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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal technoscientia Jurnal Mahasiswa TEUB Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJID (International Journal on Informatics for Development) Indonesian Journal of Electrical Engineering and Computer Science JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Mechanical Engineering for Society and Industry
Utaminingrum, Fitri
Fakultas Ilmu Komputer, Universitas Brawijaya
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Klasifikasi Kualitas Minyak Goreng berdasarkan Fitur Warna dan Kejernihan dengan Metode K-Nearest Neighbour berbasis Arduino Uno Hilman Syihan Ghifari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cooking oil is a staple ingredient that people consume in their daily life. There are various types of oil circulating among the public, including packaged oil, bulk oil, and used cooking oil. The use of cooking oil repeatedly can damage the quality of cooking oil and increase the risk of several diseases, including carcinoma. To determine the quality of oil, there are several kinds of chemical tests, including the determination of the peroxide number, saponification number, iodine number, and acid number. And can also be seen directly through the color and clarity of the oil. The design of this classification system uses color and clarity as input features of the system and used cooking oil as test objects and system datasets. To measure color and clarity, TCS3200 and LDR sensors are used. The classification process starts from inserting the used cooking oil object into a 50ml beaker glass, then inserting the beaker glass into the system with a 1cm position next to the TCS3200 and LDR sensors. The light will pass through the object and go to the TCS3200 and LDR sensors, then the reading results will be sent and processed via Arduino UNO using the K-Nearest Neighbor method and the final result in the form of proper and less appropriate classification will be displayed on the LCD monitor. The K-Nearest Neighbor classification was chosen because it is considered to have good accuracy with a limited dataset. The dataset is divided into 2, namely, 17 training data and 8 test data. From the results of the tests carried out with 8 test data for used cooking oil, the accuracy results were 75%.
Klasifikasi Jumlah Daun pada Semai Hidroponik menggunakan Pengolahan Citra dan Jaringan Syaraf Tiruan berbasis Raspberry Pi Ahmad Wildan Farras Mumtaz; Hurriyatul Fitriyah; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hydroponics is a technique of growing crops without using soil as a growth medium. A plant that is often cultivated and applied to hydroponic practices and has a relatively fast harvest period is mustard pakcoy(Brassica rapa subsp. chinensis). To produce healthy and quality mustard pakcoy, monitoring is needed, especially during the seeding period for ±21 days. When the seedling period is over, the plant must be removed immediately so that it does not quickly age and inhibit vegetative growth. In this study, an automatic monitoring system for the number of mustard pakcoy leaves was designed. This system utilizes image processing and uses a raspberry pi-based Artificial Neural Network method in its manufacture. The mustard leaf image will be captured using a webcam camera which will then be processed into a raspberry pi and the detection results will be displayed on a 16x2 i2C LCD. The training data used in this study were 300 data. The results of 40 tests carried out by this monitoring system get an accuracy of 92.5% with a system computing time of 4.6445 seconds and a computation time of the ANN method 1.2185 seconds.
Rancang Bangun Sistem Pengklasifikasi Jenis Sampah Organik dan Anorganik menggunakan metode You Only Look Once versi 3 berbasis Raspberry Pi Figo Ramadhan Hendri; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this day and age, industrial progress is increasing rapidly, thus increasing the amount of industrial waste and household waste produced by humans. The accumulation of waste is caused by the long processing time of waste. Therefore, the design of a system for classifying types of waste between organic and inorganic can help separate types of waste automatically. In the design of this system, the YOLOV3 method is used. This study has several classes, namely apples, oranges, bananas, and vegetables as the type of organic waste and the class of plastic bottles, plastic cups, and cans as the type of inorganic waste. Before the detection of waste, training was carried out on the waste dataset, which amounted to 7000 images with each class having more than 1000 images. The image that has been trained will be calculated for accuracy. The calculation in the test resulted in an accuracy of 93.7% for the type of inorganic waste. While the accuracy generated on the type of organic waste is 92%. After that, the computation time on the system is calculated and the average computation time for inorganic waste is 12.5433 seconds and the average computation time for organic waste is 15.1685 seconds. The last test that was carried out was the accuracy of waste accuracy which had the smallest result of 80% and the largest 100%.
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.
