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Norisca Lewaherilla
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Variance : Journal of Statistics and Its Applications
Published by Universitas Pattimura
ISSN : 26858738     EISSN : 2685872X     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini diterbitkan oleh Program Studi Statistik Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pattimura, Ambon. Jurnal ini diterbitkan 2 kali pada bulan Juni dan Desember.
Arjuna Subject : -
Articles 106 Documents
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MULTIGROUP MODELING BASED PATH ANALYSIS ON PANEL DATA IN THE STUDY OF PRODUCTION PERFORMANCE IN SMALL AND MEDIUM INDUSTRIES IN MALANG Hapsari, Meilina Retno; Sa'diyah, Nur Kamilah
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page49-60

Abstract

This study aims to determine the effect of the number of workers on production performance through the amount of production between small and medium industries in Malang City. This study applies multigroup modeling based on path analysis on panel data on variables that affect industrial production performance in Malang and the application of the Hypothesis of Linear Parameter Function in the multigroup model, which is the second group. This data source is secondary data from Malang Industrial data. The sample in this study is all data from small and medium industries in Malang City over several years. The results showed that the Hypothesis of Linear Parameter Function can be applied to the two-group model, that is, in a medium industry, where three significant paths are obtained. In the small industry, there are two considerable paths. Based on the coefficient of total determination, the data diversity can explain the model of 96.0%. At the same time, the remaining 4.0% is affected by other variables. The small and medium industry model shows that the number of workers significantly affects production. The effect is greater in medium industries, which is 0.935, compared to small industries, which is 0.737.
MODELING THE NUMBER OF POOR POPULATION IN EAST JAVA USING QUANTILE REGRESSION Kartini, Alif Yuanita; Huda, Tisa Dwi Julianti; Budiani, Jauhara Rana
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page105-112

Abstract

The economic development of East Java continues to increase every year. However, this increase is not directly proportional to a significant decrease in poverty rates. Therefore, research is needed to determine the factors influencing poverty in East Java. This is important because it can be used as a consideration for the East Java Provincial Government in designing strategies to reduce poverty. In the case of the number of poor people in East Java, there are outlier data, so the quantile regression method is used to overcome this. This study uses several quantile values, namely 0.25, 0.50 and 0.75. Based on the results of the quantile regression parameter estimation, one significant category at all quantile levels is the Average Length of Schooling variable. From the quantile regression model, four categories of Poor Population are obtained: low, medium, high, and very high. Based on the classification of the Poor Population in East Java in 2023, there are four districts/cities with a low number of poor people, 18 districts/cities with a moderate number of poor people, and 16 districts/cities with a high number of poor people.
IMPLEMENTATION OF LONG SHORT-TERM MEMORY (LSTM) IN FORECASTING THE NUMBER OF TRAIN PASSENGERS IN JAVA ISLAND Gunawan, Naftali Brigitta; Wiyanti, Wiwik
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page1-10

Abstract

For certain Indonesians, trains are a particularly popular form of land transportation. Every year, during specific seasons, this mode of transportation consistently experiences a surge in passenger numbers. Due to this, it is necessary to make accurate predictions to make policies, such as whether additional carriages are needed. The selection of prediction methods will significantly impact policymaking. One of the methods currently being developed for prediction is related to machine learning. This study aims to implement a forecasting method using machine learning that can be used to predict time series data. The machine learning used in this study is the Long Short-Term Memory (LSTM) method. In this study, we used time series data on the number of train passengers. The data used is secondary data from the Statistics Indonesia (BPS). The data analysis process in this study uses Python software. The results of this analysis show that the LSTM model has a high level of accuracy in prediction, indicated by the mean squared error value of 2,941,137.156 and MAPE of 0.07%. Forecasts show a gradual increase in the number of passengers, starting from 32,381 people in the first month to 33,068 people in the third month. These results indicate that the LSTM model is thought to be effective in predicting changes in the number of train passengers, and further research is needed to verify this assumption.
PERFORMANCE EVALUATION OF NEURAL NETWORKS AND TRADITIONAL STATISTICAL METHODS IN ANALYZING IMBALANCED DATA: A COMPARATIVE STUDY Chairunissa, Abela; Nisa, Hilwin
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page21-30

