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All Journal Jurnal Sistem Cerdas Dimasejati: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Journal of Artificial Intelligence and Technology Information Journal of Applied Informatics Science
Kurniawan, Heru Purnomo
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Hybrid Model of Artificial Neural Networks and Flower Pollination Algorithm for Stock Price Prediction Farhatuaini, Lia; Kurniawan, Heru Purnomo; Muslihah, Isnawati
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.433

Abstract

Predicting the future behavior of the stock market is a difficult task due to its complex and ever-changing nature. This study focuses on predicting BBRI stock prices using an Artificial Neural Network (ANN) improved with the Flower Pollination Algorithm (FPA). We found that the model works well with a 9-100-1 setup, achieving accurate predictions with a Root Mean Square Error (RMSE) of 0.127579154. While FPA effectively reduces errors in the initial 10 iterations, it faces challenges in further improvement, especially in responding to sudden changes in stock prices. Despite performing well overall, the model tends to have a wider margin during unexpected market shifts, indicating a need for additional fine-tuning. This research provides valuable insights into stock price prediction, highlighting the importance of refining models to handle unexpected market changes.
Memfasilitasi Penerapan Sistem Terpadu di Klinik Estetika D.A.N Saluky, Saluky; Akbar, Reza Oktiana; Kurniawan, Heru Purnomo
Dimasejati: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 2 (2024)
Publisher : IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70095/dimasejati.v6i2.18073

Abstract

FACİLİTATİNG THE ADOPTİON OF INTEGRATED SYSTEMS AT D.A.N AESTHETİCS CLİNİC, Assistance in implementing an integrated system in clinics and aesthetic services aims to optimize operational efficiency while enhancing service quality. This study focuses on identifying challenges encountered in adopting an information technology system that combines key functions such as patient management, scheduling, electronic medical records, and inventory management. These challenges often relate to staff adaptation to new technology and the need for adequate technical support. The implementation process includes several methods, such as needs analysis, comprehensive staff training, and regular evaluations to assess system effectiveness. Research findings reveal that intensive and continuous assistance plays a crucial role in the successful adoption of this system. Responsive technical support can help reduce psychological and technical barriers to changes in work systems, ultimately increasing acceptance and user satisfaction. The study shows that with proper assistance, clinics can achieve higher efficiency in their daily operations. The system’s implementation contributes to improved quality in aesthetic services. Supporting the use of an integrated system enables clinics to provide more professional and faster services, resulting in greater patient satisfaction. Thus, assistance not only aids in clinical efficiency but also has a positive impact on patient experience and satisfaction, which are the primary goals of healthcare services.
Identifikasi Pola Kepuasan Mahasiswa Terhadap Proses Pembelajaran Menggunakan Algoritma K-Means Clustering. Kurniawan, Heru Purnomo; Farhatuaini, Lia
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6740

Abstract

Student satisfaction levels with the learning experience at higher education institutions often exhibit variability. This study aims to comprehend the varying degrees of student satisfaction at Institut Agama Islam Negeri (IAIN) Syekh Nurjati Cirebon. Employing the K-Means clustering method, this research categorizes students based on their satisfaction levels. The survey data analyzed includes 20 dimensions of Service Quality criteria evaluated by students, with these 20 dimensions grouped into five key aspects of Service Quality assessment: tangible, reliability, responsiveness, assurance, and empathy. The analysis reveals three distinct groups of students with differing satisfaction levels: neutral/fair (class 1), agree/good (class 2), and strongly agree/excellent (class 3). Comparisons among these groups highlight the diversity of student perceptions. Furthermore, an examination of the distribution of evaluations within each class uncovers differing priorities in assessment criteria. These research findings offer insights into the spectrum of student satisfaction levels and pinpoint areas warranting further attention in each class. Such insights can inform the development of policies and strategies aimed at enhancing the quality of learning experiences at IAIN Syekh Nurjati Cirebon.
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.
Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy Ardi Susanto; Heru Purnomo Kurniawan; Lia Farhatuaini; Muhammad Iszul Wilsa; Gina Khayatun Nufus; Ananda Sathria Maulana Amri
Journal of Applied Informatics Science Volume 1 Issue 2 (2025)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v1.i2.85

Abstract

Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.
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 Akbar, Reza Oktiana Ananda Sathria Maulana Amri Ardi Susanto Ardi Susanto Dinda Febrihastatiwi Farhatuaini, Lia Gina Khayatun Nufus Indri Setiawati Muhammad Iszul Wilsa Muhammad Iszul Wilsa Muslihah, Isnawati Nurul Bahiyah Rizki Dewantara Saluky