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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 95 Documents
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ORDINAL PROBIT REGRESSION MODELING ON THE HUMAN DEVELOPMENT INDEX MALUKU AND NORTH MALUKU PROVINCES Embuai, Emanuel Marthen; Salhuteru, Rosalina; Sopaheluwakan, Marsa; Djami, Ronald J.
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (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/parameterv4i1pp73-80

Abstract

This study examines the factors influencing Human Development Index (HDI) in Maluku and North Maluku Provinces using ordinal probit regression. Secondary data are employed to identify key socio-economic variables affecting HDI levels. Model selection is based on the Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), and R² values, while the Likelihood Ratio (LR) test is used to evaluate the significance of parameters. The findings reveal that expected years of schooling (X₂) is the most significant factor in determining HDI categories. The model that includes only this variable yields the lowest AIC and SBIC values and shows a significant LR test result. Furthermore, the negative regression coefficient indicates that an increase in expected years of schooling raises the probability of a region achieving a higher HDI category. These results underscore the crucial role of education policies in promoting human development. Future research is encouraged to incorporate economic, health, and infrastructure variables to provide a more comprehensive understanding of the factors influencing HDI.
COMPARISON OF CLASSIFICATION MODELS USING SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (K-NN) METHODS IN NON-PERFORMING LOAN ANALYSIS Djami, C. P.; Latupeirissa, S. J.; Delsen, M. S. Noya Van
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (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/parameterv4i1pp175-184

Abstract

SVM works by finding the best dividing line (or hyperplane) to separate two groups of data based on the maximum margin.k-NN classifies new data based on its similarity to previous, already-labeled data points.Non-performing loan analysis is a crucial aspect of credit risk assessment. This study compares the performance of the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classification methods in analyzing non-performing loans at PT. Adira FinanceAmbon Branch. The dataset includes demographic and financial attributes, processed through normalization, data splitting, and evaluation using accuracy, precision, recall, and AUC metrics. The results show that SVM with a linear kernel performs best, achieving 97.83% accuracy and 95% AUC. Meanwhile, k-NN with k=5 attains 78.26% accuracy. Thus, SVM outperforms k-NN in classifying non-performing loans in this study.
ADAPTIVE EXPONENTIALLY WEIGHTED MOVING AVERAGE WITH MEASUREMENT ERROR (COVARIATE) WITH AUXILIARY INFORMATION MAXIMUM FOR CEMENT QUALITY CONTROL Sellyra, Eirene Christina; Ahsan, Muhammad; Wibawati, Wibawati
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (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/parameterv4i1pp29-46

Abstract

The Shewhart control chartexhibits limitations in detecting small process shifts and monitors the mean and variance separately. To address these shortcomings, this study introduces the Adaptive EWMA with Measurement Error (Covariate Method) and Auxiliary Information Max (AEWMA ME C AI Max) control chart. This novel approach integrates memory-based monitoring, joint mean-variance detection, measurement error correction through the covariate method, utilization of auxiliary variables, and adaptive adjustment mechanisms to enhance sensitivity across various shift magnitudes. The AEWMA ME C AI Max chart was applied to cement production data from PT XYZ, using Blaine fineness as an auxiliary variable for monitoring compressive strength. Comparative analysis demonstrates that the adaptive chart consistently produces control statistics closer to the upper control limit compared to the non-adaptive Max-EWMA ME C AI chart, validating its superior sensitivity in shiftdetection. Furthermore, the cement production process at PT XYZ was found to be statistically capable, with a lower capability index (Ppl) and process performance index (Ppk) of 1.45, indicating consistent compliance with lower specification limits and centered process performance. These results affirm the practical effectiveness of the AEWMA ME C AI Max chart in enhancing process monitoring and capability assessment in industrial applications.
Clustering of Water Quality Location Using Self Organizing Maps (SOM) Amalia, Rahmatin Nur; Farady, M. Difa; Aksioma, Diaz Fitra; Ahsan, Muhammad
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp197-208

