Orange: Bag of Words - Revision history

Onnowpurbo: /* Contoh */

2020-04-12T02:25:42Z

Contoh

Onnowpurbo at 10:46, 11 April 2020

2020-04-11T10:46:42Z

Onnowpurbo: /* Referensi */

2020-04-08T21:52:19Z

Referensi

Onnowpurbo: /* Contoh */

2020-03-19T10:34:55Z

Contoh

Onnowpurbo: /* Contoh */

2020-03-19T10:30:52Z

Contoh

Onnowpurbo at 07:51, 19 March 2020

2020-03-19T07:51:23Z

Onnowpurbo at 03:27, 29 January 2020

2020-01-29T03:27:04Z

Onnowpurbo at 08:28, 24 January 2020

2020-01-24T08:28:39Z

Onnowpurbo at 08:03, 17 January 2020

2020-01-17T08:03:16Z

Onnowpurbo at 03:33, 1 August 2017

2017-08-01T03:33:20Z

@@ Line 36: / Line 36: @@
 Dalam contoh ini kita hanya akan men-cek seperti apa sebuah bag of words model. Load book-excerpts.tab menggunakan widget Corpus dan sambungkan ke widget Bag of Words. Disini kita sengaja menggunakan parameter defaults - count paling sederhana adalah menghitung frekuensi istilah (term frequency). Cek apa yang di keluarkan oleh widget Bag of Words menggunakan widget Data Table. Kolom terakhir merepresentasikan frekuensi istilah (term frequency) dari setiap dokumen.
-[[File:Bag-of-Words-Example1.png|center|200px|thumb]]
+[[File:Bag-of-Words-Example1.png|center|600px|thumb]]
-Pada contoh selanjutnya, kita akan mencoba mem-prediksi kategori dokumen. Kita menggunakan dataset book-excerpts.tab, yang kita kirim melalui widget Preprocess Text dengan default parameter. Kemudian kita sambungkan widget Preprocess Text ke widget Bag of Words untuk memperoleh frekuensi istilah (term frequency). Dengan berbekal informasi frekuensi istilah (term frequency) kita akan  menghitung model.
+Dalam contoh ini, kita akan mencoba mem-prediksi kategori dokumen. Kita menggunakan dataset book-excerpts.tab, yang kita kirim melalui widget Preprocess Text dengan default parameter. Kemudian kita sambungkan widget Preprocess Text ke widget Bag of Words untuk memperoleh frekuensi istilah (term frequency). Dengan berbekal informasi frekuensi istilah (term frequency) kita akan  menghitung model.
 Sambungkan widget Bag of Words ke widget Test & Score untuk melakukan predictive modelling. Sambungkan widget SVM atau widget classifier lainnya ke widget Test & Score (semua disisi kiri). Widget Test & Score akan menghitung score performance untuk setiap learner yang ada di input. Disini kita memperoleh hasil yang baik sekali untuk widget SVM.
 Selanjutnya, kita perlu mencek dimana model melakukan kesalahan. Tambahkan, widget Confusion Matrix ke widget Test & Score. Widget Confusion matrix akan menayangkan dokumen yang berhasil di klasifikasi dengan benar dan salah klasifikasi. Pilih / select misclassified akan mengeluarkan dokumen yang misclassified, yang akan kita telaah lebih lanjut menggunakan widget Corpus Viewer.
 ==Contoh Lain==

@@ Line 2: / Line 2: @@
-Membuat sebuah bag of words dari input corpus.
+Widget Bag of Words membuat sebuah bag of words dari input corpus.
 ==Input==
@@ Line 44: / Line 44: @@
 Sambungkan widget Bag of Words ke widget Test & Score untuk melakukan predictive modelling. Sambungkan widget SVM atau widget classifier lainnya ke widget Test & Score (semua disisi kiri). Widget Test & Score akan menghitung score performance untuk setiap learner yang ada di input. Disini kita memperoleh hasil yang baik sekali untuk widget SVM.
-Selanjutnya, kita perlu mencek dimana model melakukan kesalahan. Tambahkan, widget Confusion Matrix ke widget Test & Score. Widget Confusion matrix akan menayangkan dokumen yang berhasil di klasifikasi dengan benar dan salah klasifikasi. Pilih / select misclassified akan mengeluarkan dokumen yang misclassified, yang akan kita telaah lebih lanjut menggunakan Corpus Viewer.
+Selanjutnya, kita perlu mencek dimana model melakukan kesalahan. Tambahkan, widget Confusion Matrix ke widget Test & Score. Widget Confusion matrix akan menayangkan dokumen yang berhasil di klasifikasi dengan benar dan salah klasifikasi. Pilih / select misclassified akan mengeluarkan dokumen yang misclassified, yang akan kita telaah lebih lanjut menggunakan widget Corpus Viewer.
 ==Contoh Lain==

