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All Journal Journal of Creative Student Research
Raihan Maulana
Universitas Negeri Medan
Humanities Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Transportation

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Journal : Journal of Creative Student Research

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Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Jenis Burung Raihan Maulana; Raisya Dwi Zahra Putri; Sindy Fitriani Margareth Sihaloho; Sri Mulyana
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.2966

Abstract

Birds are a group of vertebrate animals that have feathers and wings. There is a diversity of bird species in the world, that makes it difficult for ordinary people to distinguish certain types of birds, but technological advances now allow for easier identification. This research uses a dataset from Kaggle to classify various bird species in the world. This dataset consists of 84,635 bird images, covering 525 different species. In this study, we focused on 30 classes, with a total of 5,050 data divided into 4,760 training data, and 150 data each for test and validation. Classification was performed using a Convolutional Neural Network (CNN), with the training process yielding the highest accuracy of 96.30% on training data and 81.33% on validation data after 20 epochs.
Sistem Rekomendasi Pekerjaan di bidang IT Menggunakan Algoritma Content-Based Filtering Crismastiana Koloman; Raihan Maulana; Raisya Dwi Zahra Putri; Wahyu Abadi Harahap
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.2992

Abstract

Rapid growth in the Information Technology (IT) industry has created an abundance of career opportunities, but job seekers often face difficulty in finding jobs that match their background and skills. To overcome this challenge, this research presents a “Job Recommendation System” that focuses on the IT industry. The method used in this research is "Content-Based Filtering," which allows the system to recommend jobs based on similarities between the skills possessed by the user and the available job descriptions. The system allows users to enter their skills, and based on these skills, analyzes the description jobs to recommend suitable jobs. Apart from providing job recommendations, this method also helps users to identify skill areas that need improvement. The research results show that the content-based filtering method is a powerful approach for providing relevant and effective job recommendations in the IT industry. This method provides great benefits to job seekers, helping them find job opportunities that suit their background and skills. In addition, this method has the potential to be applied in various applications in various industries.
Co-Authors Crismastiana Koloman Raisya Dwi Zahra Putri Sindy Fitriani Margareth Sihaloho Sri Mulyana Wahyu Abadi Harahap