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Zuzzaifa, Nur
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Convolutional Neural Network Untuk Perbandingan Optimizer Pada Citra Batang Pohon Zuzzaifa, Nur; Rianto, Rianto
Jurnal Sistem Cerdas Vol. 6 No. 3 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i3.268

Abstract

In the surrounding environment, there are various types of trees with different characteristics. One characteristic of a tree that is difficult to distinguish is its trunk. After researchers made observations, the trunks of Pine and Tabebuya trees had the same characteristics, namely cracking. The problem of incorrectly identifying the characteristics of a tree's trunk can be overcome by classification. Deep Learning with the Convolutional Neural Network (CNN) algorithm is a method commonly used in image classification. The stages in this research include image data retrieval, data preprocessing, CNN architecture formation, model training, and model validation. Image retrieval was carried out directly by researchers, then the 1000 best images were selected. The image dataset is then divided into 75% training data and 25% validation data. Testing was carried out by comparing the Stochastic Gradient Descent (SGD) optimizer and the Adaptive Learning Rate Optimization Method (RMSprop) using epochs 10, 15, 20, 30, 50, and 80. The results showed that the SGD optimizer produced the highest accuracy compared to the RMSProp optimizer. The most optimal result when applying the SGD optimizer is 0.9360 with epochs 80, while for the RMSProp optimizer it is 0.9160 with epochs 20.
Implementasi Algoritme Long Short-Term Memory untuk Prediksi Harga Saham BBCA dan BBRI Zuzzaifa, Nur; Dwi Sancoko, Sulistyo
Jurnal Telematika Vol. 19 No. 2 (2024)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v19i2.701

Abstract

Berinvestasi dalam instrumen saham memiliki tingkat risiko yang tinggi. Hal ini terjadi karena pergerakan saham pada pasar sulit diprediksi. Analisis data historis dapat menjadi solusi para investor dalam meramalkan pergerakan harga saham di masa mendatang. Selain meningkatkan kesadaran akan pentingnya investasi, teknologi juga membantu dalam pengambilan keputusan. Penelitian ini memprediksi harga saham menggunakan algoritme Long Short-Term Memory (LSTM). Data yang digunakan diambil dari website Yahoo Finance, variabel yang digunakan hanya data penutupan (close) saham. Tahapan-tahapan yang dilakukan, seperti studi literatur, pengumpulan data, pembagian data, preprocessing data, pembentukan model, denormalisasi, dan evaluasi. Dari model yang dibangun didapatkan hasil paling optimal pada PT Bank Rakyat Indonesia, Tbk. (BBRI) dengan nilai RMSE data pelatihan sebesar 37,037 dan RMSE data pengujian sebesar 80,128. Sementara itu, pengujian menggunakan algoritme LSTM pada PT Bank Central Asia, Tbk. (BBCA) didapatkan nilai RMSE data pelatihan sebesar 36,905 dan RMSE data pengujian sebesar 99,9. Selanjutnya, model terbaik digunakan untuk memprediksi harga saham PT BCA dan PT BRI dalam sebulan ke depan.
Co-Authors Dwi Sancoko, Sulistyo Rianto Rianto