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All Journal Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT
Bhakti Helvi Rambe
Akuntansi, Fakultas Ekonomi dan Bisnis, Universitas Labuhanbatu
Aerospace Engineering Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Public Health

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Penyuluhan Membangun Wirausaha Di Desa Tanjung Siram Produk Sabun Cuci Piring Fauziah Hanum; Christine Herawati Limbong; Bhakti Helvi Rambe; Nur Ainun Gulo; Ibnu Rasyid Munthe; Syaiful Zuhri Harahap
JURNAL PKM IKA BINA EN PABOLO Vol 3, No 1: PENGABDIAN KEPADA MASYARAKAT | JANUARI 2023
Publisher : IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ikabinaenpabolo.v3i1.3746

Abstract

Kegiatan pengabdian masyarakat di Desa Tanjung Siram dilaksanakan pada tanggal 27 September 2022. Penyuluhan dan pelatihan ini berlangsung di aula kantor kepala desa Tanjung Siram. Peserta meliputi unsur masyarakat desa Tanjung Siram terkhusus kaum ibi-ibu, dosen dan mahasiswa. Kegiatan pengabdian masyarakat ini berjalan dengan baik dan lancar sesuai dengan yang direncanakan, hal ini terlihat dari antusias dan semangat wargaa dalam mengikuti pelatihan Pembuatan sabun pencuci piring. Tujuan dilakukannya pengabdian kepada masyrakat ini adalah untuk memberikan penyuluhan, pelatihan, dan praktek tentang pembuatan sabun pencuci piring dalam rangka membantu warga desa Tanjung Siram dalam menciptakan peluang usaha baru bagi warga serta mengurangi beban pengeluaran   warga dalam melakukan pembelian sabun pencuci piring.  Metode yang digunakan dalam pengabdian kepada masyarakat ini adalah dengan berdiskusi, memaparkan dan mempraktikkan cara pembuatan sabun pencuci piring.
Rekayasa Fitur dan Gradient Boosting untuk Prediksi Harga Saham Pada Pasar Saham Indonesia Bhakti Helvi Rambe; Ibnu Rasyid Munthe; Fauziah Hanum; Anita Sri Rejeki Hutagaol
Journal of Computer Science and Information System(JCoInS) Vol 7, No 1: JCoInS | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i1.8945

Abstract

This study aims to analyze the comparative performance of three machine learning models Neural Network, Random Forest, and XGBoost in predicting the stock price of Bank Rakyat Indonesia (BBRI.JK) based on feature engineering integration. The background of this study is based on the need to develop accurate and efficient predictive models to deal with stock market volatility. The Data used covers the period 2010-2025 with the application of technical indicators such as Moving Average (MA), Relative Strength Index (RSI), volatility, and price momentum as the main features. The research method uses a machine learning approach based on supervised learning with a five-fold cross validation process. Model evaluation was conducted using quantitative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Absolute Percentage Error (MAPE). The results showed that XGBoost produced the Best Performance With R2 = 0.9451, MAE = 87.3129,and MSE = 10327.1187, followed by Random Forest (R2 = 0.9233) and Neural Network (R2 = 0.9120). The XGBoost Model proved to be the most stable and efficient in handling nonlinear data as well as extreme price fluctuations. The discussion confirms that the integration of engineering features improves the generalization capability of the model and lowers the prediction error rate significantly. Future research is recommended to include macroeconomic variables, sentiment data, and reinforcement learning approaches to broaden the scope and improve the model's adaptability to global financial market dynamics.
Co-Authors Anita Sri Rejeki Hutagaol Christine Herawati Limbong Fauziah Hanum Fauziah Hanum Ibnu Rasyid Munthe Nur Ainun Gulo Syaiful Zuhri Harahap