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All Journal Jurnal Ilmu Pengetahuan dan Teknologi (IPTEK) Jurnal Kecerdasan Buatan dan Teknologi Informasi
Sumiarti Andri
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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PEMETAAN TANAH WAKAF MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS DI KECAMATAN RANGKASBITUNG: Pemetaan Tanah Wakaf Menggunakan Sistem Informasi Geografis di Kecamatan Rangkasbitung Sumiarti Andri; Fairuz Zahirah
JURNAL ILMU PENGETAHUAN DAN TEKNOLOGI (IPTEK) Vol. 5 No. 2 (2021): Jurnal Ilmu Pengetahuan dan Teknologi (IPTEK)
Publisher : Institut Teknologi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31543/jii.v5i2.183

Abstract

Badan Wakaf Indonesia (BWI) merupakan organisasi yang dibentuk untuk membantu memajukan pengelolaan tanah wakaf di Indonesia. BWI dapat membentuk perwakilan di provinsi, kabupaten atau kota sesuai dengan kebutuhan. Kecamatan Rangkasbitung merupakan salah satu wilayah yang berada di bawah naungan BWI perwakilan Kabupaten Lebak. Pendataan tanah wakaf di Kecamatan Rangkasbitung saat ini belum terorganisir dengan baik. Masih banyak tanah wakaf yang belum memiliki akta wakaf dan belum dikelola dengan baik. Hal ini menimbulkan kesulitan dalam pencarian lokasi tanah wakaf tersebut. Untuk membantu penyelesaian permasalahan tersebut maka perlu dibangun aplikasi Sistem Informasi Geografis (SIG) Pemetaan Tanah Wakaf berbasis Web di Kecamatan Rangkasbitung. Metodologi yang digunakan dalam membangun sistem ini adalah prototype. Aplikasi ini dirancang berbasis web dengan menggunakan framework Codeigniter, GeoJSON dan LeafletJS, untuk database menggunakan MySQL. Hasil dari analisis persebaran ini berupa peta digital web untuk pengetahui informasi lokasi tanah wakaf secara lebih tepat, sehingga dapat membantu untuk melakukan pencarian lokasi tanah wakaf menjadi lebih mudah. Kata Kunci : GeoJSON, LeafletJS, SIG,Wakaf, Web
WALMART PRICE PREDICTION USING HOLT-WINTERS FORECASTING Melani Indriasari; Soleh, Muhamad; 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 Fairuz Zahirah Melani Indriasari Muhamad Ramli Muhamad Soleh SUNARTO