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INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 152 Documents
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The Implementation of Geospatial Analysis on Hotel Occupancy Rate Nazuli, Muhammad Fachry; Panuntun, Satria Bagus; Maulana, Addin; Takdir; Pramana, Setia
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.797

Abstract

Introduction/Main Objectives: One of the main attributes of hotel selection and customer satisfaction is its location. Background Problems: Strategic location leads to higher demand for accommodation. Accommodation demand is reflected in hotel occupancy levels, which indicate the percentage of reserved rooms at a specific period. Novelty: This study aims to investigate the effect of spatial location on hotel occupancy rates by analyzing data collected in online hotel reservation applications. A study related to the effects of location and hotel occupancy has never been conducted in Indonesia. Research Methods: We use data from hotels located in the province of Yogyakarta, which contains 245 hotels spread over three regencies/cities, namely Yogyakarta City, Sleman Regency, and Bantul Regency. We conducted a spatial regression analysis, namely the Spatial Error Model (SEM), with a spatial weight matrix using a radius of 3.2 km. Finding/Results:  We found that spatial locations affect the occupancy rates of hotels based on the online hotel reservation application that we observed. These spatial locations include the distance from the hotel to the airport, the distance from the hotel to the bus stop, and the number of nearby restaurants, offices, and hotels.
The Application of Partial Proportional Odds Model on Determinants Analysis of Household Food Insecurity Level in Papua, Indonesia Abigael, Rolyn; Sumarni, Cucu; Sastri, Ray
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.798

Abstract

Introduction/Main Objectives: Food insecurity in Papua, Indonesia, is still high. However, the study on that issue is limited. This research aims to analyze the determinants of food insecurity in Papua. Background Problems: An ordinal logistic regression can be used. However, this model generally requires the parallel lines assumption. However, somehow, the assumption is often violated. Novelty: This study used a model that relaxes the assumption of parallel lines. This model can capture the condition that some parameters are assumed to meet parallel lines and some do not. Research Methods: In this case, the partial proportional odds model was applied to find the determinant of household food insecurity status by using the National Socioeconomic Survey (SUSENAS) data. Finding/Results: The results show that a female head of household, age 60 years and above, junior high school education and below, has a higher tendency to be at least mildly food insecure, and the effect is the same for each level of food insecurity. Household heads who do not work, work in agriculture, and have household drinking water sources that are not feasible can aggravate the food insecurity level. Meanwhile, food assistance provided by the government influences reducing food insecurity levels.
Estimation of Gross Regional Domestic Product per Capita at the Sub-District Level in Bali, NTB, and NTT Provinces Using Machine Learning Approaches and Geospatial Data Putra, I Made Satria Ambara; Prasetyo, Rindang Bangun; Wiguna, Candra Adi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.803

Abstract

Introduction/Main Objectives: This study aims to estimate Gross Regional Domestic Product (GRDP) per capita at the sub-district level. Background Problems: Currently, GRDP per capita is calculated only at the district level by BPS. Novelty: This study estimates GRDP per capita at the sub-district level using a model developed at the district level, applying machine learning and linear regression methods. Research Methods: The model was constructed using geospatial data sourced from satellite imagery, OpenStreetMap, (Village Potential Statistics) PODES, directories of large mining companies, and directories of the manufacturing industry at the district level. Linear regression and machine learning methods, including neural networks, random forest regression, and support vector regression, were used to develop the model. The research focuses on three provinces: Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Findings/Results: The best-performing model was support vector regression, with MAE and MAPE evaluations of 10.33 million and 26.11%, respectively. The results indicate that sub-districts with high GRDP per capita are typically urban areas that serve as economic hubs. The Williamson Index results show that districts in the eastern region have higher inequality levels compared to those in the western region.
Clustering Regencies/Cities Vulnerable to Air Pollution in the Java Island: Fuzzy Geographically Weighted Clustering Kusuma, Arya Candra; Wijayanto, Arie Wahyu; Arista; Bahar, Vicka Kharisma; Siregar, Tifani Husna
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.618

