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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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Sistem Klasifikasi Kesegaran Daging Ikan Gurami berdasarkan Warna dan Gas Amonia menggunakan K-Nearest Neighbor (KNN) berbasis Arduino M. Fiqhi Hidayatulah; Hurriyatul Fitriyah; 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

Gouramy in some areas is one of the mainstay of agriculture. Gouramy meat is one of the sources of food that contains lots of good nutrition for humans. The protein content in gouramy is 19%, more than other fish that are often consumed by humans, such as catfish which has (18.2%) protein content, tilapia which has (16%) protein content, and carp which has ( 16%) protein content. The protein found in fish has more benefits than other animal meat. The characteristics of fresh fish are that it has a smooth texture but is still quite chewy and dense. All parts of the meat seemed to stick tightly to the bone. Gouramy has a weakness after the fish dies, that is, it quickly declines in quality, the speed of decline in fish quality can be influenced by internal factors that depend on the type of fish itself and externally related to human handling. gouramy meat that is not fresh or rotten with fresh gourami meat. So it will be very dangerous if the meat is not fresh or in a rotten state to be consumed by consumers. In this study, a classification system was created that can detect the level of freshness of gouramy meat with the characteristics of quality deterioration found in gouramy meat. With the MQ137 sensor to calculate the NH3 odor parameter and the TCS3200 sensor to calculate the color value of gouramy meat with an output of 3 classes, namely Rotten, Less and Fresh which is displayed on lCD16x2. With the classification results obtained on 180 training data and 30 test data, it produces an accuracy of up to 80% with an average computation time of 8.0667 ms..
Rancang Bangun Sistem Deteksi Tingkat Kemanisan Buah Melon (Sky Rocket) dengan Metode Gray Level Co-Occurrence Matrix (GLCM) dan Decision Tree Kelvin Himawan Eka 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

Melon fruit has a sweet fruit taste, thick flesh, and disease resistance. A good quality melon has the characteristics of the outer skin not breaking or cracking, the flesh is free from internal bruising and browning, and the fruit is free from diseases that affect the general appearance. For diabetics, choosing a sweet melon will certainly cause problems. To determine the level of sweetness of the melon, it is first split to take a sample of the liquid and tested on a refractometer. This is certainly not practical, so innovation is needed to determine the level of sweetness of melons by processing the image with a digital image processing process. This study uses a digital image processing process by distinguishing the sweetness level of melons into 3 classes, namely low sweetness level, medium sweetness level, and high sweetness level. Method of Gray Level Co-occurrence Matrix used in this study to perform feature extraction using 5 features that correlation, Contrast, homogeneity, dissimilarity, and Energy with variations in the distance d= 1,2,3,4 and angle θ= 0°,90°,180°,270°. In the melon sweetness classification method, Decision Tree is used to distinguish low, medium, and high sweetness classes. This study used 435 data sets with 390 training data and 45 test data resulting in an accuracy of 80% at low sweetness level, 73% at medium sweetness level and 80% at high sweetness level. The best average computing time obtained at the low sweetness level is 3.87 seconds.
Sistem Peringatan Deteksi Tangga Turun dan Tangga Naik menggunakan Gray Level Co-occurrence Matrix dan Artificial Neural Network berbasis Nvidia Jetson Nano Tiara Sri Mulati; Fitri Utaminingrum
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

Disability is a term used for someone who is unable to perform certain activities due to limited physiological, psychological, and anatomical conditions. According to the WHO report, about 15% of the world's population has a disability and in 2020 a Bourne researcher noted that the most people with disabilities are blind or blind people as many as 43.3 million people. The large number of people with disabilities, especially people with disabilities, makes the government and scientists try to find tools that aim to meet the needs of people with disabilities in their activities. Several studies have made tools to detect objects using sensors, but they have weaknesses, namely a narrow range, low accuracy and relatively long computation time. In this study, an architectural disturbance detection system was applied, namely going down stairs and climbing stairs with digital images in bright room conditions (101-1000 lux) using the Gray Level Co-occurrence Matrix feature extraction method and the Artificial Neural Network classification method. Tests were conducted to determine the best distance and theta in feature extraction to produce the highest accuracy, the highest accuracy was obtained with a value of 0.9144 at distance = 3 and theta = 90Ëš. For real time test results, the average detection accuracy is 74.88% and the average computation time is 0.2945528 seconds. The integration of the input and output systems of the test results for the detection of floors, stairs going down and stairs going up results in 100% accuracy.
Rancang Bangun Sistem Pengklasifikasi Jenis Sampah Organik dan Sampah Daur Ulang menggunakan Resnet50 Paulus Ojak Parasian; Fitri Utaminingrum
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

