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Etis Sunandi
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Journal of Statistics and Data Science
Published by Universitas Bengkulu
ISSN : -     EISSN : 28289986     DOI : https://doi.org/10.33369/jsds
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Established in 2022, Journal of Statistics and Data Science (JSDS) publishes scientific papers in the fields of statistics, data science, and its applications. Published papers should be research-based papers on the following topics: experimental design and analysis, survey methods and analysis, operations research, data mining, machine learning, statistical modeling, computational statistics, time series, econometrics, statistical education, and other related topics. All papers are reviewed by peer reviewers consisting of experts and academics across universities and agencies. This journal publishes twice a year, which are March and October.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 2 Documents
 1 
Search results for , issue "Vol. 5 No. 1 (2026)" : 2 Documents clear
Enhancing Operational Efficiency in Domestic Cargo Handling at Tanjung Priok Port Affandi, Karina; Wulandari , Yuhanda Tri; Beat, Sebastiana Laura
Journal of Statistics and Data Science Vol. 5 No. 1 (2026)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v5i1.37840

Abstract

Maritime transportation plays a crucial role in national development and population mobility in archipelagic countries like Indonesia. It is also key in encouraging Indonesia's economic growth, especially in frontier, outermost, and underdeveloped areas, as well as being a gateway to international trade. One of the important nodal points in sea transportation is the Port of Tanjung Priok in North Jakarta. This port is the largest and busiest, serving as the main gateway for the flow of export-import goods and the distribution of goods between islands. This research aims to analyze the loading and unloading activities of domestic goods at the Port of Tanjung Priok using secondary data from the official website of the Central Bureau of Statistics for the period from January 2007 to December 2023. The models used are Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM). SARIMA is employed due to the presence of seasonal patterns in the data. Subsequently, the SARIMA model will be compared with the Long Short-Term Memory (LSTM) model, which uses a machine learning approach to evaluate and determine the most accurate model for predicting domestic cargo handling activities at Tanjung Priok Port. Based on the RMSE analysis, the LSTM model has a lower RMSE compared to the SARIMA model, indicating that LSTM provides more accurate predictions for this time series data. However, it is important to note that a lower RMSE does not always mean that one model is generally better. Additional evaluations, such as residual analysis, other statistical tests, or prediction consistency through cross validation, should also be considered to validate the model's superiority comprehensively. This analysis is expected to provide deeper insights into port capacity planning and operational management, enabling more precise and effective decision-making in response to future demand dynamics and operational trends.
A Review of the Convolution of Geometric Distributions and Its Properties Ayenigba, Alfred Ayo; Afariogun, David; Olajide Oyewole , AGBOOLA; Ivande , James Serumun
Journal of Statistics and Data Science Vol. 5 No. 1 (2026)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v5i1.46545

Abstract

This review explores the convolution of geometric distributions, a key operation in probability theory for deriving the distribution of the sum of independent random variables. Geometric distributions quantify the number of Bernoulli trials needed for the first success and are foundational in discrete probability models. Convolving multiple geometric distributions with a common success probability produces a negative binomial distribution, modelling the number of trials needed to achieve a given number of successes. We present a concise derivation of this result, highlighting the relationship between geometric and negative binomial distributions. The review also outlines essential properties of the negative binomial distribution, including its mean, variance, moment-generating function, and some applications.

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