Abstract

Introduction/Main Objectives: Air pollution has become a critical global concern with substantial effects on human health and the environment. Background Problems: Java Island in Indonesia, recognized for its high population density and industrial activities, necessitates focused effort in resolving this issue. Novelty: While air pollution research has been enormous, there has been no effort to cluster regencies or cities on Java Island utilizing spatially-based data. This research seeks to cluster regencies and cities on Java Island according to air pollution levels and to compare geodemographic and non-geodemographic clustering methodologies. Research Methods: This study employs secondary data regarding air pollution, obtained from the Openweather API. This study employs a geodemographic clustering technique, namely fuzzy geographically weighted clustering (FGWC), optimized by the artificial bee colony (ABC) algorithm. Finding/Results: The study findings indicate that the geodemographic clustering method ABCFGWC surpasses Fuzzy C-Means (FCM) according to the TSS (Tang-Sun-Sun) index. The data reveal that the Greater Jakarta or Jabodetabek area and its adjacent territories are more susceptible to air pollution. The findings of this study are expected to enhance the spatial planning and mapping of air pollution management strategies on Java Island.
Pemodelan Angka Prevalensi Stunting di Indonesia Menggunakan Regresi Nonparametrik Spline Truncated Multiprediktor Pressylia Aluisina Putri Widyangga; Alda Fuadiyah Suryono; Maria Setya Dewanti; Ardi Kurniawan
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting merupakan gangguan pertumbuhan dan perkembangan yang dialami anak akibat gizi buruk, infeksi berulang, dan stimulasi psikososial yang tidak memadai.. Berdasarkan data Survei Status Gizi Nasional (SSGI) tahun 2022, prevalensi stunting di Indonesia adalah 21,6% dimana angka tersebut masih diatas angka standar menurut WHO yaitu 20%. Penelitian ini dilakukan dengan tujuan untuk menganalisis faktor-faktor yang mempengaruhi angka prevalensi stunting di Indonesia menggunakan regresi nonparametrik spline truncated multiprediktor. Data penelitian merupakan data sekunder yang diambil dari Statistik Kesehatan 2022 dengan variabel respon berupa angka prevalensi stunting.
Forecasting Farmer Exchange Rate (FER) in Southeast Sulawesi Province Using Cheng’s Fuzzy Time Series Method Rastina; Laome, Lilis; Abapihi, Bahriddin; Wibawa, Gusti Ngurah Adhi; Laome, Mukhsar; Laome, Makkulau; Sohibien, Gama Putra Danu; Sukim; Fathurrahman Yahyasatrio
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.801

Abstract

Introduction/Main Objectives: This study aims to forecast the Farmer Exchange Rate (FER) in Southeast Sulawesi Province for 2024 as a basis for short-term economic assessment and policy-related analysis. Background Problems: FER is a key indicator of farmers’ purchasing power and agricultural welfare; however, its monthly dynamics are characterized by fluctuations and uncertainty, making conventional forecasting methods less effective in capturing its behavior. Novelty: This study contributes by implementing the the Fuzzy Time Series (FTS) Cheng approach for FER forecasting in Southeast Sulawesi, emphasizing its suitability for handling vagueness and nonlinear patterns inherent in agricultural economic indicators. Research Methods: The analysis utilizes monthly secondary FER data obtained from BPS-Statistics of Southeast Sulawesi Province, covering the period from January 2014 to December 2023. Forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). Finding/Results: The forecasting results indicate that the FER values for January, February, and March 2024 are each estimated at 105.93. The model achieved a MAPE of 0.3027%, corresponding to an accuracy level of 99.6973%, which places the forecasting performance in the “excellent” category.
Adding MSNBURR-IIa Distribution to MultiBUGS Ramadani , Eliana Putri; Choir, Achmad Syahrul; Pravitasari , Anindya Apriliyanti; Paraguison, Joynabel
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.804

Abstract

Introduction/Main Objectives: The MSNBurr-IIa distribution is a neo-normal distribution designed to fit right-skewed data better. This article aims to integrate the MSNBurr-IIa distribution into MultiBUGS, thereby enabling Bayesian estimation of its parameters. Background Problems: Markov Chain Monte Carlo (MCMC) is a popular method for Bayesian computations, although its implementation is frequently challenging. MultiBUGS, a statistical tool that uses the BUGS language, is used to make this easier. Novelty: This paper details integrating the MSNBurr-IIa distribution into MultiBUGS, allowing for estimating its parameters. The module's effectiveness is demonstrated through its application on both simulated data and regional economic growth data of Indonesian districts/cities in 2021. Research Methods: The MSNBurr-IIa module was developed using five steps: requirement, design, development, testing, and implementation in simulation and real-world data. It was built with Blackbox Component Builder, an integrated development environment (IDE) for the Component Pascal programming language. Finding/Results: The findings confirm that MultiBUGS, with the MSNBurr-IIa module, successfully estimates the distribution’s parameters across various datasets.
Spatial Heterogeneity of Food Security in Indonesia: Unpacking the Roles of Technology and Democracy Index Wicaksono, Ditto Satrio; Pusponegoro, Novi Hidayat; Setiyawan, Arbi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.817

