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All Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Informatika UNIKA Santo Thomas Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Applied Data Sciences Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Munthe, Ibnu Rasyid
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Religion Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Forecasting IHSG Stock Prices Using an Attention-Based CNN-BiGRU Hybrid Deep Learning Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Munthe, Shabrina Rasyid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7064

Abstract

This study develops an IHSG stock price forecasting model using a hybrid CNN–BiGRU architecture enhanced by an attention mechanism. The key novelty lies in combining CNN-based local pattern extraction with BiGRU-based bidirectional temporal modeling, while attention selectively emphasizes the most informative time steps, improving representation quality for complex and noisy financial series. Historical IHSG data from public sources were preprocessed through feature engineering and normalization, followed by XGBoost-based feature selection to retain the most predictive variables. Model robustness was assessed in two settings: (i) the full dataset and (ii) a “cleaned” dataset excluding the extreme COVID-19 volatility period. The proposed model achieved strong accuracy, with MAE/RMSE of 0.0125/0.02 on the full dataset and 0.0167/0.03 on the cleaned dataset, while Pearson correlation remained close to 1 in both scenarios, indicating high alignment with actual IHSG movements. A 30-day ahead forecast produced a stable and realistic trend. Overall, the CNN–BiGRU with attention provides an effective and robust approach for capturing multi-scale temporal patterns in IHSG forecasting.
Analysis and Implementation of Linear Regression and Decision Tree Methods to Predict Sales at Rayyan Bakery, Simpang Marbau Hidayah, Natari Dia Alika; Munthe, Ibnu Rasyid; Juledi, Angga Putra; Nasution, Marnis
Sistemasi: Jurnal Sistem Informasi Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.6124

Abstract

The development of information technology and data analytics has encouraged business actors to leverage historical data as a basis for decision-making. In the small and medium enterprise (SME) sector, particularly in the culinary field, the ability to predict sales is a crucial aspect of production planning and stock management to ensure operational efficiency. Rayyan Bakery Simpang Marbau, as a bakery SME, faces challenges due to fluctuating sales that have traditionally been managed based on experience rather than systematic data analysis. The main problem addressed in this study is the absence of a data-driven sales prediction method that can assist the business owner in estimating sales accurately. Therefore, a predictive approach that utilizes historical sales data is required to support managerial decision-making. This study employs linear regression and decision tree methods. The analyzed data consist of historical sales records of Rayyan Bakery Simpang Marbau over a specific period. Linear regression is used to model the linear relationship between sales variables, while the decision tree captures non-linear patterns and produces easily interpretable decision rules. The performance of both methods is analyzed and compared based on the accuracy of the predictions they generate. The results indicate that both linear regression and decision tree methods can be effectively used to predict sales; however, the decision tree provides greater flexibility in capturing fluctuating sales patterns. These findings are expected to assist Rayyan Bakery in production planning and stock management, as well as serve as a reference for applying sales prediction methods in similar SMEs.
Rekayasa Fitur dan Gradient Boosting untuk Prediksi Harga Saham Pada Pasar Saham Indonesia Rambe, Bhakti Helvi; Munthe, Ibnu Rasyid; Hanum, Fauziah; Hutagaol, Anita Sri Rejeki
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.
Klasifikasi Tingkat Stres Mahasiswa Dalam Penyelesaian Tugas Akhir Menggunakan Naïve Bayes Dan K-Nearest Neighbor Pefrianti, Lenni; Munthe, Ibnu Rasyid; Irmayanti, Irmayanti; Masrizal, Masrizal
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.v7i1.9060

Abstract

This study aims to analyze the stress levels of final-year students and compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in stress classification. Data were collected from 82 respondents through a questionnaire consisting of seven variables (S1–S7) measuring factors contributing to stress, which were classified into low, moderate, and high stress levels. The results show that both algorithms can classify student stress effectively, with Naïve Bayes achieving the highest accuracy (90.15%) compared to KNN (87.72%). Distribution analysis by study program indicates that Agrotechnology has the highest proportion of students with high stress (42.86%), followed by Information Systems (40.63%) and Information Technology (13.64%). This study provides insights for the university to offer targeted support through counseling or stress management workshops.
Co-Authors Aditya, Nanda Ah, Rahma Muti Amanda, Ade Trya Amansyah, Rizky Anita Sri Rejeki Hutagaol, Anita Sri Rejeki Aritonang, Yuliana Br Azhari Lubis, Haldina Syahfitri Aziz, Tengku Irwan Fauziah, Ridha Febriyanti, Ade Eka Fitriandika Sari, Novi Gaja, Eggi Ok Pernanda Gulo, Nur'Ainun Hanum, Fauziah Harahap, Tongku Hamonangan Harist N, Abdul Hasibuan, Mila Nirmala Sari Helvi Rambe, Bhakti Hidayah, Natari Dia Alika Ichlas, Al Irmayani Irmayani Irmayani, Deci Irmayanti Irmayanti, Irmayanti Juledi, Angga Putra Lashiyanti, Allbila Rahajeng Lumban Gaol, Tid Verawati Maulam, Zevi Adam Muhammad Halmi Dar Muliani, Sonia Sri Munthe, Shabrina Rasyid Mutia Mutia, Mutia Nasution, Fitri Aini Nasution, Marnis Nurul Pratiwi Pane, Dinda Nurinayah Pane, Rahmadani Pefrianti, Lenni Pertiwi, Nur Fajar Kurnia Petromak Sinaga, Gandhi Naek Putri, Kirana Dwi Rambe, Bhakti Helvi Rasyid Munthe, Shabrina Richi Andrianto Ritonga, Irna Alfaini Rusmiana, Rusmiana SANDI ARDIANSYAH Sari, Miranda Sari, Novi Fitriandika Sihite, Holong Marhula Sihombing, Volvo Simamora, Rikardo Lasroha Sipahutar, Rizka Nurfatni Siregar, Siti Kholijah Sitepu, Melda Betaria Sitompul, Joni Awendri Sitorus, Anggiat Selamat Suci Ramadhani, Suci Syaiful Zuhri Harahap Syaputra, Panca Tiara, Dewi Tomi Hidayat, Tomi Tompul, Fahreza Br Tonny, Tonny Trya Amanda, Ade Wiranti, Dea