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JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.37905/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 67 Documents
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Pengelompokan Desa di Jawa Barat Berdasarkan Indeks Desa Membangun (IDM) Menggunakan ALgoritma Clustering Large Application (CLARA) Hendriawan, Muhammad Rifqi; Marliana, Reny Rian
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.27450

Abstract

The Village Development Index (IDM) is an important instrument for measuring village planning and development, supporting the government in dealing with underdeveloped villages and advanced villages. IDM assesses development through three indexes, namely the Economic Resilience Index (IKE), Social Resilience Index (IKS), and Environmental Resilience Index (IKL). The aim of the study is to establish clusters of villages in West Java Province using the Clustering Large Application (CLARA) algorithm. This clustering was used to facilitate the identification of underdeveloped villages. The CLARA algorithm, which is a development of K-Medoids, is used for non-hierarchical clustering analysis with medoids as the cluster center. This study used data of IKE, IKS, and IKL from villages in West Java in 2023. The results produce three clusters, namely independent villages (cluster 1), developed villages (cluster 2), and developing villages (cluster 3). Based on the Ministry of Village, there are 1856 villages in cluster 1, 2494 villages in cluster 2, and 961 villages in cluster 3. Meanwhile, based on the CLARA algorithm, there are 1644 villages in cluster 1, 1573 villages in cluster 2, and 2094 villages in cluster 3. The global silhouette coefficient (SC) value in the CLARA algorithm shows an SC value of 0,3614. This indicates that the grouping criteria are included in the weak category, because they are between the values of 0,26-0,50.  
Prediksi Laju Inflasi dengan Metode Long Short-Term Memory (LSTM) Berdasarkan Data Laju Inflasi dan Pengeluaran Kota Ternate masipupu, Frangky Aristiadi; setiawan, Adi; Susanto, Bambang
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.30627

Abstract

Inflation is one of the main indicators that reflect the economic stability of a region. Ternate City, as one of the cities in North Maluku Province, exhibits fluctuating inflation dynamics from year to year. This study aims to forecast the inflation rate in Ternate using the Long Short-Term Memory (LSTM) method, which is a neural network architecture well-suited for processing time series data. The data used consists of monthly Consumer Price Index (CPI) figures for Ternate from 2016 to 2023, obtained from the Central Bureau of Statistics (BPS). The LSTM model was trained using monthly CPI changes as the basis for calculating inflation. The model evaluation results show a Root Mean Square Error (RMSE) of 0.9275, Mean Absolute Error (MAE) of 0.8369, and Mean Absolute Percentage Error (MAPE) of 20.13%. These results indicate that the LSTM model performs well in forecasting inflation in Ternate City and can be utilized as a decision-support tool in regional economic planning and policymaking.   
Analisis Faktor-Faktor yang Mempengaruhi Jumlah Kasus Tuberculosis di Kabupaten Lombok Timur menggunakan Model Spatial Autoregressive Poisson Adini, Ertina Septia; Azizah, Efida; Hastuti, Siti Hariati; Ghazali, Muhammad
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.30913

Abstract

Tuberculosis (TB) is a deadly infectious disease caused by the bacteria Mycobacterium tuberculosis. According to the NTB Provincial Health Office, the number of TB cases in NTB Province was reported as many as 7,305 cases in 2019. East Lombok Regency in that year recorded 1,521 TB cases. The high number of TB cases in East Lombok Regency is an interesting reason to use statistical analysis techniques in modeling variables that influence the number of TB cases in East Lombok Regency. This study uses Spatial Autoregressive Poisson (SAR Poisson) analysis. This method is a development of the classical regression method by considering spatial dependence on the dependent variable, namely count data that follows the Poisson distribution. According to the results of the study, there is significant spatial dependence on the data based on the results of the Moran's I test. The results of the SAR Poisson modeling show that only the Population Density variable (X_4) has a significant effect on the number of TB cases in East Lombok Regency with a parameter value of -1.24 x 10^{-21}. The corrected determination coefficient showed quite high results with a value of 71.8\%, which means that the model can explain most of the variability in the data, which is an indication that the model has a good fit and high relevance to the data. The results of the mapping of the comparison of actual data and the estimated value of TB cases from the SAR Poisson model showed similar results. 
Pengelompokan Daerah di Indonesia Berdasarkan Indikator Penetapan Daerah Tertinggal Menggunakan Model Based Clustering Nurwahyuni, Nurwahyuni; Junaidi, Junaidi; Gamayanti, Nurul Fiskia
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.25724

