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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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Simulasi Algoritme Hector SLAM untuk Pemetaan 2D pada Quadcopter berbasis ROS Selina Kusmiawati; Eko Setiawan; Edita Rosana Widasari
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

Quadcopter is an Unmanned Aerial Vehicle (UAV) that is deployed to operate in areas that are not maximally accessible by Unmanned Ground Vehicles (UGV) in geographic structures that have been distorted due to natural disasters. Quadcopter requires the ability to recognize the surrounding environment by using a map. A map is a set of features that describe the environment such as walls, obstacles, landmarks, etc. Maps are relatively easy to make in a static environment, but in a disaster-damaged environment, maps will be more difficult to create because the environment has changed. The solution to this problem is that the quadcopter must be able to build its own environmental map. To build a map, a mapping process is needed that can be done using Simultaneous Localization and Mapping (SLAM). Hector SLAM is one of the SLAM algorithms which works based on scan matching technique and without odometer. Simulations were carried out to test the 2D mapping results from the Hector SLAM algorithm. The mapping was carried out with a LiDAR sensor embedded in the quadcopter and tested in 3 different environments. Simulations were carried out with 3D Gazebo and Rviz simulators based on Robot Operating System (ROS). There are 36 test scenarios carried out with the best map accuracy obtained with a Structural Similarity Index (SSIM) value of 0.78, Mean Squared Error (MSE) value of 5344.1, and Pixel Matching percentage of 89.59%.
Rancang Bangun Sistem Portabel untuk Klasifikasi Cendol Merah Mengandung Rhodamin B menggunakan Metode Jaringan Syaraf Tiruan Muhammad Fadhil Sadeli; Dahnial Syauqy; Edita Rosana Widasari
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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Cendol is a traditional West Javanese drink made from hunkwe flour or mung bean flour. Cendol that is often found is green cendol. However, there are also cendol sellers who use red cendol derived from food coloring or agar powder. However, there are still cendol sellers who add synthetic dyes containing Rhodamin B to cendol as a dye. Because the price is relatively cheap and makes the color more striking so that buyers become interested in buying the cendol. Rhodamine B is a synthetic dye in the form of a crystalline powder, green in color, odorless, and fluoresces in a bright red solution. Rhodamine B is very dangerous if consumed and inhaled which can cause liver function disorders, cancer, irritation of the respiratory tract, skin, and eyes. The misuse of these dyes occurs due to a lack of public knowledge about how to distinguish food coloring from Rhodamine B dye and the dangers of its use. Therefore, the researchers designed a system that can classify cendol containing Rhodamine B based on color. The system is built with a portable design for efficiency and portability. This system uses a power bank as a resource, then uses a TCS3200 sensor to determine the RGB value of the cendol color, a 16x2 LCD with I2C to display the output and classification results of the system, Arduino uno as a microcontroller to process data and calculate classifications, and an Artificial Neural Network (ANN). as a classification method. This system utilizes 50 sets of training data, 25 sets of test data for the ANN method, and 15 sets of test data for the whole system. Based on the results of the tests carried out, the accuracy of the Artificial Neural Network method was 96.08% with an average computation time of 58 ms and an overall system accuracy of 93.34%.
Implementasi YOLO versi 3 untuk Mengidentifikasi dan Mengklasifikasi Sampah Kantor berbasis NVIDIA Jetson Nano Onky Soerya Nugroho Utomo; Fitri Utaminingrum; Edita Rosana Widasari
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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Waste has become a problem that is always found in several big cities in Indonesia. Waste management in Indonesia has not been effective in dealing with the increasing amount of waste. One type of waste that continues to grow is office waste which is common in urban areas. Office waste is inorganic waste generated from the activities of office employees. The office waste generated can cause problems in the environment if it is not managed properly, it is necessary to manage waste by sorting waste according to its type. In this study, we design a classification of office waste in the form of paper, plastic bottles, and cans to sort waste according to categories. This office waste classification process uses the YOLO algorithm or You Only Look Once. The YOLO algorithm or You Only Look Once is one of the algorithms used to detect an object in real-time. Based on the results of the tests that have been carried out for object detection, the accuracy results are 94%. After that, the integration test for the classification system obtained an accuracy of 97.3% and for testing the computational time for the classification system the best value for the computational time was 0.271 seconds.
