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All Journal Media Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Electronic Integrated Computer Algorithm Journal
Abdulmana, Sahidan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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FORECAST EVALUATION OF ARIMA AND ANFIS FOR INDONESIA'S MONTHLY EXPORT (2009-2024) Septiarini, Tri Wijayanti; Rofiqo, Azidni; Pariyanti, Eka; Abdulmana, Sahidan
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.93-104

Abstract

Indonesia’s export sector is a key driver of economic growth, contributing significantly to foreign exchange, employment, and industrial development. Accurate forecasting of export trends is crucial for policymakers, economists, and businesses in shaping strategies and reducing risks. This study applies the Autoregressive Integrated Moving Average (ARIMA) model to forecast Indonesia’s monthly export values from January 2014 to August 2024. Dataset has been divided into training (75%) and testing (25%) subsets, and the Box-Jenkins methodology was employed, including stationarity testing, identification via ACF and PACF plots, parameter estimation, and residual diagnostics. The optimal ARIMA(1,1,1) model achieved strong predictive performance in RMSE, MSE, and MAPE. To benchmark classical methods against modern approaches, ARIMA was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Results indicated that ARIMA delivered higher accuracy for this dataset, reaffirming the robustness of traditional models when data characteristics align with their assumptions. It has conducted prior research evaluation via 75%:25% holdout and rolling-actual back test. This research demonstrates that classical time-series models remain highly relevant in the era of artificial intelligence, emphasizing the importance of appropriate model selection in economic forecasting.
Heart Attack Risk Prediction Using Machine Learning: A Comparative Study of Decision Tree and K-Nearest Neighbors Hizbullah, Fauzi; Noorachmad Muttaqin, Alif; Andiharsa Sih Setiarto, Rahardian; Aulia Hakim, Rizki; Abdulmana, Sahidan
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.98

Abstract

Heart disease, particularly heart attacks, is a leading cause of death worldwide, highlighting the importance of early detection and risk prediction. This study develops and evaluates machine learning models to predict heart attack risk using seven health-related attributes: age, marital status, gender, body weight category, cholesterol level, participation in stress management training, and stress level. The dataset, processed with the Orange Data Mining platform, was divided into training (66%) and testing (34%) sets. Two supervised algorithms, Decision Tree and K-Nearest Neighbors (K-NN), were implemented without extensive hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, and F1 score. The Decision Tree achieved the best results with 84.78% accuracy, 88.52% precision, 79.41% recall, and 83.72% F1 score, indicating its effectiveness in identifying at-risk individuals. Key predictors included age, stress level, and cholesterol, aligning with established medical findings. While the results are promising, limitations include a small dataset and limited algorithm scope. Future research should expand the dataset, include additional clinical features, and explore advanced algorithms to improve accuracy and reduce false negatives, enhancing applicability in preventive healthcare.
Development of The Means of Engagement Concept on Enterprise Resource Planning User Satisfaction Rafsanjani, Rayhan Rafael; Lubis, Muharman; Abdulmana, Sahidan
Electronic Integrated Computer Algorithm Journal Vol. 1 No. 1 (2023): VOLUME 1, NO 1: OCTOBER 2023
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v1i1.7

Abstract

None of the several theories that underpin the assessment of IT adoption characterizes the process as dynamically as the Means of Engagement theory does. User satisfaction is a key factor in influencing the success of ERP implementation as well as the adoption of the ERP system by the user. Therefore, it is necessary to identify the factors affecting user satisfaction of the ERP system as well as the relationship between customer satisfaction and user involvement. This research aims to develop a concept or model of Means of Engagement (MOE) on relationship domain in particular for satisfaction factor with the research object of PT Glico Indonesia. The research uses SEM-PLS analysis method using the SmartPLS 4 application to construct and test a structural equation model that reflects the relationship between the variables investigated in the research. The evaluation results showed that the five SERVQUAL dimensions studied did not have a significant impact on user satisfaction while customer satisfaction had a significant positive impact on engagement. The results of this research, namely the development of the Means of Engagement model, are expected to be the basis for PT Glico Indonesia to design strategies that can improve and maintain the adoption of ERP system users based on the level of the means of engagement model.
Evaluating IT Delivery Value in the Faculty of Industrial Engineering at Telkom University Using the COBIT 2019 Framework, Domain APO04, for Mapping LAM INFOKOM Standards Fasya, Muhammad Haikal; Lubis, Muharman; Abdurrahman, Lukman; Garcia-Constantino, Matias; Abdulmana, Sahidan; Ramadhani, Rafian
Electronic Integrated Computer Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: APRIL 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v1i2.19

Abstract

In the contemporary landscape, technology holds pivotal significance across diverse domains, including academia and industry. The Faculty of Industrial Engineering at Telkom University faces challenges in optimizing the value derived from its IT investments to meet evolving market demands. To address this, IT governance methodologies are essential, ensuring effective and secure IT utilization aligned with strategic objectives. This study investigates and evaluates the IT delivery value process at the Faculty of Industrial Engineering, Telkom University, using the COBIT 2019 Domain APO04 framework and LAM INFOKOM standards. Data collection involved primary interviews and secondary document analysis. The analysis revealed gaps in assessing emerging technologies and recommending further initiatives. Recommendations span people, process, and technology aspects, aiming to enhance technology evaluation procedures and documentation. This study provides insights into effectively leveraging IT to achieve the Faculty's objectives and enhance decision-making quality.
COMPARISON OF ARIMA, EXPONENTIAL SMOOTHING, AND CHEN-SINGH FUZZY MODELS FOR INFLATION FORECASTING IN ASEAN COUNTRIES Septiarini, Tri Wijayanti; Kharis, Selly Anastassia Amellia; Jayanegara, Anuraga; Abdulmana, Sahidan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0619-0636

Abstract

This study aims to (i) develop predictive models using statistical and fuzzy approaches, and (ii) evaluate their forecasting performance. The data were obtained from www.investing.com for the period 1961 to 2017 and focus on five ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, and Thailand. The statistical models used are Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, while the fuzzy approaches include Chen and Singh fuzzy time series models. The dataset was divided into training and test sets in a 75%-25% proportion. ARIMA models capture trends and autocorrelations in time series data, while Exponential Smoothing uses exponentially weighted averages. Fuzzy models are designed to handle uncertainty and linguistic patterns in data. The results show that Singh’s fuzzy model yields the lowest error for Indonesia, while exponential smoothing and Chen fuzzy time series model demonstrate the same lowest error for Malaysia. For the Philippines, exponential smoothing is most accurate, whereas ARIMA and Singh fuzzy time series achieve the smallest error for Singapore. For Thailand, exponential smoothing and ARIMA perform equally well. However, the robustness of the forecasting model cannot be determined from either statistical or fuzzy methods, highlighting the challenge in determining the most robust model for inflation in the ASEAN region. The 75%-25% data split may also limit the generalizability of the findings. This study contributes a rare cross-country comparison of statistical and fuzzy forecasting methods in the ASEAN context. It highlights the importance of model selection based on country-specific inflation behavior and provides insights for improving forecasting strategies in macroeconomic applications.
Co-Authors Andiharsa Sih Setiarto, Rahardian Anuraga Jayanegara Aulia Hakim, Rizki Eka Pariyanti Fasya, Muhammad Haikal Garcia-Constantino, Matias Hizbullah, Fauzi Lukman Abdurrahman Muharman Lubis Noorachmad Muttaqin, Alif Rafsanjani, Rayhan Rafael Ramadhani, Rafian Rofiqo, Azidni Selly Anastassia Amellia Kharis Tri Wijayanti Septiarini