Abstract

A decline in the number of locations meeting drinking water quality standards was observed based on internal monitoring in 2021 and 2022. To address this, clustering was performed on water quality test locations using Self Organizing Maps (SOM). The analysis of data from 60 locations, considering turbidity, pH, iron, and nitrite parameters, indicated very good water quality. Outliers were detected before clustering, with the Ireng location being the most extreme, showing turbidity of 4.95 NTU and pH of 8.41, near specification limits. The clustering process removed one outlier, forming two clusters with a silhouette coefficient of 0.668. Multivariate normality tests showed the samples were not multivariate normal, leading to the use of Kruskal-Wallis testing. The results revealed significant differences between clusters 1 and 2, particularly in turbidity and iron levels. Cluster 2 had better water quality, with lower turbidity and iron content. Some locations in cluster 1 exceeded 1 NTU turbidity and had higher iron levels. The company should improve water quality monitoring and control at locations approaching specification limits.
Estimating the Potential Gross Regional Domestic Product (GRDP) of Banten Province Using the Optimal Hodrick–Prescott Filter Fajar, Muhammad; Suparintina, Lucie; Akhiriyanto, Khafid
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp185-196

Abstract

Gross Regional Domestic Product (GRDP) serves as a fundamental metric for assessing economic activity within a specific region, encapsulating the total value added by all sectors of the economy over a defined period. Although GRDP is widely utilized to evaluate regional economic performance, it predominantly reflects realized output under prevailing conditions, thereby failing to fully capture the region’s optimal productive potential. As such, estimating potential GRDP is imperative for discerning the maximum sustainable level of economic output achievable through the efficient and effective allocation of resources. Potential GRDP is conceptualized as the highest level of output that can be sustained without generating upward pressure on inflation. This study focuses on Banten Province—one of Indonesia’s principal economic hubs—and underscores the critical role of potential GRDP estimation in informing long-term development strategies, managing output gaps, and evaluating the trajectory of post-shock economic recovery. The empirical investigation reveals that potential GRDP can be reliably estimated through the application of a smoothing parameter optimized by minimizing the Generalized Cross-Validation (GCV) criterion. The trajectories of both nominal and real potential GRDP exhibit strong coherence with their respective actual GRDP values, thereby validating the robustness of the estimation technique. Moreover, the derived output gap—calculated as the deviation between actual and potential GRDP—serves as a diagnostic tool for identifying cyclical dynamics within the regional economy. Findings indicate that Banten's economy more frequently experiences positive output gaps, indicative of overheating episodes wherein aggregate demand exceeds existing productive capacity. These results highlight the necessity for macroeconomic policy interventions aimed at mitigating demand-side pressures while addressing structural supply-side limitations. In conclusion, the estimation of potential GRDP and the associated output gap provides a vital analytical framework for the formulation of adaptive, evidence-based, and sustainable economic policies at the regional level.
Regression Models with ARMA Errors for Predicting Tabarru Fund in Islamic Insurance: A Normally Distributed Simulation Approach Andirasdini, Indah Gumala; Aliem, Dien Manarul; Sofia, Ayu
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp239-248

Abstract

Islamic insurance is a financial protection system based on mutual assistance and risk-sharing, facilitated by a tabarru fund among participants. Effective management of this fund is essential to prevent financial deficits while ensuring sustainability and compliance with Sharia principles. This study aims to predict the value of the tabarru fund by developing a regression model with ARMA errors, incorporating variables such as participant contributions, claim amounts, and investment returns. The Regression model with ARMA errors is a hybrid approach that combines multiple linear regression with ARMA-based residual modeling, effectively addressing autocorrelation in regression residuals. The data used in this study were generated through a normal distribution simulation based on the monthly financial records of a Sharia insurance company over a ten-year period. The analysis results indicated that the regression model with ARMA(1,0) errors could provide predictive values with minimum error of prediction (MAPE value 0.022%). These findings demonstrate the model’s potential for strategic financial planning in Islamic insurance institutions, particularly in optimizing fund allocation and supporting risk-sensitive investment decisions.
Fractional Modeling of the Interaction Between Mycobacterium Tuberculosis and Its Response to Antibiotics Khairiah, Inayah Alifah; Adi, Yudi Ari
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp223-238

