Meiga Isyatan Mardiyah
Universitas Islam Indonesia

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Developing deep learning architecture for image classification using convolutional neural network (CNN) algorithm in forest and field images Meiga Isyatan Mardiyah; Tuti Purwaningsih
Science in Information Technology Letters Vol 1, No 2: November 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i2.160

Abstract

Indonesia is an agricultural country with a variety of natural resources such as agriculture and plantations. Agriculture and plantations in Indonesia are diverse, such as rice fields that can produce rice, soybeans, corn, tubers, and others. Meanwhile, plantations in Indonesia are like forests with timber products, bamboo, eucalyptus oil, rattan, and others. However, rice fields, which are examples of agriculture, and forests that are examples of plantations, have the same characteristics. It is not easy to distinguish when viewed using aerial photographs or photographs taken from a certain height. For recognizing with certainty the shape of rice fields and forests when viewed using aerial photographs, it is necessary to establish a model that can accurately recognize the shape of rice fields and forest forms. A model is to utilize computational science to take information from digital images to recognize objects automatically. One method of deep learning that is currently developing is a Convolutional Neural Network (CNN). The CNN method enters (input data) in the form of an image or image. This method has a particular layer called the convulsive layer wherein an input image layer (input image) will produce a pattern of several parts of the image, which will be easier to classify later. The convolution layer has the function of learning images to be more efficient to be implemented. Therefore, researchers want to utilize this CNN method to classify forests and rice fields to distinguish the characteristics of forests and rice fields. Based on the classification results obtained by testing the accuracy of 90%. It can be concluded that the CNN method can classify images of forests and rice fields correctly.
Analisis Faktor Pelanggan dalam Pemilihan Toko Ritel Modern dan Toko Ritel Tradisional di Kota Yogyakarta Faisal Ardiansyah; Indrianti Ismayani; Meiga Isyatan Mardiyah; Edy Widodo
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2020: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.42 KB)

Abstract

Minimarket adalah sebuah usaha yang menawarkan berbagai macam barang mulai dari makanan, sembako, peralatan dapur serta ditunjang dengan fasilitas dan tempat yang nyaman bagi pelanggan. Seiring berjalannya waktu ritel tradisional banyak yang gulung tikar. Berubahnya pola belanja masyarakat hal tersebut juga terjadi di Yogyakarta. Oleh karena itu dibutuhkan analisis terhadap faktor yang berpengaruh. Dalam hal ini analisis yang digunakan adalah Principal Component Analysis (PCA). PCA adalah suatu metode yang melibatkan prosedur matematika yang mengubah dan mentransformasikan sejumlah besar variabel yang berkorelasi menjadi sejumlah kecil variabel yang tidak berkorelasi, tanpa menghilangkan informasi penting di dalamnya. (Jatra, Isnanto, & Imam, 2007). Hasil diperoleh hasil sebagai berikut: Faktor menjamurnya Toko Ritel Modern di Yogyakarta yaitu terdapat 5 komponen PCA. Faktor yang dapat mempengaruhi keberlangsungan Toko Ritel Tradisional di Yogyakarta yaitu terdapat 3 komponen PCA. Faktor pembanding Toko Ritel Tradisional agar tetap bertahan yaitu terdapat 3 komponen PCA.