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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jambura Journal of Mathematics BERNAS: Jurnal Pengabdian Kepada Masyarakat Engineering Science Letter Journal of Novel Engineering Science and Technology
Fandi Presly Simamora
Universitas Mikroskil
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham Simamora, Fandi Presly; Purba, Ronsen; Pasha, Muhammad Fermi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27166

Abstract

The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test results show that Bayesian Optimization was able to find the optimal hyperparameter configuration for the BiLSTM model, with the best MAPE values for each stock: BBCA 1.2092%, BYAN 2.0609%, and TLKM 1.2027%. Compared to previous research on Grid Search-BiLSTM, the use of Bayesian Optimization-BiLSTM resulted in lower MAPE values.
Random Forest Optimization Using Recursive Feature Elimination for Stunting Classification Marpaung, Sophya Hadini; Sinaga, Frans Mikael; Rambe, Khairul Hawani; Simamora, Fandi Presly; Kelvin, Kelvin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35295

Abstract

Stunting is still a major health problem in Indonesia, with a prevalence of 27% in toddlers in 2023, far from the WHO target of below 20%. RSU Mitra Medika Tanjung Mulia in Medan serves patients with various socio-economic backgrounds, which affects the quality of services, including stunting detection. Conventional methods are prone to bias and error. This study used the Random Forest algorithm and the Recursive Feature Elimination (RFE) feature selection method to improve the accuracy of stunting classification. After data preprocessing and feature selection, two main variables were identified, namely age and height. The initial Random Forest model achieved an accuracy of 94.38%, which increased to 94.42% after hyperparameter tuning. The results showed that this approach produced an accurate, efficient model that can be integrated into clinical systems, helping medical personnel identify children at risk of stunting quickly and accurately, increasing the effectiveness of interventions, and supporting government efforts to reduce the prevalence of stunting
Pelatihan Pemanfaatan Mentimeter untuk Mendukung Pembelajaran Aktif pada Guru SMK Methodist Tanjung Morawa Manurung, Juliana Damayanti; Gunawan; Simamora, Fandi Presly; Hita; Ivan Dika Lesmana
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17324

Abstract

Transformasi pembelajaran menuju pembelajaran aktif menjadi tuntutan penting dalam implementasi Kurikulum Merdeka. Namun, pemanfaatan teknologi pembelajaran interaktif oleh guru sekolah menengah kejuruan masih relatif terbatas, sehingga partisipasi siswa dalam pembelajaran belum optimal. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan guru SMK Methodist Tanjung Morawa dalam menerapkan pembelajaran aktif melalui integrasi platform Mentimeter. Metode yang digunakan adalah pelatihan partisipatif berbasis praktik dengan pendekatan pre-test dan post-test. Kegiatan dilaksanakan di Laboratorium Komputer Universitas Mikroskil dan melibatkan sembilan orang guru dari berbagai mata pelajaran. Hasil kegiatan menunjukkan adanya peningkatan nilai rata-rata post-test dibandingkan pre-test, yang mengindikasikan peningkatan pemahaman peserta terhadap konsep pembelajaran aktif dan pemanfaatan Mentimeter. Selain itu, hasil observasi dan umpan balik peserta menunjukkan peningkatan motivasi, kepercayaan diri, serta kesiapan guru dalam mengimplementasikan pembelajaran interaktif di kelas. Kegiatan ini memberikan dampak positif dalam jangka pendek berupa peningkatan literasi digital dan pedagogik guru, serta berpotensi mendukung perubahan praktik pembelajaran yang lebih partisipatif dan berpusat pada siswa di lingkungan sekolah mitra.
Peningkatan Kompetensi Digital Guru SMK dalam Menghadapi Tantangan Industri Masa Depan di Kota Medan Simamora, Fandi Presly; Manurung, Juliana Damayanti; Halim, Apriyanto
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17373

