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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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Sistem Deteksi Hama Babi menggunakan CNN (Convolutional Neural Network) berbasis Raspberry Pi Aditia Reza Nugraha; Fitri Utaminingrum; Hurriyatul Fitriyah
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

Indonesia is an agricultural country, part of which contains the agricultural sector, such as maize, rice, oil palm and other food crops. The area of ​​agricultural land increases every year by cutting down forests as an additional resource. Forest encroachment for the agricultural sector has an impact on the environment in which agriculture is located, where agriculture is in direct contact with forest areas. Agricultural areas adjacent to plantations cause wild animals such as wild boar to become pests that can damage plantation products and are also dangerous if they come into direct contact with humans who are present while doing activities in the area. As a pest repellent prevention, it is usually carried out by conventional means such as pig traps, poison, or nets in the affected area. As an innovation using smart technology, a solution to this problem requires a system that functions as a security supervisor for plantations. The system that will be created serves to monitor the situation when the plantation is invaded by pests such as wild boars. The surveillance system uses a webcam that is attached to the Raspberry pi model 4 which functions to see the state of the garden when there are objects of pig pests. The monitoring system on the camera will classify wild boar and farmer objects on plantations using the Convolutional Neural Network method by YOLOv3 architecture with an accuracy rate of the data model for a value of 97%. The system will also be built using a buzzer notification as a notification when a pest is detected on hardware with a success of 100% buzzer output.
Sistem Deteksi Kematangan Buah Mangga berdasarkan kandungan Gas NH3, C2H5OH dan VOCs menggunakan metode K-Nearest Neighbor (K-NN) Luqmanul Halim Zain; Eko Setiawan; Hurriyatul Fitriyah
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

Mango is a fruit from a tropical climate besides having an interesting taste characteristic, mangoes have various nutritional content and a distinctive aroma. Mango (Mangifera indica L.) has more than 270 aroma of volatile compounds in different mango varieties (Shibamoto, T. et al, 1990). One of the problems in mango production is the classification process for whether mangoes are ripe. In the case of ripe mangoes, sometimes there are mangoes that have a fairly ripe color but still taste sour, and vice versa. Because of that we need a system that can determine the level of maturity of mangoes based on aroma. In this study, a system was designed to detect ripeness in mangoes based on aroma using the K-Nearest Neighbor method. In the process of classifying the sample data, Arduino nano is used as data processing. The training data is taken from mangoes with different ripeness levels, after that the tested mangoes will be detected their gas content with TGS2602, MQ135 and MQ5 sensors, after data has been obtained it will be processed by the K-Nearest Neigbour method. The classification results of the mango ripeness level will be displayed on the LCD screen along with the sensor readings. In system testing, the results classification accuracy with 15 test data, the highest accuracy reached 86.6% at K = 3 compared to the values of K = 5.7 and 9.
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.
Alas Kaki Penimbang Berat Badan Dengan Berjalan berbasis Sensor Load Cell dan Metode Regresi Linier Afflatuslloh Adi Salung; Hurriyatul Fitriyah; 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

The ideal body is everyone's dream. From teenagers to old people, both male and female, definitely want the ideal body. The ideal body can be obtained from sufficient exercise, a healthy diet, and adequate sleep. We can find out whether our bodies are ideal or not by measuring their body height and body weight, which are adjusted from the person's age. Monitoring weight is one of the needs to maintain a healthy body. We should always check our weight, so we can control the body's healthy patterns. Sometimes measuring weight takes time, for busy people it may not be important. In fact it is very necessary to always know the weight. It will be very helpful if there is a weight meter that can be used at any time and is not required to stand still on a regular weight scale. Therefore we need a tool that can make it easier for us to monitor our weight at any time. We can check our weight when sleeping, running, eating, and all daily activities. In this study, we will create a wearable device that adds the function of sandals to a weight monitoring tool. By using several Load Cell sensors on the sandal, these sensors function to get the value of the Load Cell sensor which will be processed with the Wemos D1 microcontroller. The results of the process will come out on android devices that are commonly used every day. We can use this tool like an ordinary scale, when standing upright we can get a weight value, and this tool can also be used when walking conditions. In this study, we will focus on examining the device while walking. With this tool, we can make it easier for users to monitor their weight during daily activities without using ordinary scales.
Rancang Bangun Sistem Klasifikasi Tingkat Kematangan Pisang berdasarkan Warna Kulit dan Berat menggunakan Metode K-Nearest Neighbor berbasis Arduino Pramandha Saputra; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Currently, bananas are one of the favorite fruits that have good nutrition and taste that is liked by most people, because bananas have good nutritional content for the body. Banana is a fruit that is very beneficial for human life, which can be consumed at any time and at all ages. Constraints on the community in distinguishing ripe bananas that are suitable for management are sometimes experienced by ordinary people who do not know about the characteristics of ripe bananas and are good to manage. To make processed products, bananas with the right maturity are needed, for that a study was made on the ripeness of bananas based on the weight and color of banana peels that classify them using the K-Nearest Neighbor method. In this system there are several components, namely: Arduino Mega 2560 microcontroller to process k-nearest neighbor calculation data, TCS3200 sensor whichs is use to detect skin color on bananas, loadcell sensor as a weight gauge on bananas. The system in distinguishing the ripeness of bananas using the k-nearest neighbor method got an accuracy of 86.6%. Perform testing on the value of K = 3 and then the results of the changed K value are compared to see a more accurate K value in the k-nearest neighbour method applied to the system.
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 Tingkat Keasinan Telur Asin menggunakan Metode K-Nearest Neighbor dengan Sensor TCS3200 berbasis Arduino Mega Rian Ari Hardianyah; Hurriyatul Fitriyah; Agung Setia Budi
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

