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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics INFOKAM Sisforma: Journal of Information Systems CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming SOSCIED Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Business and Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Seminar Nasional Ilmu Terapan Jurnal Kabar Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Future Artificial Intelligence and Technologies Proceeding of The International Conference on Mathematical Sciences, Natural Sciences, and Computing
Dian Kristiawan Nugroho
Universitas Stikubank
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : CESS (Journal of Computer Engineering, System and Science)

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Usability of Brain Tumor Detection Using the DNN (Deep Neural Network) Method Based on Medical Image on DICOM Niken Puspitasari; Kristiawan Nugroho; Kristhoporus Hadiono
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.48727

Abstract

Deteksi tumor otak merupakan bidang penelitian yang menarik untuk diteliti. Perkembangan teknologi informasi menghasilkan berbagai metode yang dipergunakan antara lain menggunakan CT (Computed Tomography) scan atau dikenal dengan teknologi CT scan. CT Scan mempunyai berbagai macam keunggulan dalam mendeteksi tumor otak antara lain pada sisi kecepatan, kemampuan memvisualisasikan citra 3 dimensi dan kemampuan membedakan antar jaringan yang berbeda. Keunggulan CT Scan tersebut membuat para peneliti tertarik untuk mengembangkan berbagai jenis metode yang dipergunakan untuk menganalisis dan memprediksikan hasil CT scan tersebut. Salah satu metode yang dipergunakan adalah menggunakan pendekatan Machine Learning (ML). ML dapat digunakan untuk deteksi tumor otak dengan CT scan. Prosesnya melibatkan penggunaan algoritma ML untuk mengidentifikasi pola-pola yang terdapat pada gambar CT scan pasien dengan tumor otak. Dalam hal ini, CT scan pasien dengan tumor otak digunakan sebagai dataset pelatihan untuk membangun model ML. Namun penggunaan Machine Learning juga memiliki keterbatasan dalam hal kurang handal nya Model dan kesulitan hasil deteksi yang diinterpretasikan dokter. Metode ML akan mengalami ketidakakuratan prediksi dengan model training data yang semakin besar sehingga membutuhkan metode lain yang bisa menghasilkan tingkat akurasi yang tinggi. Deep Learning (DL) merupakan fenomena baru pada dunia teknologi informasi dan telah berhasil diimplementasikan pada berbagai macam bidang penelitian. DL memberikan tingkat akurasi yang semakin tinggi jika didukung data yang semakin besar. Penelitian ini mengaplikasikan salah satu metode DL yaitu Deep Neural Network (DNN) untuk memprediksi tumor otak dari hasil CT Scan yang akan disimpan pada cloud server sehingga bisa diakses kapanpun dan dimanapun juga sepanjang tersedia teknologi Internet. Hasil penelitian ini akan bermanfaat bagi para tenaga medis dalam memprediksi tumor otak dengan lebih akurat berdasarkan gambar citra dari CT scan.Detection of brain tumors is an interesting field of research to study. The development of information technology has resulted in various methods being used, including using a CT (Computed Tomography) scan or known as CT Scan technology. CT Scan has various advantages in detecting brain tumors, including in terms of speed, the ability to visualize 3-dimensional images and the ability to distinguish between different tissues. The superiority of the CT Scan makes researchers interested in developing various types of methods used to analyze and predict the results of the CT Scan. One of the methods used is the Machine Learning (ML) approach. ML can be used to detect brain tumors with CT scans. The process involves using ML algorithms to identify patterns present in the CT scan images of patients with brain tumors. In this case, CT scans of patients with brain tumors are used as a training dataset to construct the ML model. However, the use of Machine Learning also has limitations in terms of the lack of reliability of the model and the difficulty of interpreting the results of detection by doctors. The ML method will experience prediction inaccuracies with the larger training data model, requiring other methods that can produce a high level of accuracy. Deep Learning (DL) is a new phenomenon in the world of information technology and has been successfully implemented in various research fields. DL provides a higher level of accuracy if it is supported by larger data. This study applies one of the DL methods, namely Deep Neural Network (DNN) to predict brain tumors from CT Scan results which will be stored on a cloud server so that they can be accessed anytime and anywhere as long as Internet technology is available. The results of this study will be useful for medical personnel in predicting brain tumors more accurately based on images from CT scans.
Co-Authors Achmad Nuruddin Safriandono Afandi , Afandi Afif, Randi Ahmad Fathoni Ajib Susanto Ajie, Ach. Ridlo Bayu Alex Chandra Iswanto Alfiqhyanto, Damas Aminudin, Agus Anjis Sapto Nugroho Anton Sujarwo Anton Sujarwo Aprico, Fikky Apriyanti, Dewi Aquinia, Ajeng Araaf, Mamet Adil Arsyad , Muhammad Rafi Haidar budi hartono Budiarto, Indri Cahaya, Agus Indra De Rosal Ignatius Moses Setiadi Dhendra Marutho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Ariyanto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur fakhri Farooq, Omar Fitrianto, Lindu Hari Murti Hermawan, Taufan Hidayat, Suluh Irawan, Sandy Islam, Hussain Md Mehedul Isworo Nugroho Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristiyono, Budi Kristophorus Hadiono Lie Liana Lie Liana . Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik Mulyani , Wahyu Sri Mulyo Budi Setiawan Munna, Aliyatul Muslikh, Ahmad Rofiqul Niken Puspitasari Nurmakhlufi, Alfin Ojugo, Arnold Adimabua Omar Farooq Palupi, Dian Perdana, Willy Yudha Prabowo, Ardian Adi Prihatin, Rudi Setyo Rachman, Rahadian Kristiyanto Raden Mohamad Herdian Bhakti Radyanto, Mohammad Riza Rahadiyanto, Cahyono Raharjo, Fajar Retnowati Rokhayadi, Wakhid Ruslana, Zauyik Nana Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Sunardi Sunardi Suprapto, Yossy SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto - Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony