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All Journal Jurnal Transformatika Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal DIMASTIK
Indra Abdam Muwakhid, Indra Abdam
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Journal : Jurnal Transformatika

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Comparasion Support Vector Machine And K-Nearest Neighbor for Classification fertile And Infertile Eggs Based on GLCM Texture Analysis Nurdiyah, Dewi; Muwakhid, Indra Abdam
Jurnal Tr@nsForMat!ka Vol 13, No 2 (2016)
Publisher : Jurusan Teknologi Informasi Universitas Semarang

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Abstract

Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility test usually use egg candling. The purpose of observation is to choose eggs fertile  (eggs contained embryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered into the incubator for hatching eggs and infertile can be egg consumption. However, there are obstacles in the process of sorting the eggs are less time efficient and inaccuracies of human vision to distinguish between fertile and infertile eggs. To overcome this problem, it can be used  Computer Vision technology is having such a principle of human vision. It used to identify an object based on certain characteristics, so that the object can be classified. The aim of this study to comparasion classify image fertile and infertile eggs with SVM (Support Vector Machine) algorithm and K-Nearest Neighbor Algorithm based on input from bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-ocurance Matrix).  Eggs image  studied are 6 day old eggs. It is expected that the proposed method is an appropriate method for classification image fertile and infertile eggs.
Statistical Feature Extraction Based on Wavelet Transform for Arrhythmia Detection Muwakhid, Indra Abdam; Indra Abdam Muwakhid
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12339

Abstract

Early detection of arrhythmia through electrocardiogram (ECG) signals is crucial for preventing severe cardiac conditions. This study proposes a binary classification approach using statistical features derived from wavelet-transformed ECG signals. The MIT-BIH Arrhythmia Database was used, with signals filtered using a 0.5–50 Hz Butterworth bandpass filter. Signals were segmented into 360-sample windows with 100-sample overlap and labeled based on the majority annotation within each window. Wavelet transformation using Symlet 8 at level 4 was applied, followed by the extraction of eight statistical features: mean, standard deviation, variance, skewness, kurtosis, interquartile range (IQR), root mean square (RMS), and zero crossing rate (ZCR). These features were classified using MLP, KNN, and SVM models. MLP and KNN achieved the highest accuracy of 92.46%, while SVM had lower accuracy (72.99%) but high recall (94.21%). The results demonstrate the effectiveness of wavelet-based statistical features for lightweight and accurate arrhythmia detection.
Co-Authors Ambar Dwi Erawati Dewi Nurdiyah Dewi Nurdiyah, Dewi Hargianti Dini Iswandari Harsono Harsono Indra Abdam Muwakhid Okti Tri Hastuti Okti Trihastuti Dyah Retnaningrum Rinayati Rinayati Sugiharto, Sigit