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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sains dan Teknologi
Wulaning Ayu, Putu Desiana
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Computer Science & IT Education Engineering

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Weighted nearest neighbors and radius oversampling for imbalanced data classification Pradipta, Gede Angga; Wulaning Ayu, Putu Desiana; Liandana, Made; Hostiadi, Dandy Pramana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp416-427

Abstract

The challenges associated with high-dimensional and imbalanced datasets were observed to often lead to a degradation in the performance of classical machine learning algorithms. In the case of high dimensional data, not all features contribute significantly and are considered relevant to the performance of the model. Therefore, this study introduced a novel method called feature weighted variance analysis-nearest neighbors (WFVANN) which was developed on the foundation of k-nearest neighbors (KNN). The process involved modifying the calculation of the Euclidean distance by fully considering the relevance and contribution levels of features based on their Fvalue. WFVANN at the algorithmic level processing and radius-synthetic minority oversampling technique (R-SMOTE) at the data level processing used as the oversampling method later became the proposed model to solve the aforementioned issues. Moreover, extensive experiments were conducted on two distinct types of data including the high-dimensional and imbalanced by comparing WFVANN with the state-of-art KNN-based and synthetic minority oversampling technique (SMOTE)-based methods. The results showed that the proposed method had the highest accuracy, precision, recall, and F1-measure values across the majority of test datasets and outperformed the other methods.
PERBANDINGAN SEGMENTASI CITRA TELUR AYAM MENGGUNAKAN METODE OTSU BERDASARKAN PERBEDAAN RUANG WARNA RGB DAN HSV Pradipta, Gede Angga; Wulaning Ayu, Putu Desiana
JST (Jurnal Sains dan Teknologi) Vol. 6 No. 1 (2017)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1657.251 KB) | DOI: 10.23887/jstundiksha.v6i1.9329

Abstract

Segmentasi bertujuan untuk membagi citra ke dalam sejumlah bagian atau objek, objek pada penelitian ini berupa telur ayam yang bertujuan untuk memperoleh garis tepi dari citra telur dan dapat mengidentifikasi bercak kotor pada telur. Untuk mendapatkan tujuan dari penelitian ini, maka metode yang digunakan adalah dengan segmentasi berdasarkan perbedaan ruang warna, yaitu ruang warna RGB dan HSV, serta segmentasi dengan menggunakan metode otsu. Hasil yang diperoleh dari 20 sampel telur yang digunakan menunjukkan hasil segmentasi dengan ruang warna RGB dalam mendeteksi garis tepi citra pada telur lebih baik dibandingkan dengan ruang warna HSV ,ini terlihat dari pengujian secara visual dimana ruang warna RGB memberikan hasil yang lebih sempurna dalam memisahkan telur dengan objek yang lainnya. Sedangkan dalam pengujian kualitas citra hasil dari segementasi dengan pengujian MSE dan PSNR, ruang warna HSV memberikan hasil yang lebih baik dengan RGB . Penelitian ini diharapkan dapat memberikan  kontribusi penggunaan metode ruang warna agar dapat diterapkan pada segmentasi citra. Kata Kunci : Otsu, RGB, HSV, Segmentasi
Co-Authors Dandy Pramana Hostiadi Gede Angga Pradipta, Gede Angga Made Liandana, Made