← Older revision		Revision as of 21:52, 8 April 2020
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	[[File:Orange-bagofwords3.png\|center\|400px\|thumb]]		[[File:Orange-bagofwords3.png\|center\|400px\|thumb]]

		+
		+	==Youtube==
		+
		+	* [https://www.youtube.com/watch?v=plwumuSzwvs YOUTUBE: ORANGE klasifikasi text dokumen]

	==Referensi==		==Referensi==

@@ Line 42: / Line 42: @@
 [[File:Bag-of-Words-Example2.png|center|200px|thumb]]
-Sambungkan widget Bag of Words ke widget Test & Score untuk melakukan predictive modelling. Sambungkan widget SVM atau widget classifier lainnya ke widget Test & Score (semua disisi kiri). Widget Test & Score akan menghitung score performance untuk setiap learner yang ada di input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.
+Sambungkan widget Bag of Words ke widget Test & Score untuk melakukan predictive modelling. Sambungkan widget SVM atau widget classifier lainnya ke widget Test & Score (semua disisi kiri). Widget Test & Score akan menghitung score performance untuk setiap learner yang ada di input. Disini kita memperoleh hasil yang baik sekali untuk widget SVM.
-Add Confusion Matrix to Test & Score. Confusion matrix displays correctly and incorrectly classified documents. Select Misclassified will output misclassified documents, which we can further inspect with Corpus Viewer.
+Selanjutnya, kita perlu mencek dimana model melakukan kesalahan. Tambahkan, widget Confusion Matrix ke widget Test & Score. Widget Confusion matrix akan menayangkan dokumen yang berhasil di klasifikasi dengan benar dan salah klasifikasi. Pilih / select misclassified akan mengeluarkan dokumen yang misclassified, yang akan kita telaah lebih lanjut menggunakan Corpus Viewer.
 ==Contoh Lain==

@@ Line 34: / Line 34: @@
 ==Contoh==
-Dalam contoh ini kita hanya akan men-cek seperti apa sebuah bag of words model. Load book-excerpts.tab menggunakan widget Corpus dan sambungkan ke widget Bag of Words. Disini kita sengaja menggunakan parameter defaults - count paling sederhana adalah menghitung frekuensi istilah. Cek apa yang di keluarkan oleh widget Bag of Words menggunakan widget Data Table. Kolom terakhir merepresentasikan frekuensi istilah dari setiap dokumen.
+Dalam contoh ini kita hanya akan men-cek seperti apa sebuah bag of words model. Load book-excerpts.tab menggunakan widget Corpus dan sambungkan ke widget Bag of Words. Disini kita sengaja menggunakan parameter defaults - count paling sederhana adalah menghitung frekuensi istilah (term frequency). Cek apa yang di keluarkan oleh widget Bag of Words menggunakan widget Data Table. Kolom terakhir merepresentasikan frekuensi istilah (term frequency) dari setiap dokumen.
 [[File:Bag-of-Words-Example1.png|center|200px|thumb]]
-In the second example we will try to predict document category. We are still using the book-excerpts.tab data set, which we sent through Preprocess Text with default parameters. Then we connected Preprocess Text to Bag of Words to obtain term frequencies by which we will compute the model.
+Pada contoh selanjutnya, kita akan mencoba mem-prediksi kategori dokumen. Kita menggunakan dataset book-excerpts.tab, yang kita kirim melalui widget Preprocess Text dengan default parameter. Kemudian kita sambungkan widget Preprocess Text ke widget Bag of Words untuk memperoleh frekuensi istilah (term frequency). Dengan berbekal informasi frekuensi istilah (term frequency) kita akan  menghitung model.
 [[File:Bag-of-Words-Example2.png|center|200px|thumb]]
-Connect Bag of Words to Test & Score for predictive modelling. Connect SVM or any other classifier to Test & Score as well (both on the left side). Test & Score will now compute performance scores for each learner on the input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.
+Sambungkan widget Bag of Words ke widget Test & Score untuk melakukan predictive modelling. Sambungkan widget SVM atau widget classifier lainnya ke widget Test & Score (semua disisi kiri). Widget Test & Score akan menghitung score performance untuk setiap learner yang ada di input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.
 Add Confusion Matrix to Test & Score. Confusion matrix displays correctly and incorrectly classified documents. Select Misclassified will output misclassified documents, which we can further inspect with Corpus Viewer.
 ==Contoh Lain==

