TY  - JOUR
T1  - Bıldırcınlarda Yumurta Kalite Özellikleri Arasındaki İlişkilerin Veri Madenciliği Yöntemleri ile İncelenmesi
TT  - Analysis by Data Mining Methods of Relationships among Egg Quality Characteristics in Japanese Quails
AU  - Çelik, Şenol
AU  - Şengül, Turgay
AU  - Şengül, Ahmet Yusuf
PY  - 2019
DA  - September
Y2  - 2019
DO  - 10.29133/yyutbd.580064
JF  - Yuzuncu Yıl University Journal of Agricultural Sciences
JO  - YYU J AGR SCI
PB  - Van Yuzuncu Yıl University
WT  - DergiPark
SN  - 1308-7576
SP  - 433
EP  - 439
VL  - 29
IS  - 3
LA  - tr
AB  - Buçalışmada, bıldırcınlarda bazı yumurta kalite özellikleri (şekil indeksi, kabukkalınlığı, kabuk ağırlığı, ak indeksi, ak yüksekliği, sarı indeksi, sarıyüksekliği, özgül ağırlık ve Haugh birimi) ile yumurtanın sarı ve ak ağırlığı arasındakiilişkiler CHAID (Chi-Squared Automatic Interaction Detection), GenişCHAID ve CART (Classificationand Regression Trees)algoritmaları kullanılarak incelenmiştir. CHAID, Geniş CHAID ve CARTalgoritmaları normallik, doğrusallık, homojenlik vb. varsayımları gerektirmediğindenönemli avantajlara sahiptirler. Yöntemlerin karşılaştırılmasında belirlemekatsayısı (R2), düzeltilmiş belirleme katsayısı (), HataKareler Ortalamasının Karekökü (RMSE) ve Ortalama mutlak yüzde hata(MAPE)kriterleri kullanılmıştır. Sonuç olarak, yumurtaların sarı ağırlığı üzerineetkili olan yumurta kalite özelliklerinin belirlenmesinde en uygun yönteminCHAID algoritması olduğu saptanmıştır. Bu yöntemle, en yüksek sarı ağırlığı,yumurta ağırlığının 13,36 g’dan fazla ve şekil indeksinin 0,895’ten daha yüksekolduğu gruptan elde edilmiştir. Ak ağırlığını etkileyen kalite özelliklerinibelirlemede ise, en uygun yöntem CART algoritması olmuştur. Bu yönteme göre, enyüksek yumurta ak ağırlığı, yumurta ağırlığının 12.47 g’dan fazla, akindeksinin 0,326 ve şekil indeksinin 0,865 olduğu gruptan elde edilmiştir.
KW  - Bıldırcın
KW  - Yumurta kalitesi
KW  - CHAID
KW  - CART
N2  - This study examined the relationships between someegg quality characteristics in quails (shape index, shell thickness, shellweight, egg white index, egg white height, yolk index, yolk height and Haughunit) and the yolk and white weights of eggs by using the CHAID (Chi-SquaredAutomatic Interaction Detection), Extended CHAID and CART (Classification andRegression Trees) algorithms. The CHAID, Extended CHAID and CART algorithmshave significant advantages as they do not require assumptions such asnormality, linearity and homogeneity. The methods were compared by using thecriteria of coefficient of determination (R2), adjusted coefficientof determination () and Root Mean Square Error (RMSE). As a result,the most suitable method for determining the egg quality characteristics thatare effective on the yolk weight of eggs was found to be the CHAID algorithm. Withthis method, the highest yolk weight was obtained from the group where eggweight was higher than 13.36 g, and the shape index was higher than 0.895. Fordetermining the quality characteristics that affect egg white weight, the mostsuitable method was found to be the CART algorithm. With this method, thehighest egg white weight was obtained from the group where egg weight washigher than 12.47 g, the egg white index was 0.326, and the shape index was0.865.
CR  - Ali, M., Eyduran, E., Tariq, MM., Tirink, C., Abbas, F., Bajwa, M. A., Baloch, M. H., Nizamani, A. H., Waheed, A., Awan, M. A., Shah, S. H., Ahmad, Z. & Jan, S. (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool. 47(6), 1579-1585.
CR  - Balta, B. & Topal, M. (2018). Regression tree approach for assessing the effects of non-genetic factors on birth weight of Hemşin lamb. Alınteri Journal of Agricultural Science, 33(1), 65-73.
CR  - Csuka, J. & Ledec, M. (1981). Egg Quality Evaluation by Selected Physical Markers. Rocz. Zoot. T. 8(2), 45-58.
CR  - Çelik, Ş., Şengül, T., İnci, H., Söğüt, B. & Şengül, A. Y. (2017). Estimation of egg weight from some external and internal quality characteristics in quail by using various data mining algorithms. Indian Journal of Animal Sciences, 87(12), 1524–1530.
CR  - Çelik, Ş. & Yılmaz, O. (2018). Prediction of body weight of Turkish Tazi Dogs using Data Mining techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Regression Splines (MARS). Pakistan Journal of Zoology, 50(2), 575-583.
CR  - Doğan, İ. (2003). Holştayn ırkı ineklerde süt verimine etki eden faktörlerin CHAID analizi ile incelenmesi. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 50, 65-70.
CR  - Duru, M., Duru, A. A., Karadaş, K., Eyduran, E., Cinli, H. & Tariq, M. M. (2017). Effect of Carrot 	(Daucus carota) leaf powder on external and internal egg characteristics of Hy-Line White Laying hens. Pakistan J. Zool., 49(1), 125-132.
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CR  - Eyduran, E., Yılmaz, İ., Kaygısız, A. & Aktaş, Z. M. (2013). An investigation on relationship between lactation milk yield, somatic cell count and udder traits in first lactation Turkish Saanen goat using different statistical techniques. The Journal Animal Plant Science, 23(4), 956-963.
CR  - Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K. & Grzesiak, W. (2017). Comparison of the Predictive Capabilities of Several Data Mining Algorithms and Multiple Linear Regression in the Prediction of Body Weight by Means of Body Measurements in the Indigenous Beetal Goat of Pakistan. Pakistan Journal of Zoology, 49(1), 273-282.
CR  - Gevrekçi, Y. & Takma, Ç. (2018). A Comparative Study for Egg Production in Layers by Decision Tree Analysis. Pakistan J. Zool., 50(2), 437-444.
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CR  - Koyuncugil, A. S. (2007). Borsa şirketlerinin sektörel risk profillerinin veri madenciliğiyle belirlenmesi. Sermaye Piyasası Kurulu Araştırma Raporu, Araştırma Dairesi, Ankara.
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CR  - Oguntunji, A. O. (2017). Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata). Genetika, 49(2), 743-753.
CR  - Orhan, H., Eyduran, E., Tatlıyer, A. & Saygıcı, H. (2016). Prediction of egg weight from egg quality characteristics via ridge regression and regression tree methods. Revista Brasileira de Zootecnia, 45(7), 380-385.
CR  - Takma, C., Atıl, H. & Aksakal, V. (2012). Comparison of Multiple Linear Regression and Artificial Neural Network Models Goodness of Fit to Lactation Milk Yields. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 18, 941-944.
UR  - https://doi.org/10.29133/yyutbd.580064
L1  - https://dergipark.org.tr/en/download/article-file/821673
ER  -