Abstract

Mycobacterium tuberculosis is a bacterium that causes tuberculosis, which is the second most common infectious disease in the world and generally attacks the lungs. It is important to formulate the dynamics of interactions and the effects of antibiotic administration on Mycobacterium tuberculosis into a mathematical model, especially using a fractional order approach. In this study, a model was developed using the Caputo-Fabrizio derivative. The purpose of the study was to study the dynamics of interactions between bacteria and antibiotics, where the administration of antibiotics causes the bacterial population to be divided into two types, namely sensitive bacteria and bacteria resistant to antibiotics. Based on the model built, four equilibrium points were obtained. Stability analysis shows that these equilibrium points are locally asymptotically stable under certain conditions. To support the results of the analysis, numerical simulations were carried out using the three-step Adams-Bashforth method with the Caputo-Fabrizio derivative. The simulation results showed that the smaller the value of the fractional order parameter , the faster the system reaches the equilibrium point. Although the value of affects the speed of convergence, it does not affect the stability of the equilibrium point.
Implementation of Mixed Integer Programming Using the Branch and Bound Algorithm in Optimizing the Profit of a Chips Enterprise Syahfitri, Fadila Inka; Rakhmawati, Fibri
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp209-222

Abstract

Keripik Cinta Mas Hendro is a micro, small, and medium enterprise (MSME) operating in the snack industry, specializing in the production of various types of chips. This MSME struggles to determine the optimal production quantity due to limited resources, inconsistent raw material supply, and fluctuating market demand. This study aims to optimize the production quantity for each product variant using the Mixed Integer Programming approach with the Branch and Bound method. The Branch and Bound method were specifically chosen as it effectively handles decision variables that must be integers, which was critical in real-world production settings where fractional outputs are not feasible. The results showed that after optimization, production becomes more efficient, with allocations including: 2,100 packages of 500g Balado chips, 836 of 500g Sweet Corn, 4,500 of 500g Sweet and Spicy, 350 of 500g Seaweed, 8,527 of 1000g Original, 158 of 1000g Balado, 125 of 1000g Sweet Corn, 215 of 1000g Sweet and Spicy, 150 of 1000g Seaweed, along with 185 kg of Balado chips and 176.38 kg of Sweet and Spicy. As a result, the optimization increases profit from IDR 249,840,000 to IDR 360,791,500, reflecting a gain of IDR 110,951,500 or approximately 44.42%.
Hybrid ARIMA-GARCH Model with Walk-Forward Method on LQ45 Stock Price Forecasting Kaito, Nurlaila; Annas, Suwardi; Alimuddin, Alimuddin
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp249-260

Abstract

Stock investment offers returns but also risks, such as potential capital losses due to declining stock prices. To mitigate these risks, investors use forecasting models, and one common approach is time series forecasting. The ARIMA model captures linear patterns in data, while the GARCH model handles time-varying volatility. This study uses a hybrid ARIMA-GARCH model with the Walk-Forward method to predict the daily closing prices of LQ45 index stocks from January 2022 to May 2024, utilizing data from Yahoo Finance. The Walk-Forward approach divides the data into 80% training and 20% testing, ensuring the model is tested on unseen data for more realistic evaluation. The process includes fitting the ARIMA model to stock return data, testing for heteroscedasticity, and building the hybrid ARIMA-GARCH model. The best model, ARIMA(1,0,0) – GARCH(1,1), was selected based on the lowest AIC value of -3004.88 for ARIMA and -6.83 (AIC) and -6.78 (BIC) for GARCH. This research contributes to stock forecasting by applying high-frequency data and the Walk-Forward validation method, offering a more accurate assessment of the model’s performance. It also enriches time series analysis methodology in the Indonesian stock market by combining ARIMA and GARCH models, optimizing model parameters using AIC and BIC criteria for stock price prediction.
Implementation of the Advanced Encryption Standard (AES) Algorithm to Protect Children Personal Data Patty, Dyana; Kololu, Selvy Marchia; Dahoklory, Novita; Leleury, Zeth Arthur
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (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/parameterv4i2pp261-274

Abstract

The rapid development of information and communication technology is inseparable from data security issues. In today's digital age, one type of data that is vulnerable to cyber security threats is children's personal data. Protecting children's personal data is an important priority in safeguarding their privacy and digital security. This research applies AES-256 on a website to secure personal data within the database. AES-256 is a symmetric cryptographic and block-ciphertext algorithm that can encrypt and decrypt data/information with a key size of 256 bits, which can be used to secure data. Results demonstrate that AES-256 effectively maintains full name, NIK and password confidentiality and integrity, rendering the encrypted ciphertext difficult to interpret or access. This study provides a basis for advancing data security and related applications which strengthens the complexity of encrypted children's personal data against cryptanalysis attacks.

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