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<![CDATA[Perbandingan Analisis Diskriminan Kuadratik dengan Analisis Diskriminan Kuadratik Robust ]]>
<![CDATA[Ully Martha martha]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Zilrahmi]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol2-iss4/315
<![CDATA[This study compared the performance of quadratic discrimination analysis and robust quadratic discrimination analysis using the Iris dataset from Kaggle. The robust quadratic discriminant analysis, designed to handle outliers and non-normal distributions, shows better performance with an Apparent Error Rate (APER) of 2.5%. In contrast, the quadratic discriminant analysis, used for data with multivariate normal distribution and different variance-covariance matrices among groups, yields an APER of 3.03%. These results indicate that robust quadratic discriminant analysis is more accurate in classification on this dataset compared to quadratic discriminant analysis. Keywords: Apparent Error Rate, Quadratic Discrimination Analysis, Robust Quadratic Discrimination Analysis]]>
<![CDATA[Implementation of Association Rule on Agricultural Commodity Exports in Indonesia Using Apriori Algorithm ]]>
<![CDATA[Asra Dinul Haq]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Zamahsary Martha]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss1/336
<![CDATA[Exports of agricultural commodities in Indonesia have the smallest contribution to state revenues and the movement of export values in the last decade has not shown a significant increase compared to other export sectors. This shows that there are weaknesses in the export of agricultural commodities so that an analysis is needed to optimize export results to other countries. These weaknesses can be seen in terms of quality, price, infrastructure and technology. This study uses association rule analysis with the apriori algorithm with the aim of finding out what agricultural commodities are exported simultaneously and the resulting association rules. The apriori algorithm is an algorithm used to find association rules between items in a database by considering two main parameters, namely Support and Confidence. The data used is agricultural commodity export data obtained from the publication of the Central Statistics Agency in Indonesia in 2023. Based on the analysis carried out, there are 32 association rules generated with a minimum Support of 25% and a minimum Confidence of 80%. Then after the Lift Ratio test was carried out, all the rules generated met the Lift Ratio test with a value of more than 1. The association rules produced must have at least 2 to 4 agricultural export commodities in each rule. By knowing the association rules for agricultural commodity exports, it is hoped that export distribution in the agricultural sector can be further optimized for trading abroad so that it can cover existing weaknesses.]]>
<![CDATA[Classification of Factors Affecting Preeclampsia in Pregnant Women at RSUP. Dr. M. Djamil Padang using the CART Algorithm ]]>
<![CDATA[AULIA YUSWITA]]>;
<![CDATA[Dina Fitria]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Admi Salma]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss1/341
<![CDATA[Preeclampsia is a pregnancy-specific disease characterized by hypertension and proteinuria that occurs after 20 weeks of gestation. Preeclampsia itself is caused by various factors that can influence the occurrence of preeclampsia in pregnant women, including age, parity, history of hypertension, obesity, and kidney disorders. This study aims to determine the risk factors influencing preeclampsia based on preeclampsia diagnosis at RSUP Dr. M. Djamil Padang by classifying each variable using a decision tree. This research employs the CART (Classification and Regression Tree) algorithm. The CART algorithm has a binary nature and can analyze response variables that are either categorical or continuous, handle data with missing values, and produce an interpretable tree structure. The study results indicate that the primary risk factor for preeclampsia is parity. The model developed using the CART algorithm was tested using a confusion matrix, yielding an accuracy of 54%, a precision of 33.3% in correctly classifying patients with mild preeclampsia (PER), and a recall of 23.8% in classifying patients with severe preeclampsia (PEB).]]>
<![CDATA[Analysis on Scopus Articles Padang State University Based on SINTA Website ]]>
<![CDATA[Kerin Hagia Aidillah]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dony Permana]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss1/346
