BAB 5 HASIL DAN PEMBAHASAN
5.5. Confusion Matrix
Adapun perhitungan manual untuk mendapatkan nilai akurasi klassifikasi pada dataset image angin puting beliung dan dataset image awan cumulonimbus dapat direpresentasikan hasil proses klassifikasi dengan menggunakan True Positive
(TP), True Negative (TN), False Positive (FP) dan False Negative (FN) seperti
* + *
Nilai akurasi terhadap klassifikasi training dataset image angin puting beliung dan testing dataset image awan cumulonimbus setelah dilakukan pengujian dari hasil algoritma dan model yang di dapat dari novalti dalam penelitian ini adalah sebesar 81.48%. Sehingga persentase jumlah dataset image awan yang benar diprediksi mengarah terjadinya angin puting beliung maupun yang tidak mengarah ke angin puting beliung dari keseluruhan dataset image awan adalah 81.48%.
Nilai presisi terhadap klassifikasi training dataset image angin puting beliung dan testing dataset image awan cumulonimbus setelah dilakukan pengujian dari hasil algoritma dan model yang di dapat dari novalti dalam penelitian ini adalah sebesar 0.80. Sehingga nilai presisi jumlah dataset image awan yang benar diprediksi mengarah terjadinya angin puting beliung dari keseluruhan dataset image awan yang mengarah ke angin puting beliung adalah sebesar 0.80.
Nilai recall terhadap klassifikasi training dataset image angin puting beliung dan testing dataset image awan cumulonimbus setelah dilakukan pengujian dari hasil algoritma dan model yang di dapat dari novalti dalam penelitian ini adalah sebesar 0.81 Sehingga persentase jumlah dataset image awan yang benar diprediksi mengarah terjadinya angin puting beliung dibandingkan dengan keseluruhan dataset image awan yang mengarah ke angin puting beliung adalah 0.81 .
Hasil Uji Analisi Dataset Testing Awan : Menggunakan Metode Naive Bayes untuk Cross Validation dan Confusion Matrix
=== Run information ===
Scheme: weka.classifiers.bayes.NaiveBayes
Relation: Dataset ACA Awan
Average Correlation Angle
35 3 | a = Awan Mengarah Angin Putting Beliung 7 9 | b = Awan Tidak Mengarah Angin Putting Beliung
Hasil Uji Analisis Dataset Training Angin Puting Beliung : Menggunakan Metode Naive Bayes untuk Cross Validation dan Confusion Matrix
=== Run information ===
Scheme: weka.classifiers.bayes.NaiveBayes Relation: New
std. dev. 0.0017 0.0017
Menggunakan Metode KNN untuk Cross Validation dan Confusion Matrix
=== Run information ===
Scheme: weka.classifiers.bayes.NaiveBayes Relation: New
Instances: 118 Attributes: 8
Number Sample Min
Number Sample Max Awan Mengarah Angin Putting Beliung Awan Tidak Mengarah Angin Putting Beliung
(0.52) (0.02)
precision 6.4885 6.4885
=== Confusion Matrix ===
a b c d <-- classified as
63 0 0 0 | a = Angin Putting Beliung 1 0 0 0 | b = Bukan Angin Putting Beliung
4 0 32 2 | c = Awan Mengarah Angin Putting Beliung 2 0 0 14 | d = Awan Tidak Mengarah Angin Putting Beliung
Keterangan dan Analisis :
TP : Jumlah dataset image awan yang mengarah ke angin puting beliung.
TN : Jumlah dataset image awan yang bukan mengarah ke angin puting beliung.
FP : Jumlah dataset image bukan awan yang mengarah ke angin puting beliung.
FN : Jumlah dataset image awan yang tidak mengarah ke angin puting beliung.
BAB 6
KESIMPULAN DAN SARAN
6.1. Kesimpulan
Pada bagian akhir dari desertasi ini, penulis memaparkan beberapa kesimpulan yang dapat diambil yang didasarkan pada temuan hasil penelitian diantaranya :
1. Model sampel dataset image yang digunakan image Angin Puting Beliung dan Awan untuk mendapatkan pola yang diekstrak menjadi Fenomena Chaos yaitu adanya ketidakteraturan spektrum warna dan edge/tepi.
2. Data yang dijadikan sebagai data training, data validasi dan data testing adalah untuk mendapatkan model baru dari penelitian ini yang berasal dari dataset image pihak ketiga.
3. Tahap pertama digunakan dengan Algoritma Edge Detection yang berfungsi untuk mengekstrak lapisan-lapisan yang terdapat pada awan dan angin puting beliung sehingga didapatkan model dengan menghasilkan image yang memiliki batas tepi.
4. Batas tepi yang terlihat adalah batas tepi yang memiliki spektrum warna yang ekstrem atau kontras (brightness) dengan hasil akhir berupa nilai Magnitude Gradiant.
5. Tahap kedua dengan menggunakan Algoritma Spectral Angle Mapper pada Supervised Image Classification untuk menghasilkan interval nilai Average Correlation Angle minimum dan maksimum sebesar 49.4° - 82.9° dari hasil training dataset image Fenomena Chaos.
6. Nilai akurasi yang didapat antara dataset training image angin puting beliung dengan dataset testing awan sebesar 81.48%, nilai presisi sebesar 0.80 dan nilai recall sebesar 0.81.
6.2. Saran
Berdasarkan dari pengkajian hasil penelitian pada disertasi yang dilakukan penulis, maka penulis bermaksud untuk memberikan saran yang mudah-mudahan dapat bermanfaat bagi lembaga maupun bagi peneliti selanjutnya, diantaranya sebagai berikut :
1. Dataset image yang digunakan pada penelitian ini dapat dikembangkan dari dataset lain, misalnya data hasil penelitian lapangan secara langsung baik dari hasil foto
maupun dari hasil video menggunakan satelite dan image dengan menggunakan time series.
2. Dapat dikembangkan dengan menggunakan metode Maximum Likelihood Classification dengan cara yang sama atau pun dengan cara yang berbeda untuk mendapatkan Algoritma baru.
3. Model yang terbentuk dari hasil penelitian ini dapat digunakan dan dikembangkan menjadi salah satu bahasan dalam mata kuliah Artificial Intelligence Supervised Image Classification ditambah dengan Chaos atau Fenomena Chaos.
4. Penelitian ini dapat dilanjutkan untuk menentukan range interval nilai awal,tenggah dan akhir dan dengan metode Maximum Likelihood Classification.
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