Abstract

Revolusi Industri 4.0 menuntut adaptasi signifikan dalam sektor pendidikan vokasi, khususnya dalam penguasaan keterampilan digital. Namun, guru di SMKN 6 Medan dan SMKN 13 Medan menghadapi tantangan dalam mengintegrasikan materi modern seperti berpikir komputasional, analisis data, dan literasi kecerdasan artifisial karena adanya persepsi bahwa kompetensi tersebut bersifat teknis. Kegiatan pengabdian ini bertujuan untuk meningkatkan kompetensi digital dan kesiapan mengajar guru dalam menghadapi tantangan industri masa depan. Metode pelaksanaan menggunakan pendekatan pelatihan partisipatif yang dikombinasikan dengan praktik langsung memanfaatkan perangkat lunak draw.io, bahasa pemrograman Python, dan platform kecerdasan artifisial generatif. Kegiatan dilaksanakan dalam tiga sesi intensif yang mencakup pemaparan konsep, bedah studi kasus, dan evaluasi terukur melalui tes awal dan tes akhir. Hasil pengabdian menunjukkan peningkatan kompetensi yang signifikan pada seluruh peserta. Rata-rata skor pemahaman analisis data melonjak dari 41,25 menjadi 80,00, literasi kecerdasan artifisial meningkat dari 85,60 menjadi 95,60, dan berpikir komputasional naik dari 54,63 menjadi 73,15. Kesimpulannya program pembekalan ini terbukti efektif menjembatani kesenjangan keterampilan teknis guru dan mengubah paradigma pengajaran menjadi lebih adaptif. Program ini direkomendasikan untuk berkelanjutan dengan melibatkan siswa guna menciptakan ekosistem pendidikan digital yang holistik.
Forecasting Rice Price Volatility Utilizing BiLSTM-SHAP to Ensure National Food Stability Manurung, Juliana Damayanti; Sikana, Nadya; Simamora, Fandi Presly; Manurung, Zoni Zikro
Engineering Science Letter Vol. 4 No. 03 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001360

Abstract

Rice price volatility in Indonesia remains a persistent economic issue, partly driven by climate variability and fluctuations in national rice production, prompting the government to resort to substantial annual imports. However, the extent to which domestic production factors and weather conditions influence future rice prices has not been quantitatively evaluated. This study aims to forecast short-term rice prices in Indonesia by integrating multiple time-series features, including rice prices, harvested area, paddy production, and weather features, using a Bidirectional Long Short-Term Memory (BiLSTM) network. Daily data from 2013 to 2024 were collected from the National Statistics Agency, Food Price Panel, and the Meteorology and Climatology Agency. Chronological split was applied for training, validation, and testing to preserve temporal dependency. The optimal model predicts rice prices seven days ahead using 256 hidden units, achieving MAE of 128.84 IDR, RMSE of 157.98 IDR, and R² of 0.694. SHAP analysis shows that historical rice prices have the strongest contribution with a SHAP value of 0.969652, significantly higher compared to other features. The results demonstrate that integrating agricultural and climatic inputs improves predictive performance while providing interpretable insights into price-forming factors.
Optimizing Rice Planting Schedules Based on Rainfall Prediction Using a BiLSTM Network Simamora, Fandi Presly; Rambe, Khairul Hawani; Marpaung, Sophya Hadini
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1340

Abstract

This study addresses the critical challenge of optimizing rice planting schedules in Indonesia, where unpredictable rainfall threatens national and regional food security. To tackle this issue, a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed to accurately predict rainfall patterns, with a specific focus on Deli Serdang Regency in North Sumatra. Utilizing a comprehensive weather dataset from 2013 to 2022 sourced from BMKG, a feature selection process was conducted to identify the 10 most influential features for rainfall. The BiLSTM model was then developed through several experimental scenarios, varying the data duration and architectural complexity. The best-performing model, achieved in a scenario using a double BiLSTM architecture and 10 years of data, yielded a Mean Absolute Error (MAE) of 11.2382 mm and a Root Mean Squared Error (RMSE) of 19.5650 mm. The resulting predictive capability provides a data-driven framework for optimizing planting schedules. Crucially, the study also reveals the limitations of current planting criteria, which can be misleading in regions prone to intense, short-duration rainfall, highlighting the need for more adaptive, region-specific guidelines. This work contributes to mitigating crop failure risks, enhancing crop resilience, and ensuring long-term regional food security.
Co-Authors Apriyanto Halim Gunawan Hita Ivan Dika Lesmana Kelvin Kelvin Manurung, Juliana Damayanti Manurung, Zoni Zikro Nadya Sikana Pasha, Muhammad Fermi Rambe, Khairul Hawani Ronsen Purba Sinaga, Frans Mikael Sophya Hadini Marpaung