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All Journal International Journal of Electrical and Computer Engineering Sainteks Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Semesta Teknika Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Information System Justek : Jurnal Sains Dan Teknologi Jurnal ULTIMA InfoSys Jurnal Teknologi Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi JIKA (Jurnal Informatika) Journal of Telecommunication, Electronics and Control Engineering (JTECE) Insearch: Information System Research Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Journal of Informatics and Information Security Prosiding SeNTIK STI&K RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Bayangkari Karno, Adhitio Satyo
Universitas Gunadarma
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Insearch: Information System Research Journal

 1 
Brain Tumor Classification Using Four Versions of EfficientNet Widi Hastomo; Adhitio Satyo Bayangkari Karno; Dody Arif; Indra Sari Kusuma Wardhana; Nada Kamilia; Rudy Yulianto; Aji Digdoyo; Tri Surawan
Insearch: Information System Research Journal Vol 3, No 01 (2023): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v3i01.5810

Abstract

Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.
Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Kusuma Wardhana, Indra Sari; Sutarno, Sutarno; Arif, Dodi
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
Co-Authors Agita Tunjungsari Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Aji Digdoyo Arif, Dody Aryo Nur Utomo Azis, Nur Bakti, Indra Deon Strydom Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dodi Arif Dody Arif Dyah Ruri Irawati Eka Sally Moreta Eka Sally Moreta Elliya Sestri Eva Karla, Eva Fahrul Razi Faqihudin Faqihudin Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Masriyanda, Masriyanda Meika Syahbana Rusli Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Nurdina Widanti Rachmat Nursalam Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rudy Yulianto Rudy Yulianto Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Stevianus Stevianus Sudarto Usuli Sudjiran sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Tri Surawan Tri Surawan Vany Terisia Widi Hastomo Yasin Effendi Yayat Sujatna, Yayat Yuningsih, Nia YUSUF, DIANA