Prediksi Harga Emas Menggunakan Univariate Convolutional Neural Network

Halimi, Imam; Marthasari, Gita Indah; Azhar, Yufis

doi:10.22219/repositor.v1i2.612

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Investasi terhadap Emas sudah mulai booming dan bergejolak naik pada beberapa tahun belakang, hal ini dikarenakan harga Emas yang cenderung naik dan juga tidak terdapat cacat terhadap emas yang akan disimpan dalam jangaka waktu yang tidak dapat ditentukan. Investas terhadap emas bukanlah hal yang sulit, hal tersebutlah yang mendorong terhadap banyaknya masayarakat ataupun kalangan atas yang berinvestasi terhadap Emas [5], [6].…”

Section: Pendahuluanunclassified

Penerapan Data Mining Pada Prediksi Harga Emas dengan Menggunakan Algoritma Regresi Linear Berganda dan ARIMA

Wijaya,

Triayudi

2023

JoSYC

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The development of life has developed very rapidly at this time, one thing that has quite an important influence is the business processes carried out. Investment is a business that is carried out by all levels and also members of society easily and flexibly. Currently, the investment that is very popular with the public is gold. Gold itself is one of the most sought after precious metals at the moment, apart from being used to beautify oneself, gold can also be used as an investment asset. Based on several factors above, many people invest in gold. Investments made in Gold are not investments that have a short period of time but investments that are made over a fairly long period of time. Investing in Gold is done by buying Gold at a cheap price at the moment and then selling it again when the Gold price has risen. However, in the process that occurs, problems often occur, where the problems that occur are related to the price of gold. Where this problem can be solved by making a prediction. Data mining is used in predictions because the prediction process is carried out using data mining based on data processing. Data mining itself is a technique that is widely used today to assist in the problem solving process. In this research, the solution process was carried out using the Multiple Linear Regression algorithm and also ARIMA. In this research, the research process will be carried out by comparing the Multiple Linear Regression algorithm. Comparison of algorithms aims to obtain the most optimal results from implementing the algorithm. In solving using the Multiple Linear Regression algorithm and ARIMA, these two algorithms can help solve prediction problems by producing optimal results. From the process carried out, the Multiple Linear Regression algorithm has an RMSE value of 4902782.346, while the ARIMA algorithm gets a value of 5876287.332. This indicates that the results of the Multiple Linear Regression algorithm are better than the ARIMA algorithm.

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Section: Pendahuluanunclassified

Penerapan Data Mining Pada Prediksi Harga Emas dengan Menggunakan Algoritma Regresi Linear Berganda dan ARIMA

Wijaya,

Triayudi

2023

JoSYC

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“…The model consists of univariate, multivariate, multi-step, and multivariate multi-step [43]. CNN in forecasting data is time series often used to estimate stock prices [44][45], gold prices [46][47] [48], health [49][50] [51], time series [52][53] [54], solar cells and weather forecasts [55].…”

Section: E Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction

Pranolo

Mao²,

Wibawa³

et al. 2022

Knowl. Eng. Data Sci.

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Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.

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“…There are six data mining stages, including (Halimi et al, 2019) : a. Data cleaning is the process of removing noise and inconsistent or irrelevant data.…”

Section: A Understanding Data Miningmentioning

confidence: 99%

Apriori Algorithm On Car Rental Analysis With The Most Popular Brands

Panjaitan

Handrianto

Nurhadi

2020

SinkrOn

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Nowadays, vehicle rental has become a common function for companies that have busy operational activities. Every company in carrying out operational activities requires a vehicle that is always there when needed. PT. Agung Solusi Trans is a vehicle rental company that rents various vehicle brands commonly used by customers to rent vehicles. In addition, PT. Agung Solusi Trans is also difficult to get updated information regarding the level of sales per period. Therefore, we need a decision support system and a method that can be used to design a business strategy that can provide an efficient and effective information, namely data mining using the a priori algorithm association method. The researcher specializes in taking only vehicle types as research material by selecting fifteen brands, including Agya, Yaris, Sienta, Calya, Avanza, Innova, Rush, Vios, Altis, Camry, Fortuner, Alphard, Hi Ace, Voxy, and Hilux. In analyzing the data, the writer uses a priori algorithm calculation by testing the hypothesis of two variables between the value of support and the value of confidence. After that, apriori algorithm is calculated using Tanagra. Based on the analysis done by the author, that the brands most sought after by customers are Calya, Avanza, Hilux. From these results can be used by PT. Agung Solusi Trans to prepare vehicle brands that are widely leased by customers and increase brand inventory.

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Prediksi Harga Emas Menggunakan Univariate Convolutional Neural Network

Cited by 5 publications

References 5 publications

Penerapan Data Mining Pada Prediksi Harga Emas dengan Menggunakan Algoritma Regresi Linear Berganda dan ARIMA

Penerapan Data Mining Pada Prediksi Harga Emas dengan Menggunakan Algoritma Regresi Linear Berganda dan ARIMA

Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction

Apriori Algorithm On Car Rental Analysis With The Most Popular Brands

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