- invasive species are threatening indigenous species habitat in many countries around the world. Nowadays, the monitoring method relies on scientists. Scientists are skilled to see the determined areas and record the living species. Applying high skill labors requires high cost, inefficient time and limited scope as the large area cannot be reached by the man. In this research, engine based learning approach was presented to identify the image of invasive hydrangea (indigenous species from Asia) with data collection around 800 images taken form the Brazil national forest and Hydrangea appears in some images. Gradient Descent optimization method is frequently used for artificial neural network. This method roles to discover standard grade for the best output. The Gradient Descent method role play is minimizing the cost function grade by changing the parameter grade step by step. Three optimization methods have been implemented namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam in the artificial neural network (Ann) for classifying aritmia data [32]. This research used the most suitable error grade limitation from each optimization method as the indicators at the end of the training. The result of this research showed that artificial nerve tissue using Adam optimization gets the highest accuration compared with SDG and ADADELTA optimization methods. Deep Learning Technique applied extensively in image introduction is Adam optimization. The training model has reached accuration to 83, 5 % and showed properness of approach conducted. Keyword: SGD, Adadelta, Adam, Optimizer FunctionAbstrak— Spesies invasif mengancam habitat spesies asli di banyak negara di dunia. Saat ini dalam metode pemantauan mereka tergantung pada pengetahuan ahli. Ilmuwan terlatih mengunjungi area yang ditentukan dan mencatat spesies yang menghuninya. Menggunakan tenaga kerja berkualifikasi tinggi seperti itu membutuhkan biaya yang mahal, tidak efisien waktu dan jangkauan yang terbatas karena manusia tidak dapat mencakup area yang luas. Dalam makalah ini, pendekatan berbasis pembelajaran mesin disajikan untuk mengidentifikasi gambar hydrangea invasif (spesies invasif asli Asia) dengan kumpulan data yang berisi sekitar 800 gambar yang diambil di hutan nasional Brasil dan di beberapa gambar terdapat Hydrangea.  Metode optimasi Gradient Descent sering digunakan untuk pelatihan Jaringan Syaraf Tiruan (JST). Metode ini berperan dalam menemukan nilai bobot yang memberikan nilai keluaran terbaik. Prinsip kerja metode Gradient Descent adalah memperkecil nilai fungsi biaya dengan mengubah nilai parameter selangkah demi selangkah. Telah diimplementasikan tiga buah metode optimasi yaitu Stochastic Gradient Descent (SGD), ADADELTA, dan Adam pada sistem Jaringan Saraf Tiruan untuk klasifikasi data aritmia [32]. Penelitian ini menggunakan batas nilai kesalahan yang paling sesuai dari masing-masing metode optimasi  sebagai kriteria pemberhentian pelatihan. Hasil penelitian menunjukkan Jaringan Saraf Tiruan dengan optimasi Adam menghasilkan akurasi tertinggi dibandingkan dengan dengan metode optimasi SGD dan ADADELTA.Teknik Deep Learning  yang diterapkan secara ekstensif pada pengenalan gambar yang digunakan memanfaatkan metode optimizer Adam  . Model yang kita latih menggunakan fungsi optimisasi Adam mencapai akurasi 83,5% pada tes yang lakukan, menunjukkan kelayakan pada  pendekatan yang dilakukan .Kata Kunci— SGD, Adadelta, Adam, Fungsi Optimasi

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Pekalongan is a region in Indonesia well-known for its batik production. The Pekalongan batik is rich in varieties of motifs, such as the Jlamprang, Liong, Terang Bulan, and Tujuh Rupa. The difficulty of distinguishing Pekalongan batik motifs for ordinary people causes the need for a model that can help recognize these motifs automatically based on input from digital images. This research aims to classify the Pekalongan batik motifs using a pre-trained Convolutional Neural Network (CNN), the Inception V3, and machine learning, the K-Nearest Neighbors (K-NN) algorithm. First, we extract the features from the digital image using the Inception V3 model, resulting in m x 2048 features, where m is the number of images. The extracted features generated from the Inception V3 model will be used as the dataset for the motif classification. We build models to classify the features using the K-Nearest Neighbors (KNN) with a K value of 5. In the classification process, we employ two distance metrics, the Euclidean and Manhattan distance, and analyze their performance using the 10-fold and 20-fold cross validation. The results of this study are the highest overall performace of accuracy (0.987), precision (0.987), and recall (0.987) produced by the Euclidean model.