Abstract

Class imbalance is a common issue in predictive modeling, particularly when minority classes carry critical significance, as seen in applications like fraud detection, rare disease prediction, and customer churn analysis. This study uses linear and non-linear simulated data scenarios to examine the performance of logistic regression, discriminant analysis, and neural networks on imbalanced data. For linear data, logistic regression and discriminant analysis displayed high sensitivity but extremely low specificity, indicating a strong bias toward the majority class. Neural networks showed marginal improvement but remained ineffective in detecting minority classes. In contrast, neural networks demonstrated superior sensitivity for non-linear data and were notably better at identifying minority classes, underscoring their suitability for complex data relationships. Our results highlight that accuracy alone is insufficient for evaluating models on imbalanced data; instead, sensitivity and specificity offer more relevant insights. Overall, this study suggests that neural networks are preferable for imbalanced data with non-linear patterns, and data characteristics and appropriate evaluation metrics should inform model selection.
PERFORMANCE COMPARISON OF DECISION TREE MODELS FOR PM10 PREDICTION IN JAKARTA Syahrin, Khairummin Alfi; Saputra, Agung Hari
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page11-20

Abstract

PM10 are airborne particulates that have a diameter of ≤10 μm. The potential hazards of PM10 particulates are an issue that is being intensified by many researchers. This research utilizes PyCaret, a library to accelerate the process of modeling and experimentation in the field of machine learning (ML) and data science. This research compares the performance of three decision tree-based models Extra Trees, Random Forest, and XGBoost in predicting PM10 particulate levels, presenting data and visualizations for each models predictions. The data used is ISPU data at five air quality monitoring stations in Jakarta, with the main dataset of PM10 in 2021. The forecast results show an increasing graph pattern, with higher fluctuations in XGBoost. The Extra Trees model produces the best performance, with MASE 0.8808, RMSSE 0.8113, MAE 12.6173, RMSE 14.7436, MAPE 0.2433, SMAPE 0.207, and R² -1.2013.
CLUSTERING AND VISUALIZATION OF OLYMPIC ATHLETE DATA BASED ON PHYSICAL AND DISCIPLINARY ATTRIBUTES Nisa, Hilwin; Chairunissa, Abela
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page113-122

Abstract

This study aims to identify hidden patterns in international athlete data through clustering and data visualization approaches. The goal is to group athletes based on physical characteristics and sports disciplines to uncover meaningful trends. Utilizing a dataset of over 200,000 entries from 1896 to 2016, the study applies K-Means, Agglomerative and DBSCAN clustering methods. Preprocessing steps include handling missing data, selecting relevant variables (Height, Weight, Age, Sex, Sport, and Medal), and data normalization. The Silhouette score for K-Means (0.273647136516163645), Agglomerative (0.26134664130023655), and DBSCAN (-0.23920792207945957) indicates suboptimal clustering with overlapping clusters. K-Means clustering performs slightly better among the three methods. The findings are visualized through cluster plots and an interactive map showing medal distribution. This study highlights the limitations of traditional clustering methods for large datasets and suggests future exploration with advanced techniques.
IMPLEMENTATION OF THE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY METHOD FOR FORECASTING THE STOCK RETURN OF PT LIPPO GENERAL INSURANCE TBK Bariq, Muhammad Shidqi Abdul; Sartono, Bagus; Sofia, Ayu
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page123-134