Salted eggs are one of the foods made from eggs that are preserved by salting. Most use duck eggs for preservation and it is possible to use other eggs. Salted eggs have different kinds such as boiled salted eggs, baked salted eggs, smoked salted eggs and grilled salted eggs. In detecting eggs that have been mashed and not mashed, the conventional method is still used. To distinguish between fried eggs and unmarried eggs, to determine the level of saltiness of salted eggs, the TCS3200 sensor will be used to detect the white and yolk colors. For the Arduino Mega Microcontroller, it is used for the proces implemented by the K-NN method to calculate the RGB result value. The results of the first TCS3200 obtained an average value of 4.05% while second TCS3200 obtained an average value of 8.34%. As for the accuracy of K-NN, the value is 66.67% where the difference is not too prominent so it is classified as good..
Sistem Kendali Pitch, Roll dan Ketinggian Quadcopter dengan Isyarat Tangan menggunakan Kalman Filter Muhammad Junifadhil Caesariano; Eko Setiawan; 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

UAV (Unmanned Aerial Vehicle) is an aircraft that can be controlled remotely either by humans or programmed automatically. UAV control requires a joystick controller. However, the use of a joystick as a UAV controller requires special skills in its use. Therefore, the author created a hand position-based controller that is easier to understand by people who are not familiar with the use of joysticks. However, the sensor readings are often inaccurate, therefore the Kalman Filter is used as a solution to reduce the inaccuracy. The test results show that the use of kalman filter increases the accuracy of sensor readings by using the calculation of the Root Mean Square Error. Based on testing the suitability of control using hand gestures on the movement of the UAV, the accuracy rate of control success is 85% for pitch and roll movements and 70% control success for altitude control
Rancang Bangun Sistem Klasifikasi Ukuran Baju berdasarkan Ukuran Tubuh dengan Metode K-Nearest Neighbor berbasis Arduino Xavierro Lawrenza; Hurriyatul Fitriyah; Dahnial Syauqy
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

Technological developments that are currently growing rapidly indicate the emergence of several new innovations that make work activities more practical and efficient because at this time time efficiency is an added value in the success factor in work, one of the fields of work is clothing measurement activities. Currently, the activity of measuring clothing sizes in order to get clothes of the right size requires an innovation to create efficiency and comfort. The purpose of this research is to create a system that can overcome the problems of efficiency and convenience. In this thesis research, the system was created using two ultrasonic sensors HC-SR04 as a measuring tool for body height and width as a parameter for determining the size of clothes and pants and a 16X2 LCD as the output of the size results obtained so that they are more efficient in terms of measurement and speed of time. Arduino Uno is used as a microcontroller in this study because it is hoped that its smaller size can overcome efficiency problems. The sizes that will be included as a classification include sizes S, M, L and XL. Parameters for determining the size include body height and body width. K-nearest neighbor is a classification calculation method by determining the closest distance from the test data to the training data, the number of training data for the nearest neighbor or referred to as K has been determined previously by the researcher. The calculation speed of each test data is about 10 seconds at the latest. Determination of the size using the k-nearest neighbor method because after testing and analysis this method has a high level of accuracy where the calculation results on average almost all get 100% accuracy results.
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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Untuk dipublikasikan ke Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
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