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Isran K. Hasan
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Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
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INDONESIA
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 77 Documents
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Model Regresi Linier Berganda Dalam Menganalisis Faktor-Faktor Urbanisasi Di Jawa Timur Octavia Putri Anggraini; Nurissaidah Ulinnuha; Moh Hafiyusholeh
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.v6i2.28446

Abstract

Urbanization occurs when population increases rapidly, encouraging individuals to migrate from villages to big cities. This phenomenon is triggered by the availability of wider employment opportunities and easier access to resources and technology. However, urbanization also has several negative impacts on the environment, such as reducing the ability to create a comfortable and healthy environment for city residents. This study aims to analyze the factors that influence urbanization in East Java Province using multiple linear regression. The data used is quantitative and was obtained from the East Java Provincial Statistics Agency in 2024. The variables analyzed include poverty levels, security levels, health, education, and unemployment rates. The partial analysis results indicate that the Education Ratio variable has a significant influence on urbanization in East Java, with a coefficient of determination value of 54.1\%. These findings are expected to contribute to the formulation of more targeted development policies in managing the pace of urbanization. 
Pendekatan Metode Partisi, Hierarki, dan Densitas dalam Pengelompokan Provinsi di Indonesia Berdasarkan Indeks Ekonomi Hijau Tahun 2023 Fat’hul Mubin Gufron; Innas Khoirun Chisan; Almira Utami; Nur Yudha Jati Prakoso; Fitri Kartiasih
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.v6i2.27734

Abstract

Sustainable economic development that maintains environmental balance is a top priority in Indonesia’s national development planning. One of the key indicators to measure this sustainability is the Green Economy Index (GEI). The Ministry of National Development Planning (Bappenas) assesses green economic development using 15 indicators across three pillars: economic, social, and environmental. This study aims to cluster Indonesian provinces based on the GEI. The clustering methods used include partition-based approaches (K-Means, K-Medoids), hierarchical (\textit{agglomerative clustering}), and density-based (OPTICS), with evaluation based on internal validity and stability. The results show that the hierarchical \textit{average linkage} method provides the most optimal clustering performance, dividing provinces into three main groups. Each cluster reflects different GEI characteristics, highlighting disparities in green development achievements across regions. Cluster 1 consists of one province with high economic scores but very low environmental scores; Cluster 2 includes five provinces strong in environmental performance but weak economically; and Cluster 3 contains 32 provinces with diverse characteristics in green economic practices. These findings are expected to support more targeted and region-specific policy formulation to promote equitable green economic development. 
Analisis Perbandingan Peramalan Indeks Harga Konsumen di Indonesia dengan Metode Autoregressive Integrated Moving Average dan Bayesian Structural Time Series Mochammad Taufiqurrochman; Affiati Oktaviarina
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): 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.v7i1.38286

Abstract

The Consumer Price Index (CPI) is an important macroeconomic indicator that reflects inflation and price stability in a country. This study aims to compare the forecasting accuracy of the Autoregressive Integrated Moving Average (ARIMA) and Bayesian Structural Time Series (BSTS) methods in predicting the CPI in Indonesia, as well as to provide methodological recommendations for researchers and policymakers. The data used consists of Indonesia’s monthly CPI from January 2020 to December 2024, obtained from the Central Statistics Agency (BPS), comprising a total of 60 observations. The data was divided into training data (85%) and test data (15%). The results of the study indicate that the best ARIMA model is ARIMA (0,2,1) with a MAPE value of 1.43%, projecting that the CPI is likely to decline from 105.85 to 97.34 (indicating deflation). Meanwhile, the best BSTS model is the state component semilocal linear trend with 1,000 iterations and a MAPE of 0.21%, projecting the CPI to remain relatively stable around 106. Quantitatively, BSTS demonstrates a significant advantage in accuracy compared to ARIMA, while ARIMA is superior at capturing long-term trend dynamics.  Based on these findings, it is recommended to use BSTS if the primary priority is forecast stability and the highest accuracy, whereas ARIMA is more suitable if the objective of the analysis is to capture historical trend dynamics and long-term projections. 
Perbandingan Kinerja Long Short-Term Memory dan Gated Recurrent Unit dalam Prediksi Harga Saham McDonald’s Muhammad Ridho Alfarid; Qonita Husnia Rahmah
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): 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.v7i1.33060

Abstract

Social movements occurring at the global level can influence public perspectives and actions toward a company and affect its stock value. This study aims to analyze the impact of the social boycott movement on McDonald’s (McD) stock price by comparing the performance of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, with hyperparameter tuning conducted using Optuna. The data used consist of McD’s daily closing stock prices from January 31, 2015, to January 31, 2025, obtained from www.finance.yahoo.com. The results show that the LSTM model without hyperparameter tuning provides the most optimal performance, achieving a Mean Absolute Percentage Error (MAPE) of 1.79% on the training data and 1.47% on the test data. This model is effective in identifying changes and forecasting McD’s stock price before and after the boycott 
Forecasting Fire Hotspots in Indonesia: A Comparative Performance Analysis of SARIMA and Pulse Intervention Models Bustami Bustami; Gustriza Erda; Putri Soraya Tampubolon
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): 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.v7i1.38133

