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All Journal Jurnal Transformatika InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Ilmiah Sinus
Agustiyar Agustiyar
LLDIKTI Wilayah VI Jawa Tengah
Computer Science & IT Electrical & Electronics Engineering Engineering

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Metode Pembobotan Jarak dengan Koefisien Variasi untuk Mengatasi Kelemahan Euclidean Distance pada Algoritma k-Nearest Neighbor Agustiyar Agustiyar; Romi Satria Wahono; Catur Supriyanto
Jurnal Ilmiah SINUS Vol 20, No 1 (2022): Vol. 20 No. 1, Januari 2022
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v20i1.565

Abstract

k-Nearest Neighbor (k-NN) is one of the classification algorithms which becomes top 10 in data mining. k-NN is simple and easy to apply. However, the classification results are greatly influenced by the scale of the data input. All of its attributes are considered equally important by Euclidean distance, but inappropriate with the relevance of each attribute. Thus, it makes classification results decreased. Some of the attributes are more or less relevance or, in fact, irrelevant in determining the classification results. To overcome the disadvantage of k-NN, Zolghadri, Parvinnia, and John proposed Weighted Distance Nearest Neighbor (WDNN) having the performance better than k-NN. However, when the result is k >1, the performance decrease. Gou proposed Dual Distance Weighted Voting k-Nearest Neighbor (DWKNN) having the performance better than k-NN. However, DWKNN focused in determining label of classification result by weighted voting. It applied Euclidean distance without attribute weighting. This might cause all attribute considered equally important by Euclidean distance, but inappropriate with the relevance of each attribute, which make classification results decreased. This research proposed Coefficient of Variation Weighting k-Nearest Neighbor (CVWKNN) integrating with MinMax normalization and weighted Euclidean distance. Seven public datasets from UCI Machine Learning Repository were used in this research. The results of Friedman test and Nemenyi post hoc test for accuracy showed CVWKNN had better performance and significantly different compared to k-NN algorithm. 
Prediksi Penyakit Jantung Menggunakan Attribute Weighting k-Nearest Neighbor Agustiyar Agustiyar
InComTech : Jurnal Telekomunikasi dan Komputer Vol 13, No 2 (2023)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v13i2.17883

Abstract

Penyakit kardiovaskular atau lebih dikenal dengan penyakit jantung menjadi salah satu penyebab kematian tertinggi di Indonesia dan di tingkat global. Selain pola hidup sehat untuk mencegah penyakit tersebut, deteksi dini terhadap resiko penyakit jantung dapat dilakukan dengan data mining atau machine learning salah satunya k-NN. k-NN adalah salah satu metode data mining paling sederhana dan kuat dalam konsistensi hasil klasifikasi, akan tetapi memiliki kekurangan yaitu memberikan bobot yang sama kepada semua atribut. Penelitian ini mengusulkan pembobotan pada atribut untuk mengatasi kelemahan tersebut. Prediksi penyakit jantung digunakan untuk menggambarkan kinerja metode usulan. Pada penelitian ini menggunakan dataset Heart Disease, sebuah dataset publik dari University of California Irvine. Dengan menggunakan nilai k 3, 5, 7, 9 diperoleh rata-rata kinerja metode usulan sebesar 79,87% lebih baik dibandingkan Chi-Square k-NN 79,08% dan k-NN klasik 65,89%. Penelitian ini menyimpulkan bahwa metode pembobotan atribut berhasil mengatasi kekurangan k-NN, jadi metode usulan cocok untuk prediksi penyakit jantung.
GLOBAL THRESHOLDING IMPLEMENTATION FOR NOISE HANDLING IN DIGITAL IMAGE RECOGNITION Purwanto, Dannu; Agustiyar, Agustiyar
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8713

Abstract

Text recognition (OCR - Optical Character Recognition) is a research field that is gaining widespread attention due to its wide application in image and document processing. Although OCR technology has achieved a high level of success, the main challenge faced is the presence of noise in text image, noise causes decreased text recognition results, noise causes miss classification. Therefore needed noise handling text recognition.  The aim of this research is to provide valuable insight into the techniques and approaches used in the context of noise treatment using global threshold methods. The method used starts from an input digital image, then preprocessing is carried out by converting the image into a gray scale image, then a threshold is applied to the image, then recognition is carried out. From 6 experiments, the best results were obtained for character recognition with a threshold value (t) of 65 and a character recognition accuracy percentage of 94.29%. T value determined manually and static for separates the all object and the background, while in reality the lighting or contrast always varies. Suggestions for further research include developing an adaptive thresholding method approach to obtain threshold values automatically and optimally. So that if faced with varying lighting conditions or contrast, better results can be obtained.
Co-Authors Catur Supriyanto Dannu Purwanto Romi Satria Wahono