Abstract

Binary Underdeveloped regions are regencies whose areas and communities are less developed compared to other regions on a national scale. In Indonesia, there are 62 underdeveloped regencies scattered across various provinces. This study aims to classify these 62 regencies based on the Indicators for Determining Underdeveloped Regions, which include Gross Regional Domestic Product (GRDP) per capita (X1), Percentage of Non-Food Household Expenditure (X2), Junior High School Participation Rate (X3), Senior High School Participation Rate (X4), Villages with Health Facilities (X5), Villages with Doctors (X6), Villages with Elementary Schools (X7), Villages with Junior High Schools (X8), and Regional Original Revenue (PAD) per capita (X9). The method used in this study is ModelBased Clustering using a multivariate t-distribution approach. This method focuses on a statistical model and is based on the Finite Mixture assumption. In the Finite Mixture framework, the data is assumed to originate from several distributions, and the resulting clusters represent these probability distributions. The study identifies the UUUC model as the best model, producing two optimal clusters with distinct characteristics: Cluster 1 with a low level of regional underdevelopment, and Cluster 2 with a high level of underdevelopment. It is hoped that regencies classified in the highly underdeveloped cluster can be prioritized to achieve equitable development more quickly and effectively 
Analisis Geographically Weighted Regression (GWR) Berbasis Pemetaan pada Jumlah Menara Telepon Seluler di Kabupaten Lombok Tengah Tahun 2021 Wati, Rana Ambarwati; Febiana, Izu Izatul; Hastuti, Siti Hariati
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.24770

Abstract

Cell towers are tall structures where telecommunications equipment and antennas are installed to support cellular networks. This research aims to analyze the influence of Population Size (X1) and the Number of Service Operators (X2) on the Number of Cellular Phone Towers (Y ) in 139 villages in Central Lombok Regency in 2021. The Geographically Weighted Regression (GWR) method is used to understand spatial variability in the relationship between variables (X1) and (X2) with respect to variable Y . This method is an advancement of the OLS regression analysis method, taking spatial variability into account. By using this method, it is hoped that more accurate spatial distribution patterns can be identified, along with solutions that can assist in the planning of telecommunications infrastructure development in this area. The analysis results indicate that there is significant spatial variability in the distribution of the number of cellular towers based on the Breusch-Pagan test. The significance test results for the parameters show that only the Population Size variable (X1) has a significant effect on the Number of Cellular Towers (Y ) across all observation units (villages) in Central Lombok Regency. The comparison of models shows that the GWR model for the Number of Cellular Towers is better than the OLS Regression, with lower AIC and SSE values and a higher coefficient of determination (R2). 
Faktor-Faktor Penentu Prevalensi Stunting di Nusa Tenggara Barat: Analisis Spasial dengan Modifikasi Ketetanggaan Nastiti, Kartika Tri; Luthfi, Zalfa Jihan; Ummah, Karimatul; Brilliant, Indira Ihnu; Setiawan, Ezra Putranda
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.24714

Abstract

Stunting is one of the problems faced by the Indonesian population. In 2022, its prevalence in West Nusa Tenggara reached 18.5% and became the fourth highest in Indonesia. This study was conducted to identify the factors that can be used to explain the prevalence of stunting in West Nusa Tenggara using the spatial regression method.  Considering that this province consists of two separate islands, Queen's contiguity matrix was modified to consider the connections between the islands.  Based on the AIC values, the Spatial Durbin Model (SDM) becomes the best model for stunting prevalence. The research results show that the variables Human Development Index (HDI), ADHK Gross Regional Domestic Product, and the number of community health centers have a significant effect on the prevalence of stunting in West Nusa Tenggara. Of these three variables, the HDI variable has the greatest influence on reducing the prevalence of stunting in West Nusa Tenggara. The significance of the Spatial Durbin model shows that there is a spatial effect on the dependent and independent variables. 
ANALISIS PERAMALAN HARGA SAHAM MENGGUNAKAN TEMPORAL CONVOLUTIONAL NETWORK: STUDI KASUS PT LIPPO GENERAL INSURANCE TBK Rivai, Muklas; Nugraha, Ongky Setya
Jambura Journal of Probability and Statistics Vol 6, No 2 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i2.26817

Abstract

The stock market has an important role in the Indonesian economy, but share price fluctuations are often difficult to predict accurately. The machine learning algorithm for forecasting stock price movement trends uses a Temporal Convolutional Network (TCN). This method uses a more comprehensive dataset and advanced analysis techniques to capture non-linear and dynamic patterns in stock price data. This research aims to predict the share price of PT Lippo General Insurance Tbk using Temporal Convolutional Network (TCN) to provide a more accurate and reliable forecasting model. The research method uses a quantitative approach with daily historical stock data from 2011 to 2023 which is processed through several stages, including data collection, pre-processing, model development, and performance evaluation.  The results of the study show that the stock price forecasting of PT Lippo General Insurance Tbk using the Temporal Convolutional Network (TCN) method produces values that are relatively close to the actual ones with MSE, RMSE, MAE, and MAPE indicators, respectively, being 11,076.8214; 105.2464; 63.5915; and 2.2369\%. This indicates that the TCN model is able to capture complex temporal patterns in the stock price data of PT Lippo General Insurance Tbk. The forecasting results that have been projected for the next 60 days, that the stock price of PT Lippo General Insurance for the next 60 days will tend to decrease from August 31 to November 23.  

Page 7 of 7 | Total Record : 67

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