Abstract

Wildfires in Indonesia have a widespread impact on health, the environment, society, and the economy. The number of hotspots, detected through satellite imagery, is a key indicator in monitoring the severity of fires. Because hotspot data is seasonal and prone to spikes due to extraordinary events such as El Niño, an adaptive forecasting method is needed. The SARIMA model is effective for capturing seasonal patterns, but it is less responsive to extreme spikes. Therefore, intervention analysis with pulse functions is used as an alternative to model sudden and temporary changes in time series data. This study aims to compare the performance of the SARIMA model and an intervention model using a pulse function in forecasting the number of hotspots in Indonesia. The data used in this study were obtained from the Ministry of Environment and Forestry through the SiPongi platform, consisting of monthly data from January 2014 to December 2022. The modeling results show that the SARIMA   model produced  a MAPE value of 36.93%, an RMSE of 66.27, and an MAE of 47.83. In contrast, the intervention model with a pulse function at order b=0, s=0, and r=1 specifically SARIMAachieved  a MAPE of 8.06%, an RMSE of 8.45, and an MAE of 6.67, substantially outperforming the SARIMA model across all metrics. These findings indicate that the intervention model provides much more accurate forecasts of hotspot occurrences in Indonesia. Furthermore, forecasts up to 2025 indicate a declining trend in the number of hotspots over time. However, seasonal patterns remain evident, with expected increases in hotspot activity during the months of February, August, and October. These results are expected to contribute valuable insights for developing more effective forest fire mitigation strategies in Indonesia  
Perbandingan Gaussian Process Regression dan Support Vector Regression dalam Prediksi Suhu Perencanaan Tanam Jagung Misranti A. Samulu; Novianita Achmad; Isran K. Hasan
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): 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.v7i1.38097

Abstract

Corn is an important food crop commodity that plays a significant role in the agricultural sector and regional economy. Efforts to increase corn production in Gorontalo have become one of the programs initiated by the Ministry of Agriculture to support Indonesia's corn exports. However, corn productivity is influenced by various factors, one of which is temperature variation resulting from climate change. This study aims to predict weekly maximum temperatures as a basis for determining the optimal planting time for corn by comparing the performance of the \textit{Gaussian Process Regression} (GPR) method using four kernel functions (\textit{Periodic}, \textit{Matern}, \textit{Radial Basis Function}, and \textit{Rational Quadratic}) and the \textit{Support Vector Regression} (SVR) method optimized using \textit{Particle Swarm Optimization} (PSO). The data used in this study consist of weekly maximum temperature observations from 2023 to 2024 obtained from the Gorontalo Climatology Station. The results indicate that the GPR method achieved the best performance, yielding a \textit{Mean Absolute Percentage Error} (MAPE) of 2.17\%, while the PSO-optimized SVR method produced a MAPE of 2.30\%. Based on the forecasting results, the optimal corn planting period within the next 30 weeks is between the sixth and eighth weeks, as the critical growth phase of the crop is expected to occur under the most stable temperature conditions and within the optimal temperature range for corn growth. 
Penerapan Model Word Embedding IndoBERTweet pada Metode Support Vector Machine untuk Klasifikasi Opini Publik di Media Sosial X Naufal Daffa Pahrun; Novianita Achmad; Isran K Hasan
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): 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.v7i1.38992

Abstract

This study aims to classify public sentiment regarding the redenomination of the Indonesian rupiah on social media platform X using a combination of Support Vector Machine (SVM) and IndoBERTweet word embedding. The main challenge in social media sentiment analysis lies in unstructured text, informal language usage, and contextual ambiguity. Therefore, an approach capable of capturing contextual meaning while maintaining high classification accuracy is required. This research employs a quantitative approach, including data collection through crawling, text preprocessing, data labeling, feature extraction using IndoBERTweet, and classification using SVM with a Radial Basis Function (RBF) kernel. A total of 1,014 tweets were collected and refined into 370 labeled data consisting of positive and negative sentiments. The results show that the proposed model achieves an accuracy of 82\%, precision of 83\%, recall of 82\%, and F1-score of 82\%. These findings indicate that the integration of IndoBERTweet and SVM effectively captures contextual semantics in Indonesian social media text and improves sentiment classification performance. Furthermore, the analysis reveals that the majority of public opinions tend to be positive toward the redenomination issue. This study is expected to contribute to the development of machine learning-based sentiment analysis and support more responsive policy-making based on public opinion   

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