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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Terpadu Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Prosiding National Conference for Community Service Project Jurnal E-Komtek JITU : Journal Informatic Technology And Communication Journal of Innovation Information Technology and Application (JINITA) Telcomatics Journal of Information System and Technology (JOINT) Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Community Engagement Project (Concept) Conference on Business, Social Sciences and Technology (CoNeScINTech) Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal ilmiah teknologi informasi Asia
Haeruddin, .
Universitas Internasional Batam
Aerospace Engineering Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Social Sciences Other

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Optimasi Hyperparameter N-BEATS Menggunakan Optuna untuk Prediksi Harga Saham BBCA Haeruddin, Haeruddin; Wijaya, Gautama; Sherly, Sherly
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3517

Abstract

Stock price prediction is crucial for supporting informed investment decisions due to the high volatility of stock prices. One of the key challenges in deep learning–based stock price prediction is determining the right hyperparameters. This research aimed to assess whether hyperparameter tuning with Optuna can enhance the N-BEATS model performance for predicting the stock price of PT Bank Central Asia Tbk. Historical stock price data were used and chronologically divided into training, validation, and testing sets. The efficacy of the models was measured using MAPE, RMSE, and R². The results showed that hyperparameter optimization using Optuna considerably enhanced the performance of N-BEATS, achieving a MAPE of 1.27%, which outperformed the standard N-BEATS model (1.44%) and the LSTM model (1.41%). This study proved that a systematic hyperparameter optimization approach can improve the performance of stock price forecasting models.Keyword: N-BEATS; Optuna; Stock; Predict; BBCA AbstrakPrediksi harga saham menjadi aspek penting dalam mendukung penentuan keputusan investasi karena fluktuasi harga saham yang tinggi. Salah satu tantangan utama dalam pemodelan prediksi harga saham berbasis deep learning adalah penentuan hyperparameter yang tepat. Penelitian ini bertujuan untuk mengevaluasi efektivitas optimasi hyperparameter menggunakan Optuna dalam meningkatkan kinerja model N-BEATS untuk prediksi harga saham PT Bank Central Asia Tbk. Data historis harga saham digunakan dan dibagi secara kronologis menjadi data latih, validasi, dan uji. Evaluasi kinerja akhir dilakukan menggunakan metrik MAPE, RMSE, dan R². Hasil penelitian menunjukkan bahwa optimasi hyperparameter dengan Optuna mampu meningkatkan kinerja N-BEATS dengan nilai MAPE sebesar 1.27% dibandingkan dengan N-Beats standar (MAPE 1.44%) dan LSTM (MAPE 1.41%). Penelitian ini menunjukkan bahwa pendekatan optimasi hyperparameter yang sistematis efektif dalam meningkatkan kinerja model prediksi harga saham. 
Pengembangan Sistem Prediksi Harga Saham Berbasis Web Menggunakan Model LSTM dan CNN–LSTM Haeruddin, Haeruddin; Rinaldo, Rinaldo; Gautama, Gautama
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3518

Abstract

This study discussed the challenges of stock price prediction, which is volatile and exhibits non-linear patterns, as well as the need for an easily accessible system to present prediction results quickly. A web-based application was developed to predict the closing prices of Indonesian non-banking stocks (ASII, TLKM, and UNVR) by utilizing daily market data that were updated periodically through an application programming interface. The integrated pre-trained models used were long short-term memory (LSTM) and convolutional neural network–long short-term memory (CNN-LSTM), which were integrated into the application for one-step-ahead (t+1) inference. The system development methodology followed the software development life cycle waterfall model. Functional testing using a black-box testing approach showed that the core features ran according to the requirements, so the application was considered suitable as a web-based medium for prediction and visualization.Keywords: Stock price prediction; Web application; Deep learning; Application programming interface; Non-banking stocksAbstrakPergerakan harga saham yang volatil dan non-linear menuntut pendekatan prediksi yang adaptif serta sistem yang mudah diakses. Penelitian ini bertujuan untuk mengembangkan suatu sistem prediksi harga penutupan saham dan menyajikannya secara cepat. Penelitian ini telah mengembangkan aplikasi berbasis web untuk prediksi harga penutupan saham emiten non-perbankan Indonesia (ASII, TLKM, dan UNVR) dengan memanfaatkan data pasar harian yang diperbarui berkala melalui application programming interface. Integrasi model terlatih yang digunakan yaitu long short-term memory (LSTM) dan convolutional neural network-long short-term memory (CNN-LSTM), yang diintegrasikan ke dalam aplikasi untuk proses inference satu langkah ke depan (t+1). Metodologi pengembangan sistem mengikuti software development life cycle model Waterfall. Sistem yang dikembangkan telah berfungsi sesuai dengan kebutuhan berdasarkan hasil black-box testing. 
Co-Authors ., Arron Agusyanto . Alzi Alzi Andy Lau Ari Prayoga Hutabarat Arif Kurniadi Aripradono, Heru Wijayanto Arron Arron Avista Mindy Bobby Fernando Pangaribuan Boby Candra Deven Lee Efendi Efendi Elia, Elia Elvis, Elvis Erick Erick Febrison Yohaness Felix Felix Firman Adiyasa Frans Hadinata Gautama Wijaya Gautama, Gautama Hadi Hadi Hendra Winata Hendri Hendri Herman Herman Hermanto Hermanto Husnul Khatimah Husnul Khatimah Jefferson Richtan Jemmy Jemmy Kelvin Kelvin Leo M. Nahrul Indriawan Moezes Rasuanda Muhammad Nur Faiz Patrick Pratama Hendri Prasetyo, Stefanus Eko Rahman Syahputra Ricardo Ricardo Rinaldo, Rinaldo Sepbianto Sepbianto Sherly, Sherly Sukma Aji Syaeful Anas Aklani, Syaeful Tiara Tiara Tina Tina Vincent . Vivian Vivian Viviany Viviany Wijaya, Gautama Windi Afdal, Windi