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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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Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis Rochmawanti, Ovy; Utaminingrum, Fitri; Bachtiar, Fitra A.
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021844441

Abstract

Tuberkulosis (TB) merupakan salah satu penyakit berbahaya yang dapat menular lewat udara dan sering menyebabkan kematian apabila tidak cepat ditangani. Penyakit TB bisa disembuhkan dengan deteksi dini sehingga penderita dapat segera mendapatkan pengobatan yang tepat. Metode Convolutional Neural Network (CNN) digunakan untuk mendeteksi penyakit TB melalui foto rontgen dada. Penelitian ini bertujuan untuk menentukan model CNN yang mampu menghasilkan performa paling baik dalam mendeteksi penyakit TB. Pengujian dilakukan dengan menggunakan lima pre-trained model yang telah disediakan oleh Keras yaitu ResNet50, DenseNet121, MobileNet, Xception, InceptionV3, dan InceptionResNetV2. Perbedaan ukuran gambar yag digunakan pada saat pelatihan dan pengujian juga akan dianalisis pengaruhnya terhadap nilai akurasi yang dihasilkan dan waktu komputasinya. Hasil pengujian menunjukkan bahwa model DenseNet121 mampu menghasilkan nilai akurasi tertinggi dalam mendeteksi penyakit TB, yaitu 91,57%. Sedangkan model MobileNet merupakan model dengan waktu komputasi tercepat untuk semua ukuran gambar yang diuji. Semakin besar ukuran citra maka semakin tinggi nilai akurasinya, namun di sisi lain waktu komputasi juga akan semakin lama.  Abstract Tuberculosis (TB) is one of the dangerous disease that can be transmitted through the air and often causes death if not treated quickly. This illness can be cured with early detection, so that sufferers can immediately get the right treatment. The Convolutional Neural Network (CNN) method is used to detect TB disease through chest X-rays. This study aims to determine which CNN model is able to produce the best performance in detecting TB disease. Testing was carried out using five pre-trained models provided by Keras namely ResNet50, DenseNet121, MobileNet, Xception, InceptionV3, and InceptionResNetV2. The difference in image size used during training and testing will also be analyzed for its effect on the resulting accuracy value and its computation time. The test results showed that the DenseNet121 model was able to produce the highest accuracy value in detecting TB disease, namely 91.57%. Meanwhile, the MobileNet model is the model with the fastest computation time for all image sizes tested. The bigger the image size, the higher the accuracy value, but on the other hand the computation time will also be longer.
Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu Awalina, Aisyah; Bachtiar, Fitra Abdurrachman; Utaminingrum, Fitri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935696

Abstract

Kemudahan untuk memperoleh informasi saat ini, telah sedikit membantu hidup kita. Seperti mencari ulasan untuk menimbang tempat atau barang yang akan dipilih. Beberapa orang memanfaatkan hal tersebut dengan membuat ulasan palsu untuk kepentingan mereka sendiri. Sehingga deteksi ulasan palsu sangat dibutuhkan. Model Transformer saat ini banyak diterapkan pada pemrosesan bahasa alami karena kinerja yang diperoleh nya sangat baik. Ada dua pendekatan yang dapat dilakukan dalam model Transformer yaitu pre-training dan fine-tuning. Penelitian sebelumnya telah banyak menggunakan fine-tuning dari model Transformer dikarenakan adanya kemudahan dalam pelatihan, waktu yang lebih sedikit, biaya dan kebutuhan lingkungan yang lebih rendah dibanding proses pre-training. Akan tetapi penelitian sebelumnya masih sedikit yang membandingkan model deep learning dengan fine-tuning yang khusus diterapkan pada deteksi ulasan palsu. Penelitian ini melakukan perbandingan model Transformer menggunakan pendekatan fine-tuning dengan metode deep learning yaitu CNN dengan berbagai pretrained word embedding untuk mengatasi deteksi ulasan palsu pada dataset Ott. Model RoBERTa mengungguli model Transformer dan deep learning dimana nilai akurasi 90,8%; precision 90%; recall 91,8% dan f1-score 90,8%. Namun dari segi waktu komputasi model pelatihan, DistilBERT memperoleh waktu komputasi terkecil yaitu dengan nilai 200,5 detik. Meskipun begitu, hasil yang diperoleh model Transformer maupun deep learning memiliki kinerja yang baik untuk deteksi ulasan palsu pada dataset Ott.AbstractThe ease of obtaining information today has helped our lives, like looking for reviews to weigh the place or item to choose. Some people take advantage of this by creating spam reviews for their benefit. So the detection of spam reviews is needed. Transformer models are currently widely applied to natural language processing because they have outstanding performance. Two approaches in the Transformer model is pre-training and fine-tuning. Previous studies have used a lot of fine-tuning due to the ease of training, less time, costs, and lower environmental requirements than the pre-training process. However, a few previous studies compare deep learning models with fine-tuning applied explicitly for detecting spam reviews. This study compares the Transformer model using a fine-tuning approach with a deep learning method, namely CNN, which uses various pre-trained word embedding to overcome the detection of false reviews in the Ott dataset. The result is RoBERTa model outperforms between Transformer and deep learning models, where the accuracy is 90.8%, precision is 90%, recall is 91.8%, and f1-score is 90.8%. Afterward, DistilBERT models obtained the shortest computation time with 200.5 seconds. However, the results obtained by both Transformer and deep learning models perform well to detect spam reviews in the Ott dataset.
Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB Cahyo, Muhammad Pandu Dwi; Utaminingrum, Fitri
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.785

Abstract

The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.
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