Rancang Bangun Sistem Klasifikasi Kualitas Minyak Goreng berdasarkan Warna dan Kejernihan menggunakan Metode Naive Bayes berbasis Arduino Uno Joan Chandra Kustijono; Fitri Utaminingrum; Barlian Henryranu Prasetio
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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Abstract

Cooking oil is one of the basic needs of the Indonesian people for food processing ingredients, especially for frying process. Palm cooking oil is commonly used by Indonesian people because it matches the Indonesian people's taste. In its use, cooking oil is often used repeatedly for savings which is known as used cooking oil. The use of used cooking oil can increase the risk of several diseases for the body, such as carcinoma and diarrhea caused by the accumulation of peroxide values, as well as changes in the color and clarity of the oil. To determine the quality of the oil, the method used today is to test the peroxide number in the laboratory. However, this method takes more time and is not cheap. So that a system was created that can detect the quality of cooking oil based on color and clarity using the Naive Bayes method to shorten the time at a lower price. This study uses a TCS3200 sensor to detect color changes, and an LDR sensor to detect oil clarity. There are 2 classes used, namely the Eligible class to declare that the oil is still suitable for use and the Inappropriate class to declare that the oil is not suitable for use. In the research results, the accuracy of the TCS3200 sensor is 95.15%, the accuracy of the Naive Bayes method is 87,5%, and the overall system accuracy is 87.5%.
Sistem Pemilihan Ikan Lele Siap Panen berbasis Mikrokontroler dengan Metode K-Nearest Neighbors Wisik Dewa Maulana; Dahnial Syauqy; 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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Abstract

Catfish is one type of fish that is often consumed by the community, catfish is also one of the fish with high economic value. In addition to high economic value, catfish is also a fish that has a fairly fast growth. Usually catfish with the age of 3.5 - 4 months are ready to be harvested, but even though the age of catfish has entered the harvest period, not all catfish have the same size as one another. That's all caused by the greedy nature of catfish and the imbalance in the number of catfish with the size of the pond. So farmers prefer to do manual sorting to determine which catfish are ready to harvest based on weight and length. Therefore, this study aims to assist catfish farmers in sorting catfish that are ready to harvest with the help of a load cell sensor to measure weight and an ultrasonik sensor to measure length. Then the two values will be classified using the K-Nearest Neighbors method with the help of Arduino as the main system for classifying and also servo as the output of this system. This study tested the load cell and ultrasonik sensors as input for the length and weight while the classification using the K-Nearest Neighbors method got an accuracy value of 81,25% with computing time 33 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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Abstract

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 Fault Tolerant System menggunakan Self-purging Redundancy pada Sistem Monitoring Suhu Ruang Server Alfan Rafi'uddin Ardhani; Agung Setia Budi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Server is a computer system that serves and controls access to clients that connected to it. Server is generally placed in a special room equipped with air conditioning. This is done to keep the temperature of server's components that generate heat when working. Server that work at high temperatures may cause a decrease in performance and if left unchecked can cause server outages and damages to its components. In several studies that have been done, the application of fault tolerant system is rarely used where the system only relies on a single sensor to obtain temperature data so it is susceptible to fault. Therefore, in this study, the self-purging redundancy method is used to determine the effect of the method on system's fault handling. In this research, 3 redundant modules are used in the form of Arduino Nano and temperature sensor DHT-22 to obtain temperature data which is then sent to a voter in the form of Arduino Uno. From this study, it is found that self-purging redundancy method has an effect on system's fault handling, where the system can tolerate error that occur in one redundant module at one time. In addition, the system has an accuracy of 85% and an average computation time of 14,8 milliseconds.
Monocular Depth Estimation pada Scene dalam Ruangan menggunakan U-Net dengan ResNet Krisna Pinasthika; Fitri Utaminingrum
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

Autonomous driving systems have become a topic of interest in academia, industry, and the military in recent years. Active sensors such as Light Detection and Ranging (LiDAR) can generally be used to measure the distance to an object, but the costs and computations required are very large. To obtain a relatively lower cost, a monocular camera is the solution. Based on previous research, estimating the depth value in images from monocular cameras using the Deep Neural Networks (DNN) method is proven to work well. The U-Net DNN architecture using Residual Network (ResNet) on the encoder section is used in this research. The process of training, validation, and model testing were carried out on the DIODE: A Dense Indoor and Outdoor Depth Dataset with a total of 8899 data. The training and validation phases are carried out using Adam optimization. This research obtained the best model using a learning rate of 1e-3 and a weight on the loss function of 0.3. This model obtains an evaluation metrics that are able to compete with previous studies with an RMSE value of 0.2272, an REL of 1.3676, an accuracy with a threshold of 1.25 of 56.22%, an accuracy of a threshold of 1.252 of 78.97%, and an accuracy of a threshold of 1.253 of 89.29%. Testing the inference model obtains the number of frames per second (FPS) in the range of 5-12 FPS.
