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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengembangan Riset dan Observasi Teknik Informatika KOMPUTEK Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sains dan Teknologi Asian Journal of Community Services (AJCS) INOVTEK Polbeng - Seri Informatika
Anief Fauzan Rozi, Anief Fauzan
Universitas Mercu Buana Yogyakarta
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : Jurnal Teknologi Dan Sistem Informasi Bisnis

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Sistem Rekomendasi Produk Somethinc Menggunakan Metode Content-based Filtering Azizah, Nailatul; Rozi, Anief Fauzan
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 3 (2024): Juli 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i3.1411

Abstract

In choosing skin care products, many consumers often make mistakes due to a lack of understanding of skin type and a lack of knowledge about skin care products available on the market. This often makes it difficult for them to find suitable products. This research aims to design an application that is able to provide skincare recommendations based on previous product preferences. The method used is Content-based filtering. The recommendation process is carried out by comparing product content to produce the highest to lowest ranking, as well as calculating the minimum support and confidence values ​​to determine association rules for itemset combinations. To calculate the similarity between words using the cosine similarity algorithm, product descriptions will be given a value using TF-IDF (Term Frequency-Inverse Document Frequency) calculations. After that, the similarity weight will be calculated using the cosine similarity algorithm, the similarity weights from highest to lowest. In this research, the product with the highest similarity value was obtained with a value of 0.722.
Analisis Arsitektur Convolutional Neural Network Untuk Klasifikasi Citra Bunga Munandar, Annisa Nurfitri Rida; Rozi, Anief Fauzan
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 3 (2024): Juli 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i3.1413

Abstract

Characteristic recognition of biological sciences is increasingly popular in scientific research, especially by utilizing computation.Indonesia is one of the countries with the largest biodiversity in the world, currently, Indonesia has identified and named 19,232 species of flowering plants.Flower image classification has attracted the interest of many researchers to investigate new methods.One of the widely used methods is deep learning and neural network methods.Convolutional Neural Network (CNN) is one of the most commonly used deep learning methods in image processing. This study aims to analyze the performance of flower image classification using VGG16 and NasNetMobile architecture with fine tune and without fine tune.The NasNetMobile architecture model with fine tune achieved the best accuracy of 99.15%, while the NasNetMobile architecture model without fine tune achieved the lowest accuracy of 97.45%.
Co-Authors Athallah, Muhamad Reza Audita Nuvriasari Azizah, Nailatul Burrhanuddin, Nafi' Ikhsan Febriyanti, Natasya Fitri, Ichlasia Ainul Heru Prayitno Manik, Tessa Monika Manurung, Vanni Munandar, Annisa Nurfitri Rida Saputra, Irfan Juliardi Titik Desi Harsoyo, Titik Desi Turnip, Erdina Wibowo, Rizki Widodo, Arief Setyo