@@ Line 4: / Line 4: @@
 Generates a bag of words from the input corpus.
-Inputs
+==Input==
-    Corpus: A collection of documents.
+ Corpus: A collection of documents.
-Outputs
+==Output==
-    Corpus: Corpus with bag of words features appended.
+ Corpus: Corpus with bag of words features appended.
 Bag of Words model creates a corpus with word counts for each data instance (document). The count can be either absolute, binary (contains or does not contain) or sublinear (logarithm of the term frequency). Bag of words model is required in combination with Word Enrichment and could be used for predictive modelling.
@@ Line 16: / Line 16: @@
 [[File:Bag-of-Words-stamped.png|center|200px|thumb]]
-    Parameters for bag of words model:
+* Parameters for bag of words model:
-        Term Frequency:
+** Term Frequency:
-            Count: number of occurrences of a word in a document
+*** Count: number of occurrences of a word in a document
-            Binary: word appears or does not appear in the document
+*** Binary: word appears or does not appear in the document
-            Sublinear: logarithm of term frequency (count)
+*** Sublinear: logarithm of term frequency (count)
-        Document Frequency:
+** Document Frequency:
-            (None)
+*** (None)
-            IDF: inverse document frequency
+*** IDF: inverse document frequency
-            Smooth IDF: adds one to document frequencies to prevent zero division.
+*** Smooth IDF: adds one to document frequencies to prevent zero division.
-        Regulariation:
+** Regulariation:
-            (None)
+*** (None)
-            L1 (Sum of elements): normalizes vector length to sum of elements
+*** L1 (Sum of elements): normalizes vector length to sum of elements
-            L2 (Euclidean): normalizes vector length to sum of squares
+*** L2 (Euclidean): normalizes vector length to sum of squares
-    Produce a report.
+* Produce a report.
-    If Commit Automatically is on, changes are communicated automatically. Alternatively press Commit.
+* If Commit Automatically is on, changes are communicated automatically. Alternatively press Commit.
 ==Contoh==

@@ Line 14: / Line 14: @@
 Bag of Words model creates a corpus with word counts for each data instance (document). The count can be either absolute, binary (contains or does not contain) or sublinear (logarithm of the term frequency). Bag of words model is required in combination with Word Enrichment and could be used for predictive modelling.
-../_images/Bag-of-Words-stamped.png
+[[File:Bag-of-Words-stamped.png|center|200px|thumb]]
      Parameters for bag of words model:
@@ Line 32: / Line 32: @@
      If Commit Automatically is on, changes are communicated automatically. Alternatively press Commit.
-Example
+==Contoh==
 In the first example we will simply check how the bag of words model looks like. Load book-excerpts.tab with Corpus widget and connect it to Bag of Words. Here we kept the defaults - a simple count of term frequencies. Check what the Bag of Words outputs with Data Table. The final column in white represents term frequencies for each document.
-../_images/Bag-of-Words-Example1.png
+[[File:Bag-of-Words-Example1.png|center|200px|thumb]]
 In the second example we will try to predict document category. We are still using the book-excerpts.tab data set, which we sent through Preprocess Text with default parameters. Then we connected Preprocess Text to Bag of Words to obtain term frequencies by which we will compute the model.
-../_images/Bag-of-Words-Example2.png
+[[File:Bag-of-Words-Example2.png|center|200px|thumb]]
 Connect Bag of Words to Test & Score for predictive modelling. Connect SVM or any other classifier to Test & Score as well (both on the left side). Test & Score will now compute performance scores for each learner on the input. Here we got quite impressive results with SVM. Now we can check, where the model made a mistake.
@@ Line 47: / Line 47: @@
 [[File:Orange-bagofwords1.png|center|400px|thumb]]
-[[File:Orange-bagofwords2.png|center|200px|thumb]]
+[[File:Orange-bagofwords2.png|center|400px|thumb]]
-[[File:Orange-bagofwords3.png|center|200px|thumb]]
+[[File:Orange-bagofwords3.png|center|400px|thumb]]

@@ Line 1: / Line 1: @@
-sumber: http://orange3-text.readthedocs.io/en/latest/widgets/bagofwords.html
+Sumber: https://orange3-text.readthedocs.io/en/latest/widgets/bagofwords-widget.html
@@ Line 12: / Line 58: @@
 ==Referensi==
-* http://orange3-text.readthedocs.io/en/latest/widgets/bagofwords.html
+* https://orange3-text.readthedocs.io/en/latest/widgets/bagofwords-widget.html

← Older revision		Revision as of 03:33, 1 August 2017
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	[[File:Orange-bagofwords1.png\|center\|400px\|thumb]]		[[File:Orange-bagofwords1.png\|center\|400px\|thumb]]

		+	[[File:Orange-bagofwords2.png\|center\|200px\|thumb]]
		+
		+	[[File:Orange-bagofwords3.png\|center\|200px\|thumb]]