Abstract

Introduction/Main Objectives: Food security is a key concern for all countries, especially Indonesia. Technological development and democratic quality are vital for sustainable food security. This study aims to determine the impact of technology and democracy on food security. Background Problems: The relationship between food security and these two factors remains uncertain. Moreover, the extant literature on the spatial impacts on food security yields results that are inconclusive. Novelty: This study offers a comprehensive depiction of the impact of spatial relationships between variables, with a particular focus on the quality of democracy and technology, on the multidimensionality of food security. Research Methods: A spatial lag model is applied to ascertain the impact of technological and democratic on multidimensional food security using data from 34 provinces in 2022. Finding/Results: The results reveal significant spatial dependence in Indonesia’s food security. Technological development and democratic quality positively and significantly affect food security, while urbanization and food crop land expansion show negative and positive effects, respectively. Spatial spillover accounts for approximately 37%–38% of the total impact of each explanatory variable. These findings suggest that technology adoption, democratic strengthening, and interprovincial collaboration are crucial for improving food security.
Satellite Imagery for Classification Analysis of Abrasion Areas on Panaitan, Banten Ismail, Ghaffar; Kurniawan, Robert; Silvia Ni'matul Maula
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.850

Abstract

Introduction/Main Objectives: Abrasion causes severe environmental degradation and socio- economic losses, Waton and Karang Gundul Islands have already subsided due to erosion, posing risks to Panaitan Island, a national park that also faces deforestation, infrastructure development, and vegetation loss which may intensify abrasion. Background Problems: Limited spatial data on coastal abrasion in Panaitan Island hampers effective monitoring and management, highlighting the need for spatially explicit analysis. Novelty: This study identified and classified abrasion-prone areas on Panaitan Island (a rarely exposed island) with rarely variables which have impactful indices such as MVI, TCI, and LSWI. Research Methods: Landsat 8 and Sentinel-2 imagery from 2018 and 2023 were analyzed to assess changes in vegetation, mangroves, surface temperature, and soil moisture. Random Forest, Support Vector Machine, and Logistic Regression were employed to classify abrasion-prone areas. Finding/Results: The analysis revealed signs of abrasion covering 2.04 km², with Random Forest achieving the highest accuracy (82.23%) and NDVI as the most influential variable; abrasion was mainly associated with declining forest and mangrove cover, soil moisture showed weak correlation, while moderate surface temperature had a positive effect. Preventive measures such as reforestation and mangrove rehabilitation are recommended to mitigate risks and ensure long-term environmental sustainability.
Implementation of Twofold HB Beta SAE Model to Estimate Out-of-School Children with Disabilities in Indonesia Maharani, Aisha; Ubaidillah, Azka; Adhi Kurniawan
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.851

Abstract

Introduction/Main Objectives: The high percentage of out-of-school children with disabilities in Indonesia reveals a significant gap in educational participation. Background Problems: Due to the absence of disability-focused surveys, accurate data are only available at the national level, which is insufficient to represent regional conditions. Novelty: With the increasing demand for small area data, this study estimates the percentage of out-of-school children with disabilities at the provincial and district levels simultaneously, using small area estimation (SAE). Research Methods: This study applies SAE using a twofold subarea-level model with a Hierarchical Bayes (HB) beta approach, covering all 34 provinces and 514 districts/cities in Indonesia. This model was developed using data from the National Socio-Economic Survey (Susenas) and the Village Potential Statistics (Podes). Finding/Results: The twofold HB beta SAE model achieves higher precision than direct estimation, as shown by lower relative standard errors (RSE) across regions. Furthermore, spatial patterns indicate that the percentage of out-of-school children with disabilities is mostly between 35.36% and 45.34%, with clusters concentrated in Kalimantan and Papua.

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