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A Monitoring Kualitas Kaca Panasab dengan Diagram Kontrol MEWMA wibawati, Wibawati; Fadhila, Amalia Nur
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 6 No. 1 (2023): Volume 6 Nomor 1 tahun 2023
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v6i1.2115

Panasab glass is one type of glass that can absorb some of the heart's heat so that the temperature in the room is more comfortable. One of the national glass companies that produce hot glass monitors the silvering coating process. The silvering method is considered less effective because it is expensive. Therefore, in this paper, the process of controlling the quality of hot glass is carried out on the quality characteristics of the left and right CD edge distortion, which is the length of the roll marks on the right and left sides of the net glass that affect the presence or absence of distortion in the visual check results. The two quality characteristics correlate with each other. Therefore, quality control is carried out using a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to monitor process variability and averages. MEWMA control chart statistics are affected by the weighting value . The quality characteristic data of panasab glass is divided into phase 1 and phase 2. Phase 1 is used to obtain an in-control parameter estimate (IC) which is then used for monitoring phase 2. Quality monitoring in phase 1, the process has been statistically controlled after iterations have been carried out. By using =0.4. Meanwhile, phase 2, based on the MEWMA control chart, shows that the producton procrss of panasab glass production has not been controlled statistically. Based on process capability analysis, the results obtained that the level of precision and accuracy of the quality of panasab glass products is still low. So it can be concluded that the production process of panasav glass is not yet capable.

Parameter Estimation of Mixed Geographically Weighted Bivariate Zero-Inflated Negative Binomial Regression Model Islamiati, Mawadah Putri; Purhadi, Purhadi; Wibawati, Wibawati
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.32711

The Bivariate Zero-Inflated Negative Binomial (BZINBR) regression model is commonly used to analyze two correlated count response variables characterized by overdispersion and excess zeros. To account for spatial heterogeneity in predictor effects, the BZINBR model has been extended into the Geographically Weighted BZINBR (GWBZINBR) model. However, predictor effects are not always entirely local; certain global effects may persist across regions. This study proposes the Mixed Geographically Weighted BZINBR (MGWBZINBR) model, which integrates both global and local parameter structures for modeling spatially correlated bivariate count data. The theoretical framework of the MGWBZINBR model is developed, including the derivation of the log-likelihood function, parameter estimation procedures, and hypothesis testing. Parameter estimation is conducted using the Maximum Likelihood Estimation (MLE) method via the iterative Berndt–Hall–Hall–Hausman (BHHH) algorithm. Given the complexity of the likelihood equations and the absence of closed-form solutions, numerical optimization is employed to ensure convergence and stability. The MGWBZINBR model offers a flexible and robust framework for analyzing spatial count data with excess zeros and complex dependency structures. It can be applied in various fields, including public health, ecology, and transportation analysis, to understand the influence of both local and global predictors on spatial phenomena. As the focus of this paper is methodological, empirical and simulation-based applications are intentionally excluded.

A Monitoring Kualitas Kaca Panasab dengan Diagram Kontrol MEWMA wibawati, Wibawati; Fadhila, Amalia Nur
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 6 No. 1 (2023): Inovasi Teknologi, Psikologi Belajar, dan Adaptasi Pembelajaran Matematika di E
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v6i1.2115

Panasab glass is one type of glass that can absorb some of the heart's heat so that the temperature in the room is more comfortable. One of the national glass companies that produce hot glass monitors the silvering coating process. The silvering method is considered less effective because it is expensive. Therefore, in this paper, the process of controlling the quality of hot glass is carried out on the quality characteristics of the left and right CD edge distortion, which is the length of the roll marks on the right and left sides of the net glass that affect the presence or absence of distortion in the visual check results. The two quality characteristics correlate with each other. Therefore, quality control is carried out using a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to monitor process variability and averages. MEWMA control chart statistics are affected by the weighting value . The quality characteristic data of panasab glass is divided into phase 1 and phase 2. Phase 1 is used to obtain an in-control parameter estimate (IC) which is then used for monitoring phase 2. Quality monitoring in phase 1, the process has been statistically controlled after iterations have been carried out. By using =0.4. Meanwhile, phase 2, based on the MEWMA control chart, shows that the producton procrss of panasab glass production has not been controlled statistically. Based on process capability analysis, the results obtained that the level of precision and accuracy of the quality of panasab glass products is still low. So it can be concluded that the production process of panasav glass is not yet capable.

