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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika JIK Jurnal Ilmu Komputer Jurnal Sistem Komputer BERKALA SAINSTEK Prosiding SNATIKA Vol 01 (2011) Journal of Agromedicine and Medical Sciences INFORMAL: Informatics Journal Jurnal Manajemen Kesehatan Indonesia UNEJ e-Proceeding Proceeding of the Electrical Engineering Computer Science and Informatics Management Analysis Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Health Notions Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan CARADDE: Jurnal Pengabdian Kepada Masyarakat SPIRIT Indonesian Journal of Global Health research Journal of Indonesia Road Safety Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Teknik Sumber Daya Air Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Pada Masyarakat JGGAG (Journal of Games, Game Art, and Gamification)
Saiful Bukhori
Lecturer, Faculty Of Computer Science, Universitas Jember
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Implementation of Finite State Machine in Flowchart-Based Visual Programming Game Iqbal Al Mahdi; Saiful Bukhori; Muhammad Ariful Furqon
Journal of Games, Game Art, and Gamification Vol. 10 No. 3 (2025)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v10i3.12133

Abstract

This study explores the implementation of Finite State Machines (FSMs) in a visual programming game based on flowcharts, aimed at enhancing the learning experience of programming concepts. Traditional programming education methods often struggle to engage beginners, leading to the development of interactive and intuitive approaches such as visual programming games. In this context, FSMs are integrated to manage the behavior of game units, allowing for dynamic state transitions based on user-defined flowcharts. The research adopts the Game Development Life Cycle (GDLC) approach, focusing on initialization, pre-production, production, and alpha testing stages. The primary objective is to implement and validate the FSM's effectiveness in controlling unit behavior within the game. Users can design strategies through a drag-and-drop interface, creating flowcharts that translate into FSM models, which dynamically control unit actions during gameplay. Results from the alpha testing indicate that the FSM implementation successfully manages the transitions and behaviors of game units according to the conditions specified in the flowcharts. This demonstrates the technical feasibility and effectiveness of the approach. Although the study does not extend to beta testing and release stages, the alpha testing provides a solid foundation for future research and development focused on user experience and broader feedback.
HETEROGENEOUS GRAPH NEURAL NETWORKS FOR STOCK PRICE PREDICTION: MODELING TEMPORAL AND CROSS-STOCK DEPENDENCIES Bukhori, Hilmi Aziz; Aruchunan, Elayaraja; Anam, Syaiful; Bukhori, Saiful; Maulana, Avin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp0981-1000

Abstract

Stock price prediction remains a challenging task due to the complex interplay of temporal trends and relational dependencies within financial markets. This study proposes the GNN-LSTM Hybrid model, a novel framework that integrates Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) units to simultaneously capture heterogeneous graph structures and temporal dynamics in stock data, leveraging GNNs to model relational dependencies and LSTMs to address long-term temporal patterns, with graph construction based on stock correlation and temporal edge features. Using a dataset covering 1,270 trading days from March 2015 to April 2020, we evaluate the model against traditional methods (ARIMA, LSTM) and modern graph-based approaches (T-GCN, GAT, Transformer-TS, Base GraphSAGE, SAGE-IS). The GNN-LSTM Hybrid achieves superior performance, with a Mean Absolute Error (MAE) of 0.740 (±0.13), Root Mean Squared Error (RMSE) of 1.100 (±0.21), Mean Absolute Percentage Error (MAPE) of 4.92% (±1.16), and Directional Accuracy (DA) of 67.0% (±2.7), and significantly outperforms all baselines, as confirmed by paired t-tests (p < 0.05). Hyperparameter analysis reveals that a configuration of 6 GNN layers and a hidden dimension size of 128 optimizes predictive accuracy, balancing computational efficiency (training time: 16.0 ± 0.7 s) and performance. Validation across 100 training epochs further confirms the model’s robust convergence across all metrics. With an inference time of 20.0 ± 1.0 ms, which is competitive compared to baselines like ARIMA (23.5 ± 1.1 ms) and GAT (20.5 ± 1.0 ms), the GNN-LSTM Hybrid demonstrates strong potential for practical financial forecasting, offering a scalable and accurate solution for capturing the multifaceted dynamics of stock markets, with implications for real-time applications and broader economic modeling.
Co-Authors Ahmad Fauzal Adifia Ahmad Firdaus Ababil Ahmad Firdaus Ababil Al Munawir Anam, M Khairul Ancah Caesarina Novi Marchianti Antonius Cahya Prihandoko Aruchunan, Elayaraja Basbeth, Faishal Bayhaqqi Bayhaqqi Bukhori, Hilmi Aziz Dewi Kholifatul Ummah Dewi Rokhmah Dharmawan, Tio Diah Adistia Diah Adistia A Diah Ayu Retnani Wulandari Fahruddin Arrasyid Alfansuri Faishal Basbeth Feby Indriana Yusuf Feby Sabilhul Hanafi Firman Firman Furqon, Muhammad Ariful FX Ady Soesetijo Gayatri Dwi Santika Gusfan Halik Hairul Anam Hanesya, Arini Farihatul Haryanto, Kurniawan Wahyu Hastungkara, Duhita Husnul Hotimatus s Husnul Hotimatus Sahroh I Ketut Eddy Purnama Iqbal Al Mahdi januar adi putra, januar adi Krisnha Dian Ayuningtyas Lucky Lhaura Van FC, Lucky Lhaura Luh Putu Ratna Sundari Mahamad, Abd Kadir Malik Qilsi, Fatkhur Ruli Markus Apriono Maulana, Avin Maulia Azizah Maulina Azizah Mauridhi Heri Purnomo Mochamad Hariadi Mohammad Ovi Sanjaya Mohammad Zarkasi Muhammad Noor Dwi Eldianto Mustika Rahmasuci Mustika Rahmasuci Nafolion Nur Rahmat, Nafolion Nur Negoro, Wahyu Saptha Nur Kholis Mansur Nuryadi Nuryadi Oktalia Juwita Oktavia, Nelly Puspitarini, Niken Wahyu Putra, Januar Adi PUTRI WULANDARI R., Windi Eka Y. Rebecca La Volla Nyoto Rizqi Alvian, Muhammad Bagus Saon, Sharifah Sari, Meylita Shasha Nur Faadhilah Sonya Sulistyono Sri Hartatik Sri Hernawati Sri Wahyuni Sumijan Sumijan Surmayanti, Surmayanti Syaiful Anam Tio Dharmawan Umroh Makhmudah Vivi Sefrinta Izza Afkarina Wijaya, Angga Ari Wiji Utami Windi Eka Yulia Retnani Yudha Alif Aulia Yudha Alif Auliya Yudhi Tri Gunawan Yunarni, Wiwik