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All Journal Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Buana, Widyo Pura
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Estimasi Luas Fase Tanaman Padi dengan KSA-Hybrid Dzakwan, Mochammad Nafi'; Buana, Widyo Pura
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1845

Abstract

The COVID-19 pandemic at 2020 resulted in the collecting process of the KSA Padi survey not running optimally. Alternative sources are needed that do not require officers to go to the field, one of solution is remote sensing. KSA-Hybrid is a new term in the combination of the KSA Padi method and remote sensing. Research with KSA-Hybrid is still rarely carried out and produces a significant difference to the actual situation. This study focuses on the best percentage of the combination between both. The research locus in Lamongan Regency with a coverage period of 2018-2020. The satellite imagery feature sourced from the Landsat-8 satellite. The machine learning model used a random forest with evaluation indicators by accuracy and kappa statistics. The research results obtained accuracy and kappa evaluation values ​​of 72.62 and 64.85 percent. The percentage of the best combination by KSA-Hybrid is 3% because the relative change impact is below 1%..
Improving The Accuracy of Area Sampling Frame Estimators for Agricultural Surveys Using Unequal Clustered Segment Sampling: The Case of Indonesia Zikra, Hazanul; Buana, Widyo Pura; Bimarta, Yocco; Paramitasari, Nurina
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.477

Abstract

Accurate rice production data are vital for maintaining national food security and formulating effective agricultural policies. In Indonesia, the Area Sampling Frame (KSA) method has been widely implemented to estimate rice harvest areas using segments of 300 meters×300 meters represented by nine observation points. However, this approach faces limitations, particularly the risk of undercoverage bias when estimating areas across different rice growth stages, especially if the observation points fall outside the target rice-growing regions  as population area. To address this issue, the present study introduces the Unequal Clustered Segment Sampling method as an alternative to the traditional KSA approach. The Unequal Clustered Segment Sampling method improves estimation accuracy by refining the sampling frame and excluding non-target segments, spatial points located outside actual rice-growing regions. Through a design-based estimation framework, the proposed method accounts for unequal cluster sizes, allowing a more representative depiction of field conditions. The empirical results demonstrate that the Unequal Clustered Segment Sampling method significantly reduces bias and enhances the precision of rice area estimates compared to the conventional KSA. These findings suggest that incorporating unequal clustered segment sampling designs into KSA-based surveys can yield more reliable and representative estimates, particularly in heterogeneous or fragmented agricultural landscapes.
Co-Authors Bimarta, Yocco Dzakwan, Mochammad Nafi' Paramitasari, Nurina Zikra, Hazanul