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All Journal Faktor Exacta Jurnal Kecerdasan Buatan dan Teknologi Informasi
Sumiarti Andri
Institut Teknologi Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Pengembangan Prototype Sistem Infomasi Makam berbasis Mobile untuk memudahkan Masyarakatan dalam proses Pemakam Katri Widayani; Nunie Nurida; Sumiarti Andri
Faktor Exacta Vol 12, No 1 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i1.3273

Abstract

The Public Cemetery (TPU) in DKI Jakarta is managed by the South Jakarta Funeral Service tribe under the DKI Jakarta Cemetery. The area of DKI is around 65,000 hectares, inhabited by 9.9 million inhabitants. The area of the tomb is 598 ha consisting of 78 tombs, with a mortality rate of 0.64%, the tomb area is very critical. To overcome the very high mortality rate and limited land use a ride system, that is, one tomb can be filled with more than one body. Currently the tomb management system in DKI Jakarta is generally still manual, if any family member dies, they must come to the TPU to fill out the registration form, choose an empty tomb. With road conditions in Jakarta that are often jammed, this is very difficult for people who will use the tomb. the grave manager also had difficulty informing the public about the funeral, as well as the heirs whose lease had expired. Therefore, it is very necessary to develop a mobile-based grave management information system, with the aim that place reservations and desired location information, ordering procedures can be easily and quickly accessed via the internet or mobile phone. For this reason, a funeral software prototype was developed with the object of Tanah Kusir TPU research, which is one of the largest public cemeteries in Jakarta, with an overall area of 598 hectares containing 1,776 tombs, located on Jalan Bintaro Raya, South Jakarta. The goal is to make it easier for heirs / communities to search for mobile-based graves, Prototyping methods, with details of activities: a). Running system analysis and system requirements, b) System design includes interface, data and process design with the aim of producing specifications that are appropriate , c) Coding, is the stage of translating the results of analysis and design into the PHP programming language for websites and Java for mobile, d) Testing (Testing), for the system that has been built. Two types of testing are carried out, namely testing for software and user acceptance testing conducted by the tomb manager.
WALMART PRICE PREDICTION USING HOLT-WINTERS FORECASTING Melani Indriasari; Muhamad Soleh; Muhamad Ramli; Sunarto; Sumiarti Andri
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.438

Abstract

Stock price prediction remains a complex challenge due to the volatile, noisy, and nonlinear nature of financial markets. This study aims to evaluate the effectiveness of the Holt-Winters Exponential Smoothing (HWES) method in forecasting the stock price of Walmart Inc. (WMT) and its application in investment decision-making. Historical monthly closing price data from January 2020 to December 2024 were collected and used to build an additive Holt-Winters model. The model was validated using out-of-sample data from January to February 2025, achieving RMSE of 4.535 USD and MAE of 4.801 USD, indicating good short-term predictive performance. The model was then used to forecast stock prices from March 2025 to December 2026, revealing a consistent upward trend with moderate seasonal fluctuations. However, deviations between predicted and actual values were observed during periods of market volatility, particularly in late 2025. To further evaluate performance, the Holt-Winters model was compared with the ARIMA model. Results show that ARIMA outperformed Holt-Winters in short-term forecasting with lower RMSE (4.71), MAE (4.26), and MAPE (4.21%), while Holt-Winters was more effective in capturing seasonal patterns. An investment simulation using a Dollar Cost Averaging (DCA) strategy combined with technical analysis indicators produced a total return of 3.45%, supported by both capital gains and dividend income. These findings suggest that while Holt-Winters provides a simple and interpretable approach for long-term forecasting, its performance can be improved by integrating adaptive models and external factors such as market sentiment and macroeconomic conditions for more robust predictions.
Co-Authors Katri Widayani Melani Indriasari Muhamad Ramli Muhamad Soleh Nunie Nurida Sunarto