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Parameter: Jurnal Matematika, Statistika dan Terapannya
Published by Universitas Pattimura
ISSN : 28296303     EISSN : 28296303     DOI : https://doi.org/10.30598/parameterv3i01y2024
Core Subject : Education,
Mathematics
Parameter: Jurnal Matematika, Statistika dan Terapannya is an open access journal (e-journal) published since April 2022. Parameteris published by Department of Mathematics, Faculty of Science and Mathematics, Pattimura. Parameterpublished scientific articles on various aspects related to mathematics and statistics and its application. Articles can be in the form of research results, case studies, or literature reviews.
Arjuna Subject : Matematika - Matematika Terapan
Articles 111 Documents
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Energy Use vs Staff Performance at Soekarno Hatta: Partial Least Squares Structural Equation Modeling and Importance Performance Map Analysis Approach Mc Ali Muchtar; Erry Rimawan; Mawardi Amin
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp485-496

Abstract

This study investigates the operational impact of four key infrastructure systems (HVAC, lighting, electrical equipment, and internal transport) on staff performance at Terminal 3 of Soekarno-Hatta International Airport. Despite consuming 86.59% of the terminal’s energy, HVAC systems show no statistically significant contribution to staff performance. In contrast, lighting, electrical equipment, and internal transport significantly improve staff productivity, with internal transport having the highest influence. A structural equation modeling approach using PLS-SEM and Importance-Performance Map Analysis (IPMA) was employed to analyze data from 400 respondents. The model yielded strong explanatory (R² = 0.613) and predictive relevance (Q² = 0.505), validating its robustness. Findings suggest that energy management in airport terminals should shift from consumption-based to performance-based prioritization, favoring infrastructure investments that directly enhance operational efficiency.
Forecasting Regional Economic Growth Using TVARX: Model Accuracy Evaluation in Banten Province Amelia Vega; Muhammad Fajar; Hendro Prayitno; Erya Afrianus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp551-562

Abstract

Forecasting regional economic performance is essential for supporting timely and responsive policy planning. This study aims to forecast the Gross Regional Domestic Product at constant prices (GRDP) in Banten Province for the second to fourth quarters of 2025 using the Time-Varying Autoregressive model with Exogenous Variables (TVARX). The model incorporates household final consumption expenditure, gross fixed capital formation, exchange rates, and export values as exogenous variables. Model performance was evaluated by comparing combinations of training-testing data proportions (90:10, 80:20, 70:30, and 60:40) and two estimation approaches (local constant and local linear), using the Mean Absolute Percentage Error (MAPE) as the predictive accuracy metric. All variables were transformed into logarithmic form and differenced to ensure stationarity. The results indicate that the model using a 90:10 data split and the local linear estimation approach yielded the most accurate prediction, with the lowest MAPE value of 0.6%. The best-performing model was then applied to forecast out-of-sample GRDP CP for the next three quarters, with its year-on-year growth subsequently analyzed. These findings are expected to serve as a basis for data-driven economic analysis and support macroeconomic planning that is responsive to short-term structural dynamics.
Monitoring and Evaluation of Clinker Quality Using T2 Hotelling-Generalized Variance Control Chart Aisha, Dinda Fitri Nur; Inayatulloh, Faza; Ahsan, Muhammad; Wibawati, Wibawati
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp471-484

Abstract

The cement industry is an important sector in infrastructure development, where the quality of clinker determines the final quality of the product. This study evaluates the application of T² Hotelling's and Generalized Variance (GV) multivariate control charts to clinker data based on three main variables: FCaO, C₃S, and C₃A at PT XYZ. The results show that C₃S has the highest variance in phase I and II (2.61 and 2.53), while FCaO has the lowest variance (0.10 and 0.06). All three variables had mean values within the specification limits, although there were still extreme values outside the limits. Assumption tests showed that the data was not multivariate normally distributed, but it was still assumed to be normal for control analysis purposes. In the wet season, the standard deviation decreased from 1.552 to 1.252, and in the dry season from 1.170 to 1.029, indicating a decrease in variability although the process is not yet fully under statistical control. Capability analysis shows that the dry season process is more stable, with most parameters having multivariate values that exceed the threshold. Compared to the wet season, the dry season process showed more consistent performance and was able to meet production quality standards.
Enhancing Rainfall Forecasting Performance in Bandung City Using Bi-LSTM with Grid Search Optimization on Gregorian and Lunar Calendar Data Mahdayani Putri Yunizar; Andrew Hosea Talakua; Gumgum Darmawan
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp595-601

