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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Elmayati
Universitas Bina Insan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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pelatihan Menggambar dan Mewarnai Menggunakan Microsoft Paint untuk meningkatkan kreativitas anak-anak Selviyani; Harma Oktafia Lingga Wijaya; Elmayati; Wisdalia Maya Sari; Joni Karman; Rani Purnama Aprisusanti
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2.1 Desember (2022): SPECIAL ISSUE
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.855 KB)

Abstract

This community service activity (PKM) was carried out with the aim of providing an understanding of the importance of technology, as well as providing training to improve basic skills and the use of fundamental computer applications, such as Paint, to the children of the Muara branch kel. Belalau II City of Lubuklinggau. The method of implementing this activity is carried out using the lecture and discussion method between participants and presenters, as well as how to combine children's interest in drawing with today's rapid technological developments so that children can improve their drawing skills with current technological developments. The result of this activity is an increase in participants' understanding of the basics of computer operation and participants' skills in using fundamental computer applications, including the use of the paint application as a tool for making drawings
ANALISIS SENTIMEN APLIKASI E-GOVERENMENT PADA GOOGLE PLAY MENGGUNKAN ALGORITMA NAÏVE BAYES Rini Sartina; Elmayati; Nelly Khairani Daulay
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01 APRIL.29

Abstract

E-gov is one of the innovations created by the government to be able to compete in the digital world. One government agency that has used e-gov is the Ministry of Spatial Planning/National Land Agency (ATR/BPN). in this study the authors used the Naïve Bayes Algorithm as a classification method to determine positive, negative, and also neutral sentiments. The labeling process shows that the touch my land application tends to get a neutral response which can be seen from the comparison of 4000 data, 2265 positive, 1453 negative and 211 neutral data. and the results of sentiment analysis testing using the Naïve Bayes Algorithm produce an accuracy of 81%, 78% precision, and 79% recall.
Vector Autoregression, Long Short-Term Memory, dan Xgboost Dalam Kerangka Ensemble Eddy Kurniawan; Budi Santoso; Elmayati
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 2 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i2.4028

Abstract

Stock market volatility is a crucial indicator for risk assessment and investment decision-making, particularly for the S&P 500 index as a proxy for global financial markets. This study aims to forecast S&P 500 volatility by integrating macroeconomic and technical indicators using an ensemble approach. Daily data from March 18, 2015 to January 8, 2026 are employed. The input variables include macroeconomic indicators such as the Federal Funds Rate, Consumer Price Index, US Dollar Index, crude oil prices, and the Volatility Index (VIX), as well as technical indicators including log returns, realized volatility, moving averages, Relative Strength Index, and Moving Average Convergence Divergence. Realized volatility is computed using a 21-day rolling window and annualized. The proposed framework combines Vector Autoregression, Long Short-Term Memory networks, and XGBoost within an ensemble scheme. Empirical results indicate that the ensemble model produces more stable forecasts and outperforms classical econometric models. These findings highlight the effectiveness of combining macroeconomic and technical information for volatility forecasting and stock market risk management.
Co-Authors Budi Santoso Eddy Kurniawan joni karman Lingga Wijaya, Harma Oktafia Nelly Khairani Daulay Rani Purnama Aprisusanti Rini Sartina Selviyani Wisdalia Maya Sari