FORECASTING NUMBER OF INTERNATIONAL TOURIST ARRIVALS USING MULTI INPUT INTERVENTION ARIMA MODEL Khusna, Hidayatul; Mashuri, Muhammad; Ahsan, Muhammad; Wibawati, Wibawati; Aksioma, Diaz Fitra; Suhermi, Novri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1539-1548

In 2020, the Covid-19 pandemic caused a very significant impact resulting in the drastic decline in the number of international tourist visits. As the Covid-19 pandemic ends, the government reopen international flight to Indonesia in early 2022 to remark the revival of the tourism industry. To determine how big the impact of the Covid-19 pandemic as well as the recovery process on international tourist visits through Soekarno-Hatta, Ngurah-Rai, and Kualanamu airports in the coming period, forecasting is needed. The forecasting method utilized in this study is multi-input intervention analysis. The first input is caused by the outbreak of Covid-19 pandemic, while the second input is due to the international flight reopening. The type of intervention variable chosen is a step function because both inputs give permanent effect to the international tourist arrivals. The data used in this study are monthly international tourist arrivals based on the entrances to Soekarno-Hatta, Ngurah-Rai, and Kualanamu International Airports from January 2008 to September 2023, taken from the Central Bureau of Statistics website. Based on the results, it was found that the number of international tourist arrivals entering Soekarno-Hatta airport can be modelled using SARIMA (0,1,1)(0,1,0)12 with (b=2, s=1, r=0) and (b=2, s=[3], r=0) for first and second input of intervention variable, respectively. Furthermore, the number of international tourist visits through Ngurah-Rai airport was more appropriate to be modelled using SARIMA (1,1,1)(0,1,1)12 with intervention inputs (b=1, s=[2], r=0) and (b=4, s=0, r=1). In Kualanamu airport, the first intervention order is equal to that in Ngurah-Rai airport, with (b=3, s=[3], r=0) for second intervention input and SARIMA (0,1,1)(1,1,1)12 for pre-intervention data. The forecast results show that the number of international tourist arrivals entering Soekarno-Hatta, Ngurah-Rai, and Kualanamu international airports are already recovered to pre-pandemic conditions at a quick pace

MAXIMUM EXPONENTIALLY WEIGHTED MOVING AVERAGE WITH MEASUREMENT ERROR (USING COVARIATE METHOD) USING AUXILIARY INFORMATION FOR CEMENT QUALITY CONTROL Sellyra, Eirene Christina; Ahsan, Muhammad; Wibawati, Wibawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1333-1348

The main quality characteristic at XYZ Inc. that should be observed is Compressive Strength. Cement production quality control is carried out on the average and process variability jointly with the Max-EWMA control chart. Measurement error can be found in the Compressive Strength. It can affect the sensitivity of the control chart, so quality control will be carried out by considering the presence of measurement error. Handling measurement errors can be done through three approaches (covariate method, multiple measurements, and linearly increasing variance). This research only focuses on the covariate method. Auxiliary variables also explain variance in the production process, so they are also considered in this research, with Blaine used as an auxiliary variable. Therefore, the control chart that will be formed is the Max-EWMA ME (Covariate) AI. The Max-EWMA and Max-EWMA ME (Covariate) AI control charts show that the XYZ Inc. cement production process based on variability and process averages is simultaneously statistically controlled. The controlled Max-EWMA control chart has an upper control limit of UCL=1.503018, and parameters dan . Max-EWMA ME (Covariate) AI has in-control parameters . The Max-EWMA ME (Covariate) AI control chart is more sensitive than the Max-EWMA control chart. Cement production capabilities based on Compressive Strength have a Cpl and Cpk capability index of 1.54, which means that the cement production process is capable, consistent, and has high accuracy so that the quality has reached the target.

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

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.

Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection Prasetya, Ichwanul kahfi; Isnawarty, Devi Putri; Fahmi, Abdullah; Andikaputra, Salman Alfarizi Pradana; Wibawati, Wibawati
Inferensi Vol 7, No 1 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i1.18755

Credit card is a transaction tool using a card which is a substitute for legitimate cash in transactions. The use of computer technology is needed for various kinds of electronic transactions. In the world of technology, the term machine learning is not new and technological developments are increasingly rapid in recent years. Statistical process control method (SPC) is one of the measuring instruments used to improve the performance of public services. Hotelling T^2 control chart is a method in SPC that can be used to control the process. Methods that are widely used in the detection and classification of documents one of them is Naive Bayes Classifier (NBC) which has several advantages, among others, simple, fast and high accuracy. Those two methods will be used to detecting o2utlier of this dataset. The study used the credit card fraud registry with some PCA as independent variables. The size of fraud transaction is very small which represented only 0.172% of the 284,807 transactions. This research will use Area Under Curve (AUC) as the performance goodness test. A comparison of the accuracy of NBC and Hotelling's T2 predictions shows that the performance of the T2 Hotelling method is better in detecting outliers than the NBC method

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