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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) U-NET Jurnal Teknik Informatika
Munthe, Shabrina Rasyid
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Computer Science & IT Engineering

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SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN SELEDRI DENGAN METODE FORWARD CHANING DI KELOMPOK TANI DESA SIAMPORIK: Forward chaining Jalampiran Pasaribu, Putri delviana; Kusmanto; Munthe, Shabrina Rasyid
U-NET Jurnal Teknik Informatika Vol. 8 No. 2 (2024): U-NET Jurnal Teknik Informatika | Agustus
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v8i2.872

Abstract

This research is motivated by problems in Siamporik Village who have good celery planting skills, but lack knowledge about this celery plant disease. In the village of Siamporik, the skill of growing celery is due to the lack of detecting celery disease which makes the harvest unsatisfactory. To reduce yield losses, an expert system for diagnosing sop leaves (celery) was created so that it can detect plant diseases, using the Forward Channeling method, using the Web as an application, and using MySQL and Xampp databases.
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.
Co-Authors Jalampiran Pasaribu, Putri delviana Kusmanto Munthe, Ibnu Rasyid Rambe, Bhakti Helvi