IDENTIFIKASI NILAI ESENSIAL DARI OUTLIER NON-EXTREME MENGGUNAKAN METODE MINIMUM VOLUME ELLIPSOID
Abstract
In many cases, outliers are considered to have a negative effect because they can cause the test to miss significant findings or distort the results in the data. Outliers are often discarded because they are considered an anomaly. Currently, some outliers carry essential information that cannot be discarded immediately. This study uses the Minimum Volume Ellipsoid estimator to treat the identified outliers differently. In this study, strong evidence was found that outliers do not have a completely negative connotation. Outliers should be treated differently because they carry essential information. This observation namely non-extreme outlier. The case study in this research uses house advertisement data from 5 districts in North Kalimantan and Berau district in East Kalimantan. The house in Tanjung Selor, Bulungan Regency, North Kalimantan, and Jalan Purnawirawan No. 21, RT 06, Karang Anyar, West Tarakan are suspected to be non-extreme outliers.
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[3] S. S. S. A. Mutalib, S. Z. Satari, and W. N. S. W. Yusoff, “Comparison of Robust Estimators for Detecting Outliers in Multivariate Datasets,” J. Phys. Conf. Ser., vol. 1988, no. 1, 2021, doi: 10.1088/1742-6596/1988/1/012095.
[4] A. A. A. Alkhatib and Q. Abed-Al, “Multivariate outlier detection for forest fire data aggregation accuracy,” Intell. Autom. Soft Comput., vol. 31, no. 2, pp. 1071–1087, 2022, doi: 10.32604/iasc.2022.020461.
[5] K. Huang, Y. Wu, C. Wang, Y. Xie, C. Yang, and W. Gui, “A Projective and Discriminative Dictionary Learning for High-Dimensional Process Monitoring with Industrial Applications,” IEEE Trans. Ind. Informatics, vol. 17, no. 1, pp. 558–568, 2021, doi: 10.1109/TII.2020.2992728.
[6] Fabiana Meijon Fadul, “PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST,” vol. 8, no. 1999, pp. 253–271, 2019.
[7] V. Kalender and D. Deteksi, “PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN MODEL VARIASI KALENDER DENGAN DETEKSI OUTLIER (Studi Kasus : PT. Kereta Api Indonesia DAOP IV Semarang),” vol. 6, pp. 281–289, 2017.
[8] D. Waldira, Alvi ; Hoyyi, Abdul; Ispriyanti, “Studi Kasus di Bandara Soekarno-Hatta) 1,2,3,” vol. 9, pp. 336–345, 2020.
[9] S. Sugidamayatno and D. Lelono, “Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF),” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, p. 409, 2019, doi: 10.22146/ijccs.46561.
[10] A. Raihanah, “Pearson Product Moment is Used More Than Just Measuring Correlation (Version One).” 2019.
[11] W. Soo Lon Wah, J. S. Owen, Y. T. Chen, A. Elamin, and G. W. Roberts, “Removal of masking effect for damage detection of structures,” Eng. Struct., vol. 183, no. November 2018, pp. 646–661, 2019, doi: 10.1016/j.engstruct.2019.01.005.
[12] R. N. M. Sanusi and D. R. S. Saputro, “Metode Robust Principle Component Analysis ( RPCA ) dengan Algoritme Proyeksi dan Matriks Ragam Peragam,” Prism. Pros. Semin. Nas. Mat., vol. 3, no. 2, pp. 52–57, 2020.
[13] A. Budi, S. Suma’inna, and H. Maulana, “Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis (PCA),” J. Tek. Inform., vol. 9, no. 2, pp. 166–175, 2018, doi: 10.15408/jti.v9i2.5608.
[14] K. Wada, M. Kawano, and H. Tsubaki, “Comparison of multivariate outlier detection methods for nearly elliptical distributions,” Austrian J. Stat., vol. 49, no. 2, pp. 1–17, 2020, doi: 10.17713/ajs.v49i2.872.
[15] C. Leys, O. Klein, Y. Dominicy, and C. Ley, “Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance,” J. Exp. Soc. Psychol., vol. 74, no. September 2017, pp. 150–156, 2018, doi: 10.1016/j.jesp.2017.09.011.
[16] R. Abo-Alsabeh and A. Salhi, “An Evolutionary Approach to Constructing the Minimum Volume Ellipsoid Containing a Set of Points and the Maximum Volume Ellipsoid Embedded in a Set of Points,” J. Phys. Conf. Ser., vol. 1530, no. 1, 2020, doi: 10.1088/1742-6596/1530/1/012087.
[17] A. H. M. R. Imon and A. S. Hadi, “Identification of multiple high leverage points in logistic regression,” J. Appl. Stat., vol. 40, no. 12, pp. 2601–2616, 2013, doi: 10.1080/02664763.2013.822057.
[18] H. Lu, Y. Liu, Z. Fei, and C. Guan, “An outlier detection algorithm based on cross-correlation analysis for time series dataset,” IEEE Access, vol. 6, pp. 53593–53610, 2018, doi: 10.1109/ACCESS.2018.2870151.
[19] P. J. Rousseeuw and M. Hubert, “Anomaly detection by robust statistics,” vol. 8, no. April, pp. 1–14, 2018, doi: 10.1002/widm.1236.
[20] S. K. Leem, F. Khan, and S. H. Cho, “Detecting Mid-air Gestures for Digit Writing,” IEEE Trans. Instrum. Meas., vol. PP, no. c, p. 1, 2019, doi: 10.1109/TIM.2019.2909249.
Published
2023-06-30
How to Cite
YULIANI, Risna.
IDENTIFIKASI NILAI ESENSIAL DARI OUTLIER NON-EXTREME MENGGUNAKAN METODE MINIMUM VOLUME ELLIPSOID.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 6, n. 1, p. 236-244, june 2023.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php?journal=Tekinkom&page=article&op=view&path%5B%5D=572>. Date accessed: 13 may 2026.
doi: https://doi.org/10.37600/tekinkom.v6i1.572.
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