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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Buguh: Jurnal Pengabdian Kepada Masyarakat
Ranti Vidia Mahyunis
Universitas Lampung
Humanities Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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PEMETAAN DAERAH POTENSI RAWAN BANJIR DENGAN SISTEM INFORMASI GEOGRAFI METODE WEIGHTED OVERLAY DI KELURAHAN KETEGUHAN Tarkono; As'ad Humam; Ranti Vidia Mahyunis; Shofiyyah Fauziah Sayuti; Gema Annisa Hermastuti; Dafa Sitanala Putra Baladiah; Indah Rahmayani
BUGUH: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 1 No. 3 (2021)
Publisher : Badan Pelaksana Kuliah Kerja Nyata Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1401.527 KB) | DOI: 10.23960/buguh.v1n3.138

Abstract

Abstract Keteguhan Village is an area that has the highly potential to flood disaster, such as the flash flood incident that occurred on March 30, 2020. Floodprone mapping is needed to map flood prone potentials in SKeteguhan Village with the aim of increasing the alertness and readiness of the Keteguhan Village’s community in dealing that disaster. The used method includes processing the parameters of rainfall, land cover, slope, soil type, land height and land cover, then carried out by a weighted overlay process to form new data in the form of a flood prone potential map. The obtained results are that there are 3 potential areas, namely the lowlands along the river area of Umbul Kunci Street, the river area in the nearest neighborhood of Keteguhan Village and Mushollah Nurul Jannah on Laksamana R.E. Martadinata Street. Based on the area of vulnerability level in Keteguhan Village, the safe category has an area of up to 137,451 Ha with a percentage of 44.6%, the non-prone category has an area of up to 95,5116 Ha with a percentage of 30.01%, the vulnerable category has an area of up to 62.4922 Ha with a percentage of 20.27% and the very vulnerable category has area up to 15.7767 Ha with a percentage of 5.12%.
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES Dian Kurniasari; Ranti Vidia Mahyunis; Warsono Warsono; Aang Nuryaman
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 1 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i1.587

Abstract

The trend in global currency values is speedy and fluctuating due to the recession caused by the Covid-19 pandemic. That causes investors to flock to buy gold assets. Therefore, it is necessary to predict the price of gold from a business and academic perspective to obtain a reasonable gold price prediction model. This study applies the Backpropagation Algorithm by determining the best ANN model structure based on four activation functions: Sigmoid, Tanh, ReLU, and Linear, as well as learning rate values, namely 0.01 and 0.001. The results are the best ANN model structure with four nodes in the input layer, four nodes in the hidden layer and the output layer using the Linear activation function and a learning rate of 0.01. Based on the structure of the model, the MSE value is 0.00051, the MAPE value is 1.9798%, and the accuracy is 98%.Keywords: Artificial Neural Network, Backpropagation, Gold Price Prediction, Activation Function, Model Structure Trend nilai mata uang global sangat cepat dan fluktuatif akibat terjadinya resesi yang disebabkan oleh pandemi Covid-19. Hal ini menyebabkan, para investor berbondong-bondong untuk membeli aset emas. Oleh sebab itu, perlu dilakukan prediksi harga emas, baik dari perspektif bisnis maupun akademis agar memperoleh model prediksi harga emas yang baik. Penelitian ini menerapkan Algoritma Backpropagation dengan menentukan struktur model ANN terbaik berdasarkan empat fungsi aktivasi yaitu, Sigmoid, Tanh, ReLU, dan Linear serta nilai learning rate, yaitu 0,01 dan 0,001. Hasil yang diperoleh berupa struktur model ANN terbaik dengan empat node pada input layer, empat node pada hidden layer dan output layer dengan menggunakan fungsi aktivasi Linear dan learning rate sebesar 0,01. Berdasarkan struktur model tersebut, diperoleh nilai MSE sebesar 0.00051, nilai MAPE sebesar 1,9798% dan akurasi sebesar 98%.Kata Kunci: Artificial Neural Network, Backpropagation, Prediksi Harga Emas, Fungsi Aktivasi, Struktur Model
Co-Authors Aang Nuryaman As'ad Humam Dafa Sitanala Putra Baladiah Dian Kurniasari Gema Annisa Hermastuti Indah Rahmayani Shofiyyah Fauziah Sayuti Tarkono Tarkono Warsono Warsono