Waste is a universal problem. According to World Bank, there would be 3,4 billion tonnes of waste annually in 2050. To reduce waste in Indonesia landfills there are scavengers that sort the recyclable waste from the organic ones manually, but their capacity is limited. To increase the sorting rate there should be a sorting machine right from the source, which is civilian homes, or offices. To create such machine the writer will produce a sorting machine for organic waste and recyclable waste using the Resnet50 method. To train the Resnet50 model the writer use a dataset from Kaggle which consist of 22500 training and testing data. The Resnet50 model will be trained using 20 epochs with learning rate of 0,001 for the first 10 epoch and a learning rate of 0,0001 for the next 10 epochs, which resulted in a model with 99% accuration, 3% loss, 96% accuration validation, and 12% loss validation. The machine will then be tested with different object to camera lengths starting from 16 cm, 18 cm, 20 cm, 22 cm, 24 cm, and 26 cm. The best accuration is gained from the length of 20 cm and 22 cm with 85% accuration and overall average classification time of 1,17 seconds.
Rancang Bangun Sistem Klasifikasi Sampah Anorganik Kantor menggunakan Deep Learning Arsitektur Xception berbasis NVIDIA Jetson Nano Rahmatul Bijak Nur Kholis; Fitri Utaminingrum
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

Ineffective waste management is believed to be the reason of the piling amount of waste in landfill. There should be sorting mechanism from the source of waste before being transported to TPA. Offices are of the areas which produce waste similar to household waste and must carry out waste sorting process. Actually, there have been trash cans provided for different waste type. However, people tend to be ignorant of this and keep on throwing waste not in the right container. Thus, the trash can method is rendered ineffective. This study attempted to create a prototype of trash can that classifies office waste in the form of plastic bottles, cans, and paper. The system then automatically places those waste based on their category container. To perform its job of classifying, the deep learning Xception architecture method is used, applied to the system processor, namely the NVIDIA Jetson Nano. This system produces output, namely the movement of the sorting arm. The sorting arm moves to the left if the classification results are plastic bottles waste, it moves to the right for cans waste, and it stays in the middle for paper waste. For testing the classification of waste objects in the system, the accuracy result obtained 91.67% and the average computation time for classification was 0.06385 second. An integration testing was also carried out on the system which resulted in an accuracy of 97.22%.
Sistem Pemilihan Enam Menu Display pada Klinik menggunakan Pergerakan Kepala berbasis Nvidia Jetson Nano Andika Kalvin Simarmata; Fitri Utaminingrum
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 the National Socio-Economic Survey (Susenas) in 2018, there were 30.38 million people with disabilities/14.2 percent of the total population in Indonesia. Persons with disabilities have difficulty operating electronic devices. On this basis, persons with disabilities need tools to operate these devices without physical contact. In addition, physical contact must also be minimized globally due to the COVID-19 pandemic which has spread throughout the world. The solution to this problem is to create a system that can operate electronic devices without making physical contact. This solution can be realized by using Facial Landmark-based head movements to select the display menu at the clinic with the application name "Smart Clinic". The clinic is the main focus because it is the most vulnerable place to the spread of COVID-19. This system works by classifying head movements to the right, left, up, and down. The output of this system is in the form of six menu options. The results obtained from the research accuracy of 90% for the distance of the user as far as 60 cm from the camera, 85% distance of the user as far as 75 cm and 82% distance of the user as far as 90 cm. The distance is selected based on the user's safe viewing distance with the monitor screen, which is in the range of 50-100 cm. This system also has a relatively fast computation time of 0.0034745 s.
Rancang Bangun Sistem Deteksi Gerakan Mata untuk Pemilihan Enam Menu Display menggunakan Circular Hough Transform berdasarkan Facial Landmark berbasis NVIDIA Jetson Nano Imam Faris; Fitri Utaminingrum
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

