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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Syifa Nur Fauziah
Universitas AMIKOM Yogyakarta
Computer Science & IT

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Algoritma Adaptive Neuro Fuzzy Inference System Untuk Perkiraan Intensitas Curah Hujan Ma’ruf Aziz Muzani; M. Iqbal Abdullah Sukri; Syifa Nur Fauziah; Windha Mega Pradnya; Andi Suyonto
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.777 KB)

Abstract

Rainfall is the amount of rain that pours or falls within a certain period of time in an area. Rainfall information is useful in many areas. Therefore, fast, accurate and detailed information is indispensable. The method used to predict rainfall is the Adaptive Neuro Fuzzy Inference System (ANFIS) by utilizing daily rainfall data. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a combination of artificial neural network and fuzzy logic. In the learning process, Adaptive Neuro Fuzzy Inference System (ANFIS) method is used LSE Recursive algorithm for advanced learning. The research phase starts from rainfall data collection, learning data, functional and non-functional analysis, ERD, Adaptive Neuro Fuzzy Inference System (ANFIS) method, and Root Means Squared Error (RMSE) calculation and the program is created using PHP and MYSQL as database storage. In this study, two input variables used in the form of rainfall data one day before and rainfall data two days earlier, obtained root means square error (RMSE) results of 17.7 in 1200 training data and 9.4 in 200 test data.
Data Mining Untuk Klasifikasi Produk Menggunakan Algoritma K-Nearest Neighbor Pada Toko Online Ma’ruf Aziz Muzani; M. Iqbal Abdullah Sukri2; Syifa Nur Fauziah; Agus Fatkhurohman; Dhani Ariatmanto
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.365 KB)

Abstract

The rapid growth of e-commerce in Indonesia has been largely facilitated by the presence of e-marketplaces. The e-marketplace trend in Indonesia continues to develop along with the development of technology and the internet. During its development, e-marketplaces offer more and more products. As a result, buyers need more effort to find the product they want. In order to facilitate the search for these products, a product classification is carried out. This study classifies products in the Shopee emarketplace using the K-Nearest Neighbor algorithm. The product data used comes from web scraping in the categories of cellphones and accessories, Muslim fashion, and home appliances. The stages of the classification system begin with the preprocessing stage, then the term weighting stage uses the TF-IDF method, then cosine similarity to calculate the similarity distance between documents, and then sorting the results of the cosine similarity to retrieve data for the number of k values. Based on testing on 9 product data with three different k values. Obtained an average that shows the lowest accuracy, precision, and recall results when the value of k = 3. The accuracy result is 88.89%, precision is 83.33%, and a recall of 100% is obtained when using the value of k = 5 or k = 7.
Co-Authors Agus Fatkhurohman Andi Suyonto Dhani Ariatmanto M. Iqbal Abdullah Sukri M. Iqbal Abdullah Sukri2 Ma’ruf Aziz Muzani Windha Mega Pradnya