bank. While software includes Jupyter Notebook, Open CV, NumPy, and Scikit-learn. The hardware implementation of the system uses a webcam camera as input, the image results obtained will be processed with the Raspberry Pi 4 model B, after which the results of the classification are displayed in the form of output on the 16x2 LCD. Based on the test, it can be seen that the implementation process from input to output is going well. System accuracy testing produces an accuracy value of 90% with an average computation time of 0.4984 for one test process.
Klasifikasi Kesiapan Panen Tanaman Hidroponik Bayam Hijau menggunakan Metode Pengolahan Citra dan K-Nearest Neighbours berbasis Raspberry Pi Bilawal Haesri; Hurriyatul Fitriyah; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Green spinach is one of the vegetables that is favored by the Indonesian people for consumption, this can be seen from the increasing production of green spinach from 2020 to 2021 with an increase of 9%. Generally, the cultivation of green spinach is done conventionally using soil as the plant medium, but this cultivation technique requires a large area of land. One alternative way to cultivate green spinach is through hydroponic techniques that utilize nutrient-rich water as the plant growth medium. The readiness of hydroponic plants for harvest can be determined by their age, but it must first be seen from the shape and size of the hydroponic plants to be harvested. The problem found in hydroponic farming is that hydroponic farmers need to do regular monitoring to determine whether the hydroponic plants are ready to harvest or not on each plant that takes a long time, which reduces the effectiveness of plant production, because it will inhibit the hydroponic production cycle. In this study, we will build a system that can classify the readiness for harvest of hydroponic green spinach using digital image processing and K-Nearest Neighbors classification. The system uses a webcam to take images, Raspberry Pi as an image processing and classification device, and a 20x4 LCD to display the harvest readiness classification results. The system was tested using 12 images of green spinach with a classification accuracy using K-NN of 100% at K=3 and an overall computation time of the system with an average value of 1.4 seconds.
Implementasi Metode Decision Tree untuk Sistem Pendeteksi Stres berdasarkan Detak Jantung dan Kelenjar Keringat Rejeki Puspa Dinasty; Edita Rosana Widasari; Hurriyatul Fitriyah
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

Emotions greatly affect individual behavior and stress refer to physiological responses that occur when individuals fail to respond appropriately to emotional or physical threats. For this reason, it is important to know a person's mental stress, so that further action can be taken, so as not to have a serious impact on physical and mental health. Using the decision tree method with HR and GSR features to detect stres is useful because this method is able to handle data that is not well structured and provides an easily understood visual representation of the decision making process. In addition, HR and GSR features can provide useful information about a person's physical state that can be used to identify stress levels. From the results of system testing with MAX30102 and GSR sensors The accuracy of reading MAX30102 and GSR sensors is seen from the accuracy of the level stress results displayed and the value of the respondent questionnaire results. Stres detection systems through the heart rate and sweat glands have an accuracy of 98.5% while for the average system computing time needed to detect stress of 36802.83 ms or 36.80 seconds. The accuracy results were obtained by testing 24 respondents.
Sistem Klasifikasi Air Mineral Layak Minum berdasarkan Nilai PH dan Kekeruhan Menggunakan Metode Naive Bayes berbasis Arduino Uno Faza Gustaf Marrera; Barlian Henryranu Prasetio; Hurriyatul Fitriyah
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

Water is a very important natural resource for the survival of humans, one of which is as a source of consumption. The safety standards for drinking water established by the government are very important to ensure the safety of the drinking water we consume. To be safe for health, drinking water must meet physical, microbiological, chemical, and radioactive requirements. However, there are still some areas in Indonesia that face difficulties in ensuring the safety of the drinking water they consume. From this problem, researchers want to make a drinking water quality detection system that can provide information related to pH and water clarity to users. The drinking water quality detection system is made using the pH-4502C and turbidity SEN0189 sensors and the Arduino UNO microcontroller connected to the 16x2 LCD as an information display. The results of the two sensors will then be classified using the Naive Bayes method. In this study, sensor testing and method testing have been conducted. The results obtained in the pH-4502 accuracy test are 96.54%. Then, based on the results of the SEN0189 turbidity sensor test, it can be seen that the SEN0189 turbidity sensor can work well in reading the condition of water being cloudy or not. From the readings taken on drinking water (clear) and coffee water (cloudy), it can be seen that the voltage values produced by the sensor are different. A higher voltage value indicates that the water is clearer, while a lower voltage value indicates that the water is cloudier. In the Naive Bayes accuracy test, the result obtained is 100% of 15 total test data and 30 training data. The data is in the form of drinking water in a public environment.
Pengendalian pH Air pada Budidaya Ikan Lele dan Kangkung dalam Ember (Budikdamber) menggunakan Regresi Linear berbasis Arduino Wisnumurti Wisnumurti; Hurriyatul Fitriyah; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 13 (2023): Publikasi Khusus Tahun 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dipublikasikan di JTIIK (Jurnal Teknologi Informasi dan Ilmu Komputer)
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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Abstract

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.
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.
Sistem Pengenalan Plat Nomor Kendaraan untuk Akses Perumahan menggunakan YOLOv5 dan Pytesseract berbasis Jetson Nano Muhammad Rizky Rais; Fitri Utaminingrum; Hurriyatul Fitriyah
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

