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Rudi Setiawan
Magister Ilmu Fisika,Universitas Diponegoro
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Journal : Jurnal Teknologi Informasi dan Ilmu Komputer

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Deteksi Malaria Berbasis Segmentasi Warna Citra dan Pembelajaran Mesin Setiawan, Agung W.; Rahman, Yusuf A.; Faisal, Amir; Siburian, Marsudi; Resfita, Nova; Gifari, Muhammad W.; Setiawan, Rudi
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.2021844377

Abstract

Di beberapa daerah di Indonesia, malaria masih merupakan salah satu penyakit endemik dan termasuk ke dalam kategori penyakit menular dengan vektor nyamuk Anopheles. Penurunan jumlah mortalitas penderita malaria ini telah menjadi program Pemerintah Indonesia dan World Health Organization. Salah satu hal penting yang dapat dilakukan adalah menyediakan alat diagnosis malaria yang cepat dan akurat berbantukan komputer. Oleh karena itu, pada studi ini dikembangkan sebuah metode deteksi malaria berbasis segmentasi warna citra yang dikombinasikan dengan metode pencacahan objek citra dan pembelajaran mesin berbasis Convolutional Neural Network. Pada studi ini, segmentasi citra dilakukan dengan menetapkan suatu nilai ambas batas tertentu (thresholding) pada model warna HSV. Nilai ambang batas untuk masing-masing kanal warna ditetapkan sebagai berikut: H = 100-175, S = 100-250, dan V = 60-190. Terdapat tiga skema pembelajaran mesin yang digunakan, yaitu citra asli menggunakan RMSProp optimizer, citra tersegmentasi menggunakan RMSProp dan Adam optimizer. Akurasi pelatihan dan validasi CNN tertinggi diperoleh dengan skema citra tersegmentasi menggunakan RMSProp optimizer, yaitu sebesar 92,77% dan 94,38%. Sementara, deteksi malaria berbasis pencacahan objek memiliki akurasi sebesar 93,78%. Meskipun deteksi malaria berbasis pencacahan objek memiliki akurasi 93,78%, tetapi sumber daya komputasi dan waktu yang diperlukan jauh lebih rendah.AbstractMalaria is still one of the endemic diseases in several regions of Indonesia. Reducing the malaria mortality rate has become a notable programme, not only does the Government of the Republic of Indonesia project it, but also the World Health Organization has a similar plan to tackle this disease. One of the prominent concerns to properly promote this programme is providing a rapid and accurate malaria diagnosis tool by applying the computer-aided diagnostics to minimize human errors. The aim of this study is to develop a colour microscopic image-based malaria detection using object counting and CNN-based machine learning. In this research, the HSV colour model with threshold values of H: 100-175, S: 100-250, and V: 60-190 was used to remove the image background. There are three machine learning schemes implemented in this study, i.e. original image using RMSProp optimizer, segmented image using RMSProp and Adam optimizer. The highest training and validation accuracy of CNN were obtained using a segmented image scheme by the RMSProp optimizer, 0.9277 and 0.9438. On the contrary, object-based malaria detection has an accuracy of 93.78%. Furthermore, there are several considerations to determine the malaria detection method, i.e. accuracy, computational resources, and time. Even though malaria detection using object counting has an accuracy of 93.78%, lower than the accuracy of CNN validation, the computational resources and time required are much lower and faster. Therefore, this detection method is suitable for smartphone-based devices with low-middle end specifications.
Co-Authors Adi Pamungkas Agustiany, Fifin Arifiani Aisyah Rahmawati Alamri, Sazili Mustofa Amani, Risca Andreas N, R Djoko Anggraeni, Ellya ANGGRAENI, RENI Ani Haryani, Ani Aprianti, Penti Arif Setiawan , Andi Aris Wahyu Murdiyanto Aritonang, Sovian Aryani, Wiwik Dyah Asep Ahmad Sopandi Asnani Asnani Aulia Safitri, Aulia Azdy, Rezania Agramanist Azzahra, Haliza Bartolomeus Samho Bartoven Vivit Nurdin Bernard Dehaan, Yeovan Naufal Chairunnisa, Devina Azzahra Cristofer, Fernando Daulay, Okky Freeza Dede Hertina Dedet Erawati Denis Pramudia Putra Dermawijaya, Boyke Iskandarsyah Dewi Utami Dewi, Fitria Dewi, Manda Iska Eddy, Syaiful Elsa Efrina Elvi Yenie, Elvi Enggar Alfianto Erdyvania Apritrycia Etty Puji Lestari Fahruzi, Akhmad Faisal, Amir Fatmawati, Fatmawati Gifari, Muhammad W. Grahita Kusumastuti, Grahita Hadia Sukma, Yoga Hakim Santoso, Abdul Hamada Zein Hari Agus Sujono Hartono, Ahmat Dwi Hayatou Oumarou Heri Sutanto Herninda, Amalia Revy I Ketut Sutapa Ida Zahrina Imam, Asep Indiriani, Dini Irawan, Ghani Rafif Irwansyah, Defi Iskandar Mirza Istiqomah, Hilmi Aulia Janizal Jarwinda, Jarwinda Jayawarsa, A.A. Ketut Juhana, Riyadi Kadir Parewe, Andi Maulidinnawati Abdul Kholida, Putri Kisman, Zainul Laksono, Muharram Budi Latipah, Asslia Johar Madi, Madi Mahdi, Arisul Margianto, Tofik Marlina Marlina Mashud, Mustain Mega Iswari Muhammad Nur Ihsan, Muhammad Nur Muhartini Salim Nova Resfita Nuhan, Hudan Khalish Nur Aida Wassi`atu Sakdiah Nurhasan Nugroho Nurwijayanti Oscar Yasunari Pambudi, Wisnu Setyo Pingki Novita Berliana Pratama, Gerald Untirtha Priyanto, Rachmat Puji Astuti, Nur Rochmah Dyah Purwoko, Agus Putra, Fajri Profesio Putri Kholida Putri, Sherlyana Hardiyandeffi Raditia Fath Kharomatudzaky Rahmadi, Isnaini Rahmahtrisilvia Rahmahtrisilvia Rahman, Fikri Aulia Rahman, Titik Khawa Abdul Rahman, Yusuf A. Ramayanti, Rizka Renaldy, Rexsa Resfita, Nova Rio Febrianto Arifendi, Rio Febrianto Ronny Durrotun Nasihien, Ronny Durrotun Rozak, Abd Rumondang, Rumondang Saadah, Neng Siti Nur Sabar Sabar Safaruddin Sdarmin Sudarmin, Sdarmin Sekar Ambarsari Sujatmiko Putri Sepiah Dwi Cahyani Setiawan, Agung W. Setiawan, Cahya Heru Siagian, Apriansyah Dharmawan Siburian, Marsudi siregar, umaiyu Siti Khumayah Sparingga, Daniel Sri Ernawati Sulistyowati Sulistyowati Sumarmi Supriyadi Supriyadi Supriyanto, Adolf Asih Susilo Susilo Sutikno Sutikno Syahputra, Ahmad Reynaldi Syahrizal Nasution Syaripudin Syaripudin Thahyo Subroto, Thahyo Umar Al Faruq Wahyuningtyas, Amalia Wibowo, Nanang Roni Widodo Widodo, Teguh Heri YOSAN, R BAGUS Yoseptry, Ricky Yulianti, Wayan Rifa Zaenal Arifin Zulmiyetri Zulmiyetri