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All Journal Jurnal INFOTEL Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Journal of Engineering and Applied Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi IJCOSIN : Indonesian Journal of Community Service and Innovation

Condro Kartiko

Institut Teknologi Telkom Purwokerto

Author-ID : 2462913

Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Identifikasi Spesies Reptil Menggunakan Convolutional Neural Network (CNN) Olvy Diaz Annesa; Condro Kartiko; Agi Prasetiadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Reptiles are one of the most common fauna in the territory of Indonesia. quite a lot of people who have an interest in knowing more about this fauna in order to increase knowledge. Based on previous research, Deep Learning is needed in particular the CNN method for computer programs to identify reptile species through images. This reseacrh aims to determine the right model in producing high accuracy in the identification of reptile species. Thousands of images are generated through data augmentation processes for manually captured images. Using the Python programming language and Dropout technique, an accuracy of 93% was obtained by this research in identifying 14 different types of reptiles.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Condro Kartiko; Apri Junaidi; Tri Ginanjar Laksana; Novanda Alim Setya Nugraha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

Co-Authors Afandi Nur Aziz Thohari Agi Prasetiadi Apri Junaidi Ariq Cahya Wardhana Dicky Dewantoro Faisal Dharma Adhinata Fauzan Romadlon Ferani, Nia Annisa Galuh Boy Hertantyo Hesa Abda Arrasyid laksana, tri ginanjar Nugraha, Novanda Alim Setya Olvy Diaz Annesa Ramadhani, Rima Dias Tenia Wahyuningrum Teotino Gomes Soares Wahyu Andi Saputra

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)