Security is important for housing. In general, housing now uses rfid to enter through the gate. In addition to the use of rfid, there are human workers who help secure access to housing. However, access to housing using rfid and human labor has the disadvantage that rfid can be lost and human labor can feel tired. With the existing problems, a system is needed that can help cover these weaknesses. A vehicle license plate detection system was created for residential residents using the YOLOv5 and jetson nano-based pytesseract methods. YOLOv5 (You Only Look Once) is a fast and accurate new object detection algorithm that is suitable for real-time applications. This system will recognize the vehicle number plates of residential occupants starting from training data and license plate detection using YOLOv5 and then, when the vehicle number plate has been detected, the contents of the number plate will be read using the pytesseract ocr (optical character recognition) method so that it can open the housing access door latch. . This recognition system uses training data of 502 images of vehicle license plates. The result of this test is the movement of the servo motor for housing access when the number plate is detected in the data file. The accuracy obtained in this study was 100% for the number plate detection system and 100% for the identification of vehicle occupants' vehicle license plates.
Co-Authors Abdurrahman Diewa Prakarsa Abimanyu Sri Setyo Achmad Baichuni Zain Aditia Reza Nugraha Aditya Rafly Syahdana Afflatuslloh Adi Salung Agi Putra Kharisma Agif Sasmito Agung Setia Budi Ahmad Fahmi AdamSyah Ahmad Fatchi Machzar Ahmad Haris Wahyudi Ahmad Wildan Farras Mumtaz Alfatehan Arsya Baharin Ali Ilham Ainur Rahman Allif Maulana Ananda Ribelta Andhika Rizky Fariz Andi Dwi Angga Prastya Andy Hartono Aprilo Paskalis Polii Aries Suprayogi Bagus Sawung Timur Barlian Henryranu Prasetio Belsazar Elgiborado Giovani Djoedir Bilawal Haesri Bimo Dimas Nugraraga Boris Wiyan Pradana Chandra Gusti Nanda Putra Cut Fahrani Dhania Dahnial Syauqy David Isura Dede Satriawan Denis Andi Setiawan Dewi Pusparini Dian Bagus Setyo Budi Diego Yanda Setiawan Dimas Bagus Jatmiko Dimas Dwi Saputra Dimas Firmanda Al Riza Dimas Guntoro Dipatya Sakasana Dody Kristian Manalu Dwi Fitriani Edhi Setyaw, Gembong Eko Ardiansyah Eko Setiawan Erdano Sedya Dwiprasajawara Esa Prakasa Fadhilatur Rahmah Faizal Andy Susilo Fajra Rizky Falachudin Akbar Fatchullah Wahid Afifi Faza Gustaf Marrera Fikriza Ilham Prasetyo Gembong Edhi Setiawan Gembong Edhi Setyaw Gembong Edhi Setyawan Gunawan Wahyu Andreanto Habib Muhammad Al-Jabbar Hafizh Hamzah Wicaksono Hamdan Zuhdi Dewanul Arifin Hamzah Attamimi Handi Handi Handy Yusuf Herwin Yurianda Ichwanul Muchlis Imam Pratama Setiady Indera Ulung Mahendra Iqbal Koza Irham Manthiqo Noor Issa Arwani Ivana Agustina Julisya Thana Khriswanti Khairul Anwar Komang Candra Brata Lashot Ria Ingrid Melanika Lintang Cahyaning Ratri Luqmanul Halim Zain M Ilham Fadilah Akbar M Nuzulul Marofi M. Fiqhi Hidayatulah Marrisaeka Mawarni Mimi Hamidah Moch Zamroni Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Mohamad Abyan Naufal Fachly Mohamad Misfaul May Dana Mohammad Isya Alfian Mohammad Lutfi Zulfikri Muh. Syifau Mubarok Muhamad Delta Rudi Priyanto Muhamad Ichwan Sudibyo Muhammad Ammar Hassan Muhammad Daffa Bintang Nugroho Muhammad Fatham Mubina Akbar Muhammad Irfan Reza Muhammad Junifadhil Caesariano Muhammad Raihan Al Hakim Muhammad Rifqi Radifan Masruri Muhammad Riyyan Royhan Muhammad Rizki Chairurrafi Muhammad Rizky Rais Muhammad Rizqi Zamzami Muhlis Agung Saputro Musada Teguh Andi Afandi Nafisa Nafisa Nashir Umam Hasbi Nico Dian Nugraha Nur Aini Afifah Isbindra Nur Syifa Syafaat Okky Nizka Pratama Oktaviany Setyowati Olivia Rumiris Sitanggang Pandy Aldrige Simanungkalit Pramandha Saputra Putra Wijaya Putri Harviana Raden Galih Paramananda Rakhmadhany Primananda Rando Rando Refsi Ilham Cahya Rejeki Puspa Dinasty Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Damayanti Rian Ari Hardianyah Ricky Zefani Aria Zurendra Rifqi Alvaro Rifqi Imam Ramadhan Rizal Maulana Rizka Ayudya Pratiwi Rizqy Maulana Rosa Mulyanis Chan Sabriansyah Rizqika Akbar Salsabiil Hasanah Samuel Lamhot Ladd Palmer Simarmata Satyaki Kusumayudha Septian Mukti Pratama Shafa Sabilla Zuain Sulthan Ghiffari Awdihansyah Syarief Taufik Hidayatullah Tatit Kisyaprakasa Thomas Oddy Chrisdwianto Tibyani Tibyani Tri Oktavia Mayasari Tunggal Manda Ary Triyono Utaminingrum, Fitri Wahyu Hari Suwito Widasari, Edita Rosana Wijaya Kurniawan Wildo Satrio Wisnumurti Wisnumurti Xavierro Lawrenza Yusuf Hendrawan Zultoni Febriansyah