Abstract

Rainfall is a climatic factor that strongly influences human activities and plays a crucial role in decision making related to water resources, mobility, and disaster preparedness. High rainfall intensity may escalate into hydrometeorological hazards, underscoring the importance of accurate rainfall forecasting to support early warning and mitigation efforts. This study aims to compare the forecasting accuracy of monthly rainfall predictions between the Gregorian and lunar calendars using the Bidirectional Long Short-Term Memory (Bi-LSTM) model optimized through a grid search approach. The method is designed to capture temporal patterns arising from the distinct structures of two asynchronous calendars. Daily rainfall data from Bandung City, Indonesia, covering the period from 2000 to 2025, were converted into monthly series in both calendar systems. The results reveal that the Gregorian calendar provides significantly better forecasting performance, achieving the lowest MAPE value of 11.60 percent at the three-month horizon. In contrast, the lunar calendar shows higher variability and reaches its best MAPE of 31.43 percent at the same horizon. These findings indicate that the Gregorian calendar offers a more stable temporal representation for rainfall forecasting in Bandung and supports improved predictive modeling for climate-related decision making.
Comparing Weighting Schemes in Modeling Child Malnutrition in East Java Zulfani Alfasanah; Bambang Widjanarko Otok; Muhammad Ahsan
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Partial Least Squares is increasingly used as an alternative to covariance-based SEM due to its flexibility in handling non-normal data, small sample sizes, and complex models, as well as its ability to operate under different inner weighting schemes. However, empirical studies rarely compare these weighting schemes, even though they may influence measurement validity and structural interpretations. This study applies PLS-SEM using both the path and factor weighting schemes to evaluate their performance in modeling child malnutrition. Child malnutrition remains a major public health concern, as it is driven by the interaction of socioeconomic, food security, parenting, and access to basic services. The study estimates and evaluates measurement and structural models using PLS under path and factor schemes. The findings show that both schemes produce acceptable measurement and structural models, but the path scheme yields more consistent indicator significance and more stable structural relationships, while the factor scheme is more sensitive to weaker indicators, leading to some nonsignificant loadings and paths. The results suggest that although both weighting schemes are suitable for exploratory analysis, the path weighting scheme provides more robust and interpretable results for explaining child malnutrition, highlighting the importance of weighting scheme selection in applied PLS-SEM research.
Exploring the Determinants of Behavioral Intention and Use Behavior Toward TikTok Shop Based on the UTAUT2 Model Rizki Fitriani Muin; Bambang Widjanarko Otok
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp29-44

Abstract

The rapid growth of social commerce through TikTok Shop has opened new opportunities for businesses and consumers in Indonesia, including in the city of Ambon. This study aims to analyze the factors influencing consumers’ behavioral intention and use behavior in shopping on TikTok Shop by applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model. The respondents of this research were TikTok Shop users in Ambon who had made at least one transaction, with a minimum sample size of 200 participants. The results show that all UTAUT 2 constructs, except for facilitating conditions on use behavior, have a significant effect on consumers’ intention and usage behavior. The variable behavioral intention was found to be the most dominant factor driving actual usage behavior, with R² values of 0.753 for behavioral intention and 0.817 for use behavior, indicating a strong predictive capability of the model. These findings affirm the relevance of the UTAUT 2 model in explaining technology adoption in the context of social commerce and provide practical implications for platform developers and digital business practitioners to design marketing strategies that align with the characteristics of consumers in eastern Indonesia.
Cluster Analysis on Time Series Data for Indonesian Stock Prices Using Dynamic Time Warping Wisnowan Hendy Saputra; Nuruddeen Shehu
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp01-16

Abstract

This study investigates the application of the Dynamic Time Warping (DTW) algorithm to cluster the ten stocks with the largest market capitalization on the Indonesia Stock Exchange as of February 2026. Unlike conventional distance metrics, DTW handles time lag nonlinearly to identify hidden temporal pattern similarities. Adjusted closing price data was obtained through web scraping from Yahoo Finance using the R programming language. The clustering procedure was performed using Ward's Hierarchical Agglomerative Clustering method, where the number of clusters was evaluated through Silhouette coefficient analysis and qualitative economic interpretability. While the highest Silhouette score suggested two clusters, a three-cluster solution was selected as the most representative structure to better capture the granular dynamics of different market sectors. The first cluster is dominated by the banking and energy sectors with stable growth trends. The second cluster includes cyclical industrial and infrastructure stocks with high volatility. The third cluster uniquely unites GOTO and UNVR stocks in a long-term bearish downward pattern, despite their origins in different sectors. These findings demonstrate that DTW is highly effective in uncovering cross-sector market dynamics, providing a more accurate basis for portfolio diversification strategies than traditional business sector classifications.
Two-Stage Estimation in Copula-Based Bivariate Survival Models with Cox Marginals Wahyu Dwi Rahmawati; Jerry Dwi Trijoyo Purnomo; Bambang Widjanarko Otok
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp17-28

Abstract

This study is motivated by the presence of dependence between event times in bivariate survival data, which cannot be adequately captured by univariate Cox models. A copula-based bivariate survival model with Cox Proportional Hazards marginals is considered. The estimation procedure follows a two-stage approach, where marginal parameters are first obtained using partial likelihood and the baseline survival function is estimated via the Breslow method. In contrast to conventional approaches that treat marginal estimates as final or imposing shared parameter structures, this study introduces a modification in the second stage by performing simultaneous joint estimation of marginal without imposing shared parameter and dependence parameters using the BHHH algorithm. This allows the marginal parameters to be estimated while explicitly accounting for the dependence between event times. The evaluation was conducted via simulation with variation in sample size, censoring, dependency level and copula types. Simulation results show that the proposed method produces stable and reliable parameter estimates while preserving the interpretability of marginal effects, providing a flexible framework for modeling dependent bivariate survival data.
Improving Land Use Classification Accuracy Using Zonal Statistics And Supervised Machine Learning Gede Awantara; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp181-194

Abstract

This study aims to improve land use classification accuracy by integrating zonal statistics with supervised machine learning using Sentinel-2 imagery. Two classification models were developed: Model A based on single-pixel values and Model B using aggregated zonal statistics derived from polygon shapefile data. Two algorithms, Random Forest and Classification and Regression Trees (CART), were implemented and evaluated through 5-fold cross validation. The results show that Model B consistently outperformed Model A, with the best performance achieved by Random Forest Model B, reaching an overall accuracy of 73.74% and a kappa coefficient of 0.5999. Class-wise evaluation based on F1-score revealed strong performance in dominant classes such as settlement, water bodies, and rice fields, while underrepresented classes like cropland and shrubland were more difficult to classify due to class imbalance. These findings highlight the effectiveness of zonal statistics in producing more representative training features and improving model stability and accuracy in land use classification tasks.
Rainfall Prediction in Central Maluku Regency using The Autoregressive Moving Average (ARMA) Model Approach Indri Kezia Latupeirissa; Fadila Rumbaroa; Henry Junus Wattimanela; Wa Ode Ma’arifah; Jean Apituley; Sahrul Rumalean
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rainfall is one of the main indicators in climate analysis of a region, as its distribution and intensity can indicate seasonal patterns, climate variability, and long-term climate change. Central Maluku Regency exhibits unpredictable rainfall patterns due to its geographical factors. This study aims to forecast rainfall in Central Maluku Regency using the Autoregressive Moving Average (ARMA) model approach. The data used were obtained from the Central Statistics Agency (BPS), consisting of rainfall data from January 2019 to October 2024. The analysis process includes stationarity tests on variance and mean using the ADF test, model identification, parameter estimation and significance testing, as well as residual diagnostic testing. The results indicate that the ARMA(1,0) model is the best-fitting model, with an accuracy measured by a MAPE of 43,24%, which is considered reasonably accurate. These findings provide important information to support climate data-based decision-making in Central Maluku Regency.

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