Sistem Parkir Otomatis berdasarkan Pengenalan Jenis Kendaraan menggunakan Metode Yolov3-Tiny Gabe Siringoringo; Fitri Utaminingrum
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

Indonesia is the country with the highest population growth in the world. The increasing population of Indonesia has resulted in higher transportation needs because people prefer to use private vehicles rather than public transportation. This causes problems for motorists when looking for an empty parking space in the parking area. To overcome this problem, the author uses the YOLOv3-Tiny algorithm method to detect and identify parking spaces. The research conducted by the author is an implementation of development research. The author shows a literature study to obtain the required information, performs requirements engineering to analyze needs in conducting research, then designs and implements a system based on needs. The design and implementation results will be tested and evaluated until the end of the stages carried out are concluded. From the results of system testing, the system obtained that the accuracy of the information for car parking slots was 98% and 97% for motorcycle parking lots. The system's accuracy rate for detecting car objects is 98.48%, and 95.27% for motorbikes. The average value of the accuracy of the servo response when opening and closing the bars is 88.88%. The results of the CPU speed test in 3-speed modes obtained the best performance at 2.0GHz. Therefore, an automatic parking system prototype can be made and can carry out its essential functions. The system can detect empty and non-empty parking lots by object detection and classification using the YOLOv3-Tiny model. The system performance results for speed detecting and displaying parking slot information with a maximum CPU speed of 2.3 fps and the fastest average system computing time at 0.41 seconds per loop from the beginning of the detection process to the end of displaying information.. The system's accuracy influences the results of the information displayed in detecting objects.
Co-Authors Abadi, Dendy Satria Abiyyu Herwanto Achmad Dinda Basofi Sudirman Achmad Jafar Al Kadafi Adam Ibrahim, Muhammad Adharul Muttaqin Adinugroho, Sigit Aditia Reza Nugraha Afdy Clinton Afrizal Rivaldi, Afrizal Agung Setia Budi Agung Setia Budi Agung Setia Budi, Agung Setia Agus Wahyu Widodo Ahmad Wali Satria Bahari Johan Ahmad Wildan Farras Mumtaz Ainandafiq Muhammad Alqadri Akbar Dicky Purwanto Akbar Wira Bramantya Akbar, Muhammad Danar Al Amin, Nisrina Fairuz Hafizhah Al Huda, Fais Alfan Rafi'uddin Ardhani Alfianto Palebangan Alhamdi, Achmad Fahri Aliffandi Purnama Putra Alrynto Alrynto Alvin Evaldo Darmawan Amalia Septi Mulyani Amaliah, Ichlasuning Diah Andika Bayhaki Al Rasyid Syah Andika Kalvin Simarmata Andrika Wahyu Wicaksono Anugrah Zeputra Arthur Ahmad Fauzi Asep Ranta Munajat Asfar Triyadi Audrey Athallah Asyam Fauzan Aufa Nizar Faiz Auliya Firdaus Awalina, Aisyah Bagas Nur Rahman Bagus Septian Aditya Wijayanto Barlian Henryranu Prasetio Beryl Labique Ahmadie Blessius Sheldo Putra Laksono Budi Atmoko Burhan, M.Shochibul Cahyo, Muhammad Pandu Dwi Candra, Alvin Choirul Huda Constantius Leonardo Pratama Dahnial Syauqy Danudoro, Kevin Daris Muhammad Yafi Desy Marinda Oktavia Sitinjak Dewi Amalia Dharmatirta, Brian Aditya Dimas Rizqi Firmansyah Dony Satrio Wibowo Duwi Purnama Sidik Dzakwan Daffa Ramdhana Eko Sakti Pramukantoro, Eko Sakti Eko