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All Journal IPTEK The Journal for Technology and Science TELKOMNIKA (Telecommunication Computing Electronics and Control) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ABSYARA: Jurnal Pengabdian Pada Masyarakat Bulletin of Computer Science Research JUSTIN (Jurnal Sistem dan Teknologi Informasi) eProceedings of Engineering
Desepta Isna Ulumi
Universitas Tanjungpura, Pontianak, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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Journal : IPTEK The Journal for Technology and Science

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Weighted k Nearest Neighbor Using Grey Relational Analysis To Solve Missing Value Desepta Isna Ulumi; Daniel Siahaan
IPTEK The Journal for Technology and Science Vol 29, No 3 (2018)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.699 KB) | DOI: 10.12962/j20882033.v29i3.5011

Abstract

Software defect prediction model is an important role in detecting the most vulnerable component error software. Some research have been worked to improve the accuracy of the prediction defects of the software in order to manage human, costs and time. But previous research used specific dataset for software defect prediction model. However, there is no a generic dataset handling for software defect prediction model yet. This research proposed improvements to the results of the software defect prediction on the merged dataset, which is called generic dataset, with a number of different features. In order to balance the number of features, each dataset should be filled with a missing value. To fill the missing values, Weighted k Nearest Neighbor (WkNN) method was used. Then, after missing values were filled, Naïve Bayes was used to classify the selected features. This research needed to obtain a set of features which was relevant, then performed a feature selection method. The results showed that by using seven NASA public MDP datasets, Naïve Bayes with Information Gain (IG) or Symmetric Uncertainty (SU) feature selection presented the best balance value.Software defect, NASA public MDP, weighted KNN,Naive Bayes
Co-Authors Agus Zainal Arifin Aminul Wahib Anggi Perwitasari Azalia, Clara Oxana Daniel Oranova Siahaan Eko Darwiyanto Hadiasmah, Anaqah Aisyah Hafiz Muhardi Haried Novriando Helen Sastypratiwi Herry Sujaini Moch Zawaruddin Abdullah Montass, Requestha Amorine Putri, Galuh Kusuma Rizka Wakhidatus Sholikah Rosyadi, Ahmad Wahyu Yanuar Firdaus Yulianti Yulianti