There are several problems in industrial process for example problems associated
with product quality. In statistics, observation which is significantly different to the average is
called outlier. The outlier can give significant influence to the result of modeling, which can affect
the decision making. This research develops the outlier detection method using the Likelihood
Displacement Statistic method, called Likelihood Displacement Statistic-Lagrange (LDL)
method. The LDL method is applied to sugar and molasses production data of Djombang Baru
Sugar Factory, Jombang, East Java. The result of this research shows that factors influenced the
sugar and molasses production are sugar cane with the dirt less than 5%, sugar cane with the
dirt between 5% to 7%, sugar cane with the dirt higher than 7%, and imbibition water.

Creator(s)

• MAKKULAU
• SUSANTI LINUWIH
• PURHADI
• MUHAMMAD MASHURI

Contributor(s)

-

Publisher

Universitas Kristen Petra; 2010

Language

Indonesian

Category

jou – Journal

Sub Category

-

Source

Jurnal Teknik Industri, Vol. 12, No. 2, Desember 2010, 95-100; Makkulau (NA00404219), Susanti Linuwih (NA00404220), Purhadi (NA00404221), Muhammad Mashuri (NA00404222)

Subject(s)

• OUTLIERS (STATISTICS)
• MULTIVARIATE ANALYSIS
• LINEAR MODELS (STATISTICS)

File(s)

• jiunkpe-ns-jou-2010-na00404219-23739-pendeteksian_outlier-resource1.pdf