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Stock Price Forecasting on Time Series Data Using the Long Short-Term Memory (LSTM) Model

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Stock price forecasting on time series data is a complex task due to the dynamic and uncertain nature of financial markets. This research aims to forecast stock prices by applying an advanced machine learning model, namely Long Short-Term Memory (LSTM), a deep learning architecture that excels in capturing long-term dependencies in time series data. The dataset used in this study consists of 1221 daily ANTM.JK stock price data over the period April 30, 2019 to April 30, 2024. The model was trained and evaluated using performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) in measuring the level of forecasting accuracy. The results show that the LSTM model can accurately predict stock prices on time series data, as evidenced by the MAPE accuracy evaluation value of 2.52% and RMSE of 54.64. These findings indicate that the LSTM model is effective in predicting stock prices on time series data and can be used as a supporting tool in making the right investment decisions.

Analysis of Rainwater Availability and Water Requirements in the Amarasi District Area Kupang Regency

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: The hydrological conditions of the Amarasi District, Kupang Regency have 3-4 wet months and 8-9 dry months according to Oldeman’s classification. Annual rainfall in the last ten years shows the lowest rainfall was 1,461mm and the highest was 2,688mm, the average annual rainfall was 1,859mm. The Amarasi region is included in zones D and E of the Oldeman climate classification. The practice of utilizing limited water resources by planting fodder crops in the form of legumes and grasses as well as food crops in integrated dry land agriculture has been carried out by the Amarasi community. This research examines the availability and demand for domestic, agricultural and livestock water and will produce an availability index as the carrying capacity of regional water availability. The method used to calculate the availability of water originating from rain is runoff analysis based on weighted coefficients for each land use, then analyzed in a monthly series and compared with the level of water demand for each use. Domestic water needs, livestock drinking and irrigation follow SNI 19-6728.1.2002. Then the analysis results are interpreted with tables and time series graphs. The research results show the following time series of water availability: January: 44,626,635.13m3; February: 31,210,646.01m3; March : 20,050,098.53m3; April: 11,726,074.47m3; May : 3,023,180.66m3; June: 1,747,689.52m3; July : 973,284.18m3; August : 168,880.36m3; September: 1,026,614.82m3. October 2,846,522.91m3; November 10,713,903.37m3; and December: 26,871,976.23m3. The amount of water demand in the time series is as follows: January: 1,929,491.70m3; February: 1,391,817.98m3; March: 1,697,543.42m3; April: 781,567.95m3; May : 883,736.22m3; June: 755,911.95m3; July : 284,777.42m3; August : 384,569.42m3; September: 387,871.95m3. October 355,448.02m3; November 3,242,283.15m3; and December: 2,631,159.26m3. Water availability index: January: 4.32 (good), February: 4.46 (good), March: 8.47 (good), April: 6.67 (good), May: 29.23 (slight critical), June: 43.25 (mild critical), July: 29.26 (mild critical), August: 227.27 (bad/severe critical), September: 37.78 (mild critical), October 12.49 (good), November: 30.26 (mild critical) and December 9.79 (good).

The Role of Demand Forecasting Analysis (Case Study: Bio Farma for Papua Area)

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Bio Farma as the only vaccine manufacturer in Indonesia, divides its marketing area for vaccine distribution throughout Indonesia, represented by marketing representatives in 34 provinces in Indonesia. Segmentation based on geography for vaccine products is divided into five regions, one of the region is Papua. The forecast method used in Bio Farma for Papua Area is still manual. Marketers order vaccines from central Bio Farma and make forecasts if product stock is empty. If the product is empty, a buffer stock will be created. With the buffer stock system that has been implemented so far, there are often problems with excess product which causes the product to expire. From the data, the total loss due to overstock is Rp. 14,161,693 in 2022. If consumer demand falls short of expectations, it will definitely have an impact on the manufacturing of high inventory value, even leading to lost opportunities for sales. That is the fundamental issue with this research. The goal of this research is to identify the possible causes for the overstock and to identify a suitable solution for those issues.

There are five things consist of root causes the problem; (1) fully manual forecasting, (2) low sales forecast accuracy, (3) sales forecasting based on sales of the last one or two months, (4) lack of employee knowledge about sales forecasting, (5) significant gap between target and actual sales. The alternate strategy recommended is to provide a forecasting technique that is suitable for the company. Different forecasting techniques were selected to be compared in order to determine which may be used to improve forecasting accuracy. The forecasting techniques that are employed are the 3-month simple moving average, the 5-month simple moving average, the 3-month weighted moving average, the 5-month weighted moving average, and exponential smoothing. By using the MAD and MAPE measurement tools, exponential smoothing showed the most acceptable accuracy result.