<![CDATA[Universities have the responsibility to carry out education, research, and community service as mandated by Law Number 20 of 2003 on the National Education System in Article 20. The flagship research theme set by Universitas Negeri Padang (UNP) for the period 2020-2024 is "Development of Digital Learning Services and Development of Minangkabau Cuisine based on Local Potential." The focus of the flagship research activities at Padang State University encompasses two main research areas: 1) Digital Learning Services; and 2) Minangkabau Cuisine. The objective of this research is to compare the flagship research theme with the Scopus articles from Universitas Negeri Padang on the SINTA website. By analyzing the trends of Scopus article topics on the SINTA website using web scraping techniques and wordcloud visualization, it is concluded that there is a match between the trending topics of UNP's Scopus articles and UNP's flagship research theme, particularly in the field of Digital Learning Services. From the wordcloud results, which show keywords such as Learning, Development, Student, and Model. This research allows us to easily observe from the wordcloud visualization the trend of research topics in Scopus articles on SINTA at Universitas Negeri Padang, reflecting the realization of Universitas Negeri Padang flagship research theme for the period 2020-2024]]>
<![CDATA[Perbandingan metode Double Moving Average(DMA) dan Double Exponential Smoothing (Brown) Terhadap Tingkat Pengangguran Terbuka (TPT) di Kota Padang Panjang. ]]>
<![CDATA[Nufhika Fishuri]]>;
<![CDATA[Fadhilah Fitri]]>;
<![CDATA[Dony Permana]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss2/366
<![CDATA[The Open Unemployment Rate (TPT) is the percentage of unemployed people in the total labor force. The population included in the labor force is the population aged 15 years and over who has a job but is temporarily not working. Unemployment occurs because of a mismatch between the demand for employment and the qualifications of job seekers. Many job vacancies require graduates with a diploma or degree, so unemployment is one of the problems faced by Padang Panjang City. To overcome TPT in Padang Panjang City, one of the needs is to do forecasting to see how the TPT rate will occur in the coming year. This research uses a forecasting method by comparing the Double Moving Average (DMA) and Double Exponential Smoothing (DES) forecasting values of the Unemployment Rate in Padang Panjang City from 2006 to 2023. This forecasting is done to provide insight into the future condition of the workforce in Padang Panjang City. The results of the forecasting indicate that in 2024, there will be an increase of 0.42%, and for the next 2 years, there will be a decrease]]>
<![CDATA[Peramalan Total Nilai Ekspor Indonesia Menggunakan Metode Singular Spectrum Analisis ]]>
<![CDATA[Ronald Rinaldo]]>;
<![CDATA[Yenni Kurniawati]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss2/370
<![CDATA[Forecasting export data presents unique challenges due to seasonal fluctuations and complex global economic dynamics. Inaccurate forecasts may lead to misguided economic policies, particularly in the export sector, which plays a critical role in national economic growth. This study aims to forecast the total export value of two major sectors in Indonesia from January to December 2024 using the Singular Spectrum Analysis (SSA) method. Forecasting is essential in supporting economic policy planning and strategic decision-making. SSA is chosen for its ability to decompose time series data into interpretable components such as trend, seasonality, and noise. The forecasting model's performance is evaluated using the Mean Absolute Percentage Error (MAPE), which provides an intuitive accuracy interpretation in percentage terms. The optimal parameter for SSA was found at L=28L = 28L=28, yielding a MAPE of 16.63%, indicating good forecasting accuracy. The forecasted export values show that the highest export is expected in December 2024 (USD 39,578.67 million), and the lowest in January 2024 (USD 21,689.14 million). These findings suggest that SSA is effective in forecasting economic time series data, particularly Indonesia’s export values. This study contributes to the practical application of SSA in economics and serves as a reference for future research and policymakers in formulating export strategies.]]>
<![CDATA[Peramalan Harga Beras di Kota Padang untuk Tahun 2025 Menggunakan Jaringan Syaraf Tiruan dengan Metode Backpropagation ]]>
<![CDATA[Farras Luthfyah Nisa]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Denny Armelia]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss4/381