Klasifikasi Frekuensi Penggunaan Minyak Goreng Ikan dan Tahu menggunakan Metode Jaringan Syaraf Tiruan berbasis Arduino Aulia Zhafran; Dahnial Syauqy; Edita Rosana Widasari
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

Palm cooking oil which belongs to the basic food category (SEMBAKO) is a food element made from triglycerides derived from palm oil. Yellow to orange is the normal color found in palm cooking oil. Using the same cooking oil continuously can reduce the quality of the cooking oil and can be dangerous for the health of consumers. Frequency classfication system is needed to find the accurate amount of used cooking oil. The parameters used in the classification process are color and turbidity which are tested using a TCS3200 sensor to process and measure color and an LDR sensor to process and measure the level of turbidity of cooking oil connected to Arduino Uno and use the Artificial Neural Network (ANN) classification method. The classification results are divided into 7 classes, namely pure oil, 1 time fish frying, 2 fish frying, 3 fish frying, 1 times tofu frying, 2 times tofu frying, and 3 times tofu frying. The classification results will be displayed on a 20x4 I2C lCD. Based on the test results, the accuracy of the color sensor is 98.827% and the LDR sensor can see the difference in the level of turbidity of a cooking oil so that the system can have an accuracy rate of 80% in computation time for 5,114 seconds after processing 70 training data and 20 test data.
Klasifikasi Kandungan Boraks pada Gendar menggunakan Sensor Warna dengan Metode Jaringan Syaraf Tiruan berbabsis Arduino Andhika Nino Pratama; Dahnial Syauqy; Edita Rosana Widasari
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

Gendar is one of the traditional foods typical of Central Java that can be found until now. The texture of the gendar itself is like rice cake or ketupat but is more chewy and has a more savory taste. In ancient times, the use of bleng salt or what is now called borax was commonly used in the process of making gendar because it can provide a savory taste of food and provide a legit and chewy texture as well as a preservative for gendar. Borax is a dangerous chemical compound which if consumed by the body does not cause an immediate reaction. The safe limit for the use of borax itself is 1 gram in 1 kg of food and the fatal dose when consumed and enters the body for children is 3 - 6gr and for adults is 15 - 20gr. The rampant ignorance of the public regarding the safe limits of borax that enters the body has prompted researchers to design a system that can classify the borax content in gendar based on 3 classes, namely no borax, light borax, and heavy borax. The system utilizes Arduino Uno as a data processor and classification calculation, a color sensor that is used as a color detector for the gendar object being tested, and a 16x2 LCD to display the classification results. The classification process itself uses the backpropagation artificial neural network classification method. Based on the system testing process, of the 30 samples tested, 90% accuracy was obtained with the average computation time required by the backpropagation Neural Network in the classification process is 3057ms or 3 seconds and 0.057 seconds.
Rancang Bangun Sistem Pendeteksi Central Sleep Apnea menggunakan K-NN berbasis Arduino Bluetooth Module Qonita Luthfiyani; Edita Rosana Widasari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Sleep apnea is a major problem with sleep disorders that cause partial or complete cessation of breathing during sleep. Detection of Central sleep apnea disease is considered important because this condition causes the body difficulty breathing in a short time at bedtime which will wake up the patient's sleep so that it will be difficult to sleep well and experience excessive drowsiness during the day. The National Sleep Foundation (NSF) suggests that about 20 percent of people experience excessive daytime sleepiness, which is caused by a person not getting enough sleep. However, central sleep apnea screening with polysomnography is ineffective because it requires a lot of sensors and must go to the hospital. The goal of the study was to create a system that could effectively detect central sleep apnea using recording physical activity only from the heart with ECG signals connected to a smartphone with bluetooth because people with Central sleep apnea correlate with cardiovascular disease. The system uses arduino UNO, ECG AD8232 sensor to record ECG signals, and HC-05 bluetooth module for wireless communication with smartphones. The system detects by taking the signal through the electrode then the signal result will be extracted using the RR interval feature and QRS duration and classified using the K-NN method. The results of the classification display are displayed on LCD and smartphone via bluetooth. The results shown are in the form of classes "Normal" or "Central sleep apnea". The resulting accuracy in testing with K-NN was 83.33% and the average K-NN compute time was 78,7 ms.