Abstract

The Indonesian capital market is one of the investment destinations for investors from developed countries. The development of Indonesia's economic conditions is considered good for investors in investing their funds. Financial sector shares are one of the sectors that has experienced development throughout this year. One of the seven stocks showing good growth is PT Lippo General Insurance Tbk (LPGI). The important thing that is the main concern of investors is the level of yield or return from a stock. Based on this, stock return forecasting analysis can be important information for investors. This research uses the GARCH method to forecast LPGI stock returns. The analysis results show that the best model for LPGI stock returns is ARIMA (2,0,0) GARCH (1,1) with a very small return value and a negative sign. Thus, these results provide information that the forecasting period is not the right time for investors to buy LPGI shares. However, investors who have bought LPGI shares and made a profit are advised to sell LPGI shares before the forecast period. The empirical evidence from this study demonstrates that the GARCH model can effectively capture the volatility pattern of LPGI stock returns in n financial market. This finding supports the application of GARCH in modeling return fluctuations in emerging markets.
ARCH MODEL FOR FORECASTING BCA BANK STOCK PRICE VOLATILITY Surya, Annisa Cahyani; Ariyanto, Adisty Syawalda; Napitupulu, Leonard Andreas; Sihaloho, Ryantoni; S, Mika Alvionita; Muthoharoh, Luluk
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page147-154

Abstract

This research analyzes the Autoregressive Conditional Heteroskedasticity (ARCH(p) model to predict the BCA Bank share price in the range of January 2013 to November 2023. BCA Bank's share price, as one of the shares traded on the Indonesian Stock Exchange, requires accurate volatility modeling. Researchers use the ARIMA(0,1,2) model as the initial approach, but because of heteroscedasticity, they apply the ARCH(8) model to overcome it. The results show that the ARCH(8) model performs best, with the lowest AIC values for volatility. BCA Bank's daily stock price as of December 1, 2023, showed high volatility, signaling significant risk to investors.
COMPARISON OF SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFICATION METHOD AND LEXICON BASED ON JIWA+ BY JANJI JIWA APPLICATION REVIEWS Arti, Reyana Hilda; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page135-146

Abstract

The coffee beverage industry in Indonesia is experiencing significant growth, intensifying competition among businesses striving to maintain quality for customer loyalty. E-commerce applications play a vital role in preserving business standards as they directly engage with consumers. Janji Jiwa is among the coffee brands leveraging an application named Jiwa+ in their operations. Analyzing reviews on this e-commerce platform provides valuable insights for business owners and app developers. In this study, sentiment analysis was conducted by classifying reviews into positive, neutral, and negative sentiments using two methods: Lexicon Based and Naïve Bayes. The Lexicon Based method uses a predefined dictionary as the basis for labeling, while Naïve Bayes relies on training data to provide new insights into how both methods handle this type of data. A total of 597 Jiwa+ application reviews from the Google Play Store were utilized, split into 90% training and 10% testing data sets. The study results indicate that Naïve Bayes produces a better model than the Lexicon-Based method, as shown by its higher accuracy, sensitivity, and specificity. This is because Lexicon-Based relies on labeling words from a dictionary, which may not cover all words in the reviews, leading to labeling errors and misclassification.
COMPARISON OF THE CHAIN LADDER AND BORNHOUTTER-FERGUSON METHODS IN CALCULATING CLAIM RESERVES FOR REINSURANCE COMPANY IN INDONESIA Yulita, Tiara; Julianty, Dila Tirta; Aprilia, Inaya Sathrani; Inayah, Larasati Nurul
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page155-166

Abstract

In the insurance industry, calculating claims reserves plays a crucial role in managing both risk and the financial stability of an insurance company. When a claim is filed, the insurer must allocate a reserve fund to anticipate potential future losses. In general insurance, claim settlements are often not completed immediately because there is usually a time gap between the occurrence of an incident and the reporting of the claim. Unresolved claims create liabilities or obligations for the insurance company. These allocated funds are referred to as claim reserves, which are generally categorized into two types: Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). This research focuses on determining estimates of claim reserves using the Chain Ladder and Bornhuetter-Ferguson methods for loss insurance data for the property business class of reinsurance companies in Indonesia for the period 2011 to 2021. The results show that the claims reserves for each method are IDR 2,499,456,710,993 and IDR 2,266,000,657,647 with MAPE value of 1.38% and 2.10%. The results of the MAPE value calculation show that the claim reserves estimate using the Chain Ladder method is better than the Bornhuetter-Ferguson method.

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