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+.. title: Belajar KMeans Menggunakan Python .. slug: .. date: 2019-05-5 .. tags: KMeans, Python, Clustering, sklearn .. category: Data Aanalysis .. link: .. description: Klasterisasi data menggunakan Python .. type: text .. figure:: https://www.kcet.org/sites/kl/files/atoms/article_atoms/www.kcet.org/living/homegarden/firewood-01.jpg :target: https://www.kcet.org/sites/kl/files/atoms/article_atoms/www.kcet.org/living/homegarden/firewood-01.jpg
 
+Photo by Horia Varlan/Flickr/Creative Commons License
+
+Apa itu KMeans? ###########
+
+KMeans merupakan metode klasterisasi yang mudah dan sederhana. Klasterisasi KMeans didasarkan pada kedekatan data. Jarak antar titik data dapat diukur menggunakan metode Eucledian, Mnhattan atau Minkowski. Berikut adalah klasterisasi KMeans menggunkan Python dengan menggunakan dataset bunga Iris.
+
+Dataset Bunga Iris?
+
+High level conceptual information (Heading 2).
+
+At a minimum, a concept includes the following components.
+
+A title, phrased as a gerund or question.
+One or more body paragraphs.
+Complex concepts may contain 2 or more subsections.
+
+What is ?
+When you need to break down a subject, you can break it down into subsections (H3s). Typically you would have 0 H3s, or 2+ H3s.
+
+What is ?
+When you need to break down a subject, you can break it down into subsections (H3s)
+
+Do this
+
+A task typically follows conceptual information. Task titles should be imperative. Tasks should have a short introduction sentence that captures the user's goal and introduces the steps, for example, "Verify your products are in the catalog:"
+
+A task should have 3 - 7 steps. Tasks with more should be broken down into digestible chunks.
+
+Intro sentence.
+
+#. Step 1.
+
+#. Step 2.
+
+#. Step 3.
+
+Following the steps, you should add the result and any follow-up tasks needed.