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All Journal ILKOM Jurnal Ilmiah Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Teguh Adriyanto
Universitas Nusantara PGRI Kediri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Classification of Dog and Cat Images using the CNN Method Teguh Adriyanto; risky aswi ramadhani; Risa Helilintar; Aidina Ristyawan
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1116.203-208

Abstract

Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data  and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of  kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.
Perancangan dan Implementasi Sistem Kendali Lampu Jarak Jauh Berbasis Iot di SDN 2 Mlorah Zen Arfiansyah; Teguh Adriyanto; Aidina Ristyawan
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 7 No. 3 (2023): SEMINAR NASIONAL INOVASI TEKNOLOGI 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v7i3.3577

Abstract

Sistem kendali lampu jarak jauh menggunakan aplikasi WhatsApp telah peneliti kembangkan untuk mengoptimalkan pemanfaatan Internet of Things (IoT) pada lingkungan sekolah. Pengendalian lampu di SDN 2 Mlorah masih menggunakan sistem manual dengan menekan tombol on/off pada saklar lampu, sehingga petugas datang ke sekolah untuk menyalakan/mematikan lampu, hal ini kurang efektif dapat efisien karena dapat menguras tenaga dan waktu. Oleh karena itu, penelitian ini bertujuan untuk merancang sistem kendali lampu di sekolah dengan memanfaatkan aplikasi WhatsApp sebagai pengendalian tombol on/off. Metode penelitian yang digunakan adalah pendekatan agile, yang memungkinkan pengembangan perangkat lunak secara cepat dengan fokus pada kecepatan delivery dan kemampuan untuk beradaptasi dengan perubahan. Dalam konteks ini, model yang diterapkan adalah Extreme Programming (XP), yang terdiri dari empat tahapan yaitu planning, design, coding, dan testing. Hasil penelitian ini menunjukkan bahwa sistem kendali lampu jarak jauh menggunakan aplikasi WhatsApp dapat berhasil diimplementasikan. Dengan pengembangan sistem kendali lampu jarak jauh ini diharapkan dapat memberikan solusi praktis untuk mengendalikan lampu pada sekolah melalui aplikasi WhatsApp.
Co-Authors Aidina Ristyawan Risa Helilintar Risky Aswi R, Risky Zen Arfiansyah