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All Journal Jurnal Sistem Cerdas Infotek : Jurnal Informatika dan Teknologi Journal of Artificial Intelligence and Technology Information
Muhammad Iszul Wilsa
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Comparative Analysis of Hybrid ARIMA-LSTM against Statistical and Machine Learning Benchmarks for Commodity Stock Muhammad Iszul Wilsa; Heru Purnomo Kurniawan; Rizki Dewantara; Dinda Febrihastatiwi; Indri Setiawati
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.260

Abstract

Predicting stock prices in Indonesia’s commodities and energy sectors is a complex challenge due to high volatility influenced by global market dynamics and macroeconomic factors. This study aims to test the robustness of the ARIMA-LSTM hybrid model in predicting closing stock prices for six major issuers: ADRO, PTBA, MEDC, ANTM, MDKA, and AALI. The proposed approach employs a dual-input strategy that integrates 27 technical indicators with the linear residuals from the ARIMA model. The research methodology begins with data decomposition using the ARIMA model to capture linear components, followed by modeling the residuals using Long Short-Term Memory (LSTM) to capture complex non-linear patterns. The experimental results show that the hybrid model consistently delivers the best performance compared to single models such as ARIMA, Random Forest, and Single LSTM across all test datasets. In the 1-step-ahead scenario, the hybrid model achieved the lowest average MAPE of 2.20%, while in the 5-step-ahead scenario, the error rate remained at 3.98%. A key finding of this research is the hybrid architecture’s ability to mitigate the extreme overfitting experienced by the Single LSTM model, while providing better prediction stability against variations in issuer characteristics. This study concludes that the integration of statistical decomposition and deep learning provides a reliable framework for investors and analysts to make data-driven decisions amid the volatile fluctuations of the Indonesian capital market.
Perancangan dan Implementasi Sistem Informasi Manajemen Operasional dan Penggajian Berbasis Web Menggunakan Model Waterfall Suhadi Parman; Gina Khayatun Nufus; Sokid; Kahfi Gunardi; Muhammad Iszul Wilsa
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35288

Abstract

Micro, Small, and Medium Enterprises such as E-Donuts face challenges in daily operational management, sales recording, packaging and donut stock management, and employee payroll because they still rely on manual processes. This study aims to design and implement EdoBest, a web-based operational management and payroll application developed using the Waterfall model. The development process includes planning, requirement analysis through interviews and observation, UML-based system design and responsive interface design, implementation using Laravel 12 and MySQL, black-box testing, and maintenance. The novelty of EdoBest lies in the integration of culinary MSME operational modules into a single system, including delivery batch management, multi-outlet management, packaging and donut stock management, employee activity recording, cash advance management, report validation, and automatic payroll calculation based on daily operational data. The quantitative testing results show that 16 functional test scenarios, consisting of 10 Admin scenarios and 6 Employee scenarios, were successfully executed with a 100% success rate. This research contributes a functionally tested design and implementation of an integrated information system that improves recording accuracy, reporting efficiency, real-time monitoring, and decision-making support for multi-outlet culinary MSMEs.
Elite-Refined Genetic Algorithm with Hill Climbing Local Search for University Course Scheduling Heru Purnomo Kurniawan; Lia Farhatuaini; Nurul Bahiyah; Ardi Susanto; Muhammad Iszul Wilsa; Gina Khayatun Nufus
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.584

Abstract

Abstract— This paper proposes a hybrid optimization approach combining Genetic Algorithm (GA) and Hill Climbing (HC) to address the university course scheduling problem in the Informatics Study Program at Universitas Islam Negeri Siber Syekh Nurjati Cirebon. The hybrid GA-HC model integrates GA’s global exploration capability with HC's local refinement strategy to minimize hard and soft constraint violations while achieving balanced timetables. The dataset includes 56 course classes, 18 lecturers, and three rooms, with scheduling over five working days and 11 time slots per day. Experimental results demonstrate that GA-HC outperforms pure GA and pure HC in convergence speed, average fitness, and stability of feasible solutions. Parameter tuning analysis further shows that moderate mutation rates and limited HC iterations yield optimal trade-offs between runtime and solution quality. The proposed hybrid framework effectively enhances convergence, reduces conflicts, and improves overall timetable quality, confirming its robustness for large-scale academic scheduling problems.
Co-Authors Ardi Susanto Dinda Febrihastatiwi Farhatuaini, Lia Gina Khayatun Nufus Indri Setiawati Kahfi Gunardi Kurniawan, Heru Purnomo Nurul Bahiyah Rizki Dewantara Sokid Suhadi Parman