Sistem Kendali Intensitas Cahaya dan Kelembaban Tanah untuk Umbi Porang (Amorphophallus Oncophyllus) menggunakan Metode Logika Fuzzy Nur Syifa Syafaat; Hurriyatul Fitriyah; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Porang is a tuber that has high mineral and glucomannan content, of which glucomannan used for the pharmaceutical, beverage, cosmetic, adhesive/glue, and textile industries. In 2018, 254 tons of porang tubers were exported, with a value of Rp. 11.31 billion. Porang has its own growing requirements, including the height of the planting area between 100-600 masl, temperature 25-35 °C, loose soil texture with high organic content, good air aeration, neutral pH between 6-7, requires about 30% shade, and soil moisture of about 40%. Therefore, planting to harvesting porang tubers can only be done once a year, planting during the rainy season and harvesting during the dry season. Due to this, a control system is needed for light intensity and soil moisture so that the cultivation of this porang plant is more optimal. This system uses a BH1750 sensor which functions to measure light intensity and a soil moisture sensor to measure soil moisture in plants. This system uses Arduino UNO as a controller for controlling light intensity and soil moisture. In testing on the fuzzy logic method, soil moisture data has an accuracy of 70% and light intensity has an accuracy of 80%, and on average both have an accuracy of 75%. In 10 trials the average time required is 1.799 seconds..
Implementasi Alat Monitoring Kesuburan Lahan Pertanian Ketela Pohon berbasis Web Fathul Abdillah Khosin; Mochammad Hannats Hanafi Ichsan; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Cassava is a plant that can be used as a substitute for the daily staple food. Many farmers encounter obstacles at the time of post-harvest cassava, which is not suitable for harvesting for consumption. Most farmers do not know the fertility of the land to be planted. Implementing a monitoring tool on the system for observing the fertility of cassava agricultural land can be a solution. The method used is the Telemetry method. Where Telemetry is the process of measuring the parameters of an object (objects, space, natural conditions) whose measurement results are sent to other places via cables or without using cables (wireless). Telemetry is expected to provide convenience in measurement, monitoring and reduce barriers to obtaining information. Sensors are connected to components, then these components will be connected to each other through communication in the sensor network. From the implementation of the test, the results obtained from the test were that the tool worked properly where the monitoring tool could retrieve data from the research place where the sensor was installed. Then the monitoring tool can also send data to the webserver and the webserver successfully receives data from the monitoring tool and then saves it to the database on phpMyAdmin and displays it on the web pc or cellphone. Performance is obtained based on throughput, packet loss and delay with the result that the throughput is 8,567 kbps. The packet loss of is 0.1%. The total delay is 4.449 s and the average delay is 1.6322 ms.
Perancangan Sistem Pengamanan Ganda pada Brankas menggunakan Convolutional Neural Network berbasis Raspberry Pi Muhamad Fauzan Alfiandi; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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In human life, one of the most important things is security. Security works to prevent, protect assets, physical or digital items that we own from theft and lost items. According to the data from Indonesian National Police Yogyakarta Region, the number of theft cases in 2021 has reached 1219 cases, and that's why a protection system is necessary as an effort to guard against any thief. The commonly used protection system for physical items is a safety box. Technological advancements especially hardware, encourage people to help, simplify and solve problems. Microcontroller technology is currently evolving. Microcontroller serves a digital processing purpose and certain program and instruction can be made according to what we want. Technological advancements can be associated with the security field such as biometric face recognition. This face recognition system can recognize a person's face. To construct a protection system preventing theft, this research uses double security on a safety box, PIN and face detection. Applying the deep learning Convolutional Neural Network for face detection so the system can detect the safety box owner's and not the owner's face. PIN number combination must be inputted to lock the safety box using a solenoid lock. The purpose of this research is to construct a double security safety box without risking losing a key. According to the test results, the system can detect the owner's face object with 83% accuracy, 81% precision, 86% recall with 8.19 seconds of computing time, 100% success rate of PIN input, face detection and keypad integration to solenoid lock test results with a 100% success rate.
Implementasi Wearable Device untuk Sistem Pendeteksi Stres pada Manusia berdasarkan Suhu Tubuh dan Detak Jantung Izzati Firsta Wijayanti; Edita Rosana Widasari; Barlian Henryanu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stress is the result of an unusual situation, with factors such as good or bad events, frustration, pressure, and environmental conditions causing stress. Stress can affect the condition of the body, and if a person experiences it, the body's reactions include excessive sweating, shaking, an increase in heart rate, rapid breathing, and headaches. Stress will be detected earlier and treated more effectively. As a result, we require a practical stress detector that is simple to use and can determine a person's condition based on his heart rate and body temperature. This stress detector uses two parameters: heart rate, which is detected by the MAX30102 sensor, and body temperature, which is measured by the MLX90614 sensor and processed by the Arduino Nano. The outcome of developing a tool with both sensors detecting via the fingers displayed on the OLED 0.96 inch and the accurarcy of the measurement between MLX90614 and thermometer is 98.51%, the measurement between MAX30102 and pulse oximeter got 92.43% accuracy and the overall test of the tool that has mad comparisons with DASS 42 stress scale got 70% accuracy using 10 subjects. The average computations time when a person detected as stress is 0.359 seconds.
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