Setiawan Eko Setiawan Enny Trisnawati, Enny Ervin Yohannes Ester Nadya Fiorentina Lumban Gaol Faris Chandra Febrianto Farrassy, Muhtady Fatwa Ramdani, Fatwa Fernando, Leo Luis Figo Ramadhan Hendri Fikri, Aqil Dzakwanul Fitra Abdurrachman Bachtiar Fitrahadi Surya Dharma Fitria Indriani Fitriyah, Hurriyatul Fitriyani, Rahma Nur Gabe Siringoringo Gagana Ghifary Ilham Gembong Edhi Setyawan Guruh Adi Purnomo Haikal, M. Fikri Hassadiqin, Hasbi Hendry Y. Nanlohy Herman Tolle Hernanda Agung Saputra Hilman Syihan Ghifari Hilmy Bahy Hakim Hisdianton, Oktavian Huda Ahmad Hidayatullah Hurmuzi, Abdan Idza Hurriyatul Fitriyah Ichsan Ali Rachimi Ida Yusnilawati Ikhsan Rahmad Ilham Imam Cholissodin Imam Faris Intan Fatmawati Irnayanti Dwi Kusuma Irsal, Riyandi Banovbi Putera Issa Arwani Jawahir, Asma Kamilah Nur Joan Chandra Kustijono Juniman Arief Kabisat, Aldiansyah Satrio Kelvin Himawan Eka Maulana Kezia Amelia Putri Kirana Sekar Ayu Kohichi Ogata, Kohichi Krisna Pinasthika Lailil Muflikhah Laksono Trisnantoro Laksono, Blessius Sheldo Putra Larasati, Anindya Zulva Leina Alimi Zain Lilo Nofrizal Akbar Linda Silvya Putri Lita Nur Fitriani LUTHFATUN NISA M. Ali Fauzi M. Fiqhi Hidayatulah M.Shochibul Burhan Marianingsih, susi Marsha Nur Shafira Masyita Lionirahmada Maulana Yusuf Meidiana Adinda Prasanty Mela Tri Audina Misran Misran Mochammad Bustanul Ilmi Mochammad Hannats Hanafi Ichsan Mohammad Andy Purwanto Mohammad Isya Alfian Mohammad Sezar Nusti Ilhami Muchlas Muchlas Mufita, Aulia Riza Muhadzdzib, Naufal Muhamad Fauzan Alfiandi Muhammad Amin Nurdin Muhammad Arga Farrel Arkaan Muhammad Fadhel Haidar Muhammad Hafid Khoirul Muhammad Ibrahim Kumail Muhammad Nazrenda Ramadhan Muhammad Rafi Zaman Muhammad Raihan Wardana Budiarto Muhammad Rizky Rais Muhammad Tri Buwana Zulfikar Ardi Muhammad Wafi Muzammilatul Jamiilah Nico Dian Nugraha Niko Aji Nugroho Noza Trisnasari Alqoria Nugraheny Wahyu Try Nyoman Kresna Aditya Wiraatmaja Olivia Rumiris Sitanggang Onky Soerya Nugroho Utomo Paulus Ojak Parasian Permana, Frihandhika Pratama, Aimar Abimayu Pratama, Wildan Bagus Priyanpadma, Sulthon Purboningrum, Fadhila Putera, Muhammad Reza Dahri Putra Pandu Adikara Putra, Firnanda Al Islama Achyunda Putra, Reza Qonita Luthfiyani Qurrotul A'yun Rachmad Jibril Al Kautsar Rahma Tiara Puteri Rahmatul Bijak Nur Kholis Rahmawati, Athirah Naura Rakhmadina Noviyanti, Rakhmadina Ramadhani, Roihaan Randy Cahya Wihandika Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renaldi Primaswara Praetya Renita Leluxy Sofiana Rhaka Gemilang Sentosa Ringga Aulia Primahayu Riyandi Banovbi Putera Irsal Rizal Maulana Rizal Maulana, Rizal Rizdania, Rizdania Rizka Husnun Zakiyyah Rizky Haris Risaldi Rizky Teguh Nursetyawan Rizky Yuztiawan, Fachrie Robbani, Ihwanudien Hasan Rochmawanti, Ovy Samuel Andika Sasongko, Listyawan Dwi Shaleh, Achmad Rizqi Ilham Shih, Timothy K. Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siswanti Slamet Arifmawan Sri Mayena Surga, Itsar Irsyada Syahrul Yoga Pradana Syaifuddin, Tio Tiara Sri Mulati Tibyani Tibyani Tibyani Tobias Sion Julian Tsani, Farid Nafis Versa Christian Wijaya Vikorian, Eldad Virza Audy Ervanda Wahyu Adi Prijono Wayan Firdaus Mahmudy Widasari, Edita Rosana Wijaya Kurniawan Wijaya, Waskitha William Hutamaputra Willy Andika Putra Wisik Dewa Maulana Yazid Basthomi Yoke Kusuma Arbawa Yongki Pratama Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Zamaliq Zamaliq Zhuliand Rachman Zulfina Kharisma Frimananda