<![CDATA[Rice is a staple food commodity in Indonesia that significantly influences economic stability and food security. In Padang City, rice price fluctuations frequently occur due to high dependence on external supply sources and limited local production, highlighting the need for a reliable predictive system. This study aims to forecast the monthly average retail price if rice in Padang City for the year 2025 using an Artificial Neural Network (ANN) based on the Backpropagation algorithm. The forecasting model is developed using historical rice price data from January 2017 to December 2024. In addition to building the forecasting model, this study evaluates the model’s accuracy in capturing the complex and nonlinear patterns of rice price fluctuations. The forecasting results are expected to serve as a valuable reference for local policymakers, market participants, and consumers in making strategic decisions to anticipate future price volality.]]>
<![CDATA[Klasterisasi Kabupaten/Kota Berdasarkan Faktor-Faktor yang Mempengaruhi Kemiskinan di Sumatera Barat Menggunakan Metode K-Medoids ]]>
<![CDATA[Afifah Hardi]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Denny Armelia]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss4/382
<![CDATA[Poverty remains a significant issue in Indonesia, particularly in West Sumatra Province, where regional disparities persist despite a national decline in poverty rates. This study aims to classify the 19 regencies/cities in West Sumatra based on key socioeconomic indicators to support more targeted and effective poverty alleviation policies. Using a quantitative descriptive approach, the research applies the K-Medoids clustering method to group regions according to four indicators: Gross Regional Domestic Product (GRDP) per capita, Human Development Index (HDI), Open Unemployment Rate (OUR), and Gini Ratio. Secondary data for the year 2024 were obtained from the official website of the Central Bureau of Statistics of West Sumatra. Prior to clustering, data standardization using Z-score transformation was performed, and multicollinearity was tested using the Variance Inflation Factor (VIF). The silhouette method indicated that the optimal number of clusters is four. The clustering analysis revealed four distinct groups: (1) underdeveloped areas with low income and human development but high inequality; (2) moderately developed areas with stable unemployment and low income inequality; (3) urbanized areas with high income and human development but also high unemployment and inequality; and (4) a single metropolitan area with high economic and human development and moderate inequality. The findings highlight the importance of region-specific strategies in addressing poverty, considering the diverse economic and social conditions across regions. The results can serve as a basis for designing equitable and effective socioeconomic development policies.]]>
<![CDATA[Penerapan Partial Least Squares dan Pendekatan Robust dalam Analisis Diskriminan untuk Data Berdimensi Tinggi ]]>
<![CDATA[Rahmadina Adityana]]>;
<![CDATA[Dodi Vionanda]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Fadhilah Fitri]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss3/396
<![CDATA[Classical discriminant analysis, namely linear discriminant analysis and quadratic discriminant analysis, is generally known to suffer from singularity problems when exprerienced with high-dimensional data and is not robust to outliers that make the data not multivariate normally distributed. This research focuses on investigating the classification performance of discriminant analysis on high-dimensional data by applying two approaches, namely the Partial Least Square (PLS) dimension reduction approach as a solution to high-dimensional data and a robust approach with the Minimum Covariance Determinant (MCD) estimator technique that is robust to outliers. The data used for this study is Lee Silverman Voice Treatment (LSVT) data. PLS forms five optimal latent variables that represent predictor variable information. Based on the assumption test of covariance homogeneity between groups, the test statistic value is greater than the chi-square table or the p-value is smaller than the significance level, which means that the assumption is unfulfilled, so quadratic discriminant analysis is applied. The evaluation results showed that the quadratic discriminant analysis analysis model with the MCD approach on the PLS transformed data was able to achieve 81% accuracy, 71% precision, 86% recall, and 77% F1-score. These values indicate that both approaches are able to maintain the efficiency of discriminant analysis classification performance on high-dimensional and multivariate non-normally distributed data.]]>
<![CDATA[Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation ]]>
<![CDATA[Wahda Aulia Assara]]>;
<![CDATA[Zamahsary Martha]]>;
<![CDATA[Dony Permana]]>;
<![CDATA[Dina Fitria]]>
<![CDATA[ UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science ]]>
Publisher : <![CDATA[Departemen Statistika Universitas Negeri Padang ]]>
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DOI: 10.24036/ujsds/vol3-iss3/412
<![CDATA[Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.]]>