Person with hand disabilities who often have difficulties in carrying out daily activities as normal human. One of the cases for people with hand disabilities is difficulty when want to turn on electronic devices such as televisions, lights and other devices so required the help of nurses. The solution offered is to create a tool for people with disabilities to select six screen display menus using the circular hough transform method in making menu selections without have to direct contact with the device. This system works by detecting the movement of circles on the iris of the eye, namely movement to the oblique left, up, oblique right, left, down, and movement to the right. The output of the system is that the user can choose six smart assistant menu displays and voice output. The center point of the iris will be processed by computer to be used as a cursor that can execute commands from the user in making menu selections, and blinking for 0-3 seconds will command the menu to be activated. The average value of the accuracy level of the whole system on the distance variation 30, 40 and 50 cm eye movement detection system is 85.18% with a computation time of 0.1816 seconds. The system can be used to help people with disabilities and lighten the work of nurses in understanding the activities required by people with hand disabilities.
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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Abstract

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.
Rancang Bangun Sistem Deteksi Pergerakan Kepala pada Pemilihan Enam Menu Makanan menggunakan Metode Optical Flow berbasis NVIDIA Jetson Nano Huda Ahmad Hidayatullah; 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

Covid-19, known as the corona virus, is a zoonotic disease that is transmitted from animals to humans through viruses in the form of droplets and aerosols. And in 2020, the director general of the World Health Organization (WHO) Tedros Adhanom Ghebreyesus has announced that COVID-19 has become the official name of the corona virus that originated in Wuhan, China (WHO, 2020). And also announced that the risk of transmission through air and touch can be much higher in places that are bad at regulating health rules and protocols, such as restaurants, canteens, and food courts in malls and so on. Where many people spend a long period of time. Therefore, this study aims to help efforts to break the chain of the spread of the Covid-19 virus by implementing a minimal touch or touchless device system in public facilities that require direct contact with devices such as electronic keyboard-mouse, remote control or also in choosing a device. menu display with LCD screen. In this study, a head movement detection system was applied to the selection of six food menus using the Nvidia Jetson Nano- based optical flow method. The study was conducted to determine the effectiveness of the average accuracy in the study and it was obtained that 85.7% at a combination of distances at 45 cm, 55 cm, and 65 cm with an average computation time of 0.040861 s in the combination Head movement turned to the right, to the left, up and down..
Sistem Klasifikasi Sampah Perkantoran menggunakan Metode Faster Region Based Convolutional Neural Network berbasis NVIDIA Jetson Nano Daris Muhammad Yafi; 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

According to law number 18 of 2008 concerning waste management, which is about the remnants of human activities or also from natural processes contained in solid or semi-solid form either in the form of organic or inorganic substances, which can be decomposed or not and are considered useless and then discharged into the environment. Therefore, the Ministry of Environment and Forestry (KLHK) has announced that the country's total waste production in 2020 will reach 67.8 million tons. This means that as many as 270 million people produce around 185,753 tons of waste every day. Based on the sources of waste disposed of in 2021, offices are the four largest waste contributor in Indonesia. The solution given in this research is to create a tool to automatically classify paper, plastic bottles, and cans using the Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm to facilitate waste sorting based on three types of waste. In the testing process, 36 objects were used with the division of 12 objects for the paper class, 12 objects for the plastic bottle class and 12 objects for the can class, and the model used was the result of training the Faster RCNN algorithm with 180,000 number_steps. The test results get an accuracy of 91.6667% for paper class, 91.6667% for plastic bottles, and 83.3333% for cans with a total computation time of 0.7166351s.
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