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Pulungan, Ridson Al farizal
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Implementasi Small Area Estimation Hierarchical Bayes - Beta Difference Benchmark dalam Estimasi NEET Lulusan Perguruan Tinggi Salis, Dian Rahmawati; Japany, Adham Malay; Rodliyah, Ratih; Ibad, Syaikhul; Pulungan, Ridson Al farizal; Ramadhan, Yogi
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2285

Abstract

The survey data generated by BPS serves as the primary data source for calculating various SDGs indicators. However, not all indicators can be reliably estimated, particularly at detailed disaggregation levels. Some indicators face issues due to sample inadequacy, resulting in high Relative Standard Errors (RSEs) if estimated directly. One such indicator is the percentage of young college graduates who are neither in education, employment, nor training (NEET). This indicator is only available at the provincial level, with disaggregation based on other characteristics only available at national level. Therefore, this study aims to estimate NEET among college graduates at the regency/city level in Sumatra Island for the year 2023 using the SAE HB Beta model. To maintain consistency with direct estimates at the provincial level, which have shown sufficiently low RSEs, a benchmarking process will be conducted using the difference benchmark method. Based on the findings, the difference benchmark method enhances the validity of the estimation results using the SAE HB Beta model.
Analisis Sentimen Berbasis Aspek Pada Ulasan Google Maps Pulungan, Ridson Al Farizal; Nugroho, Wisnu Adi Agung
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2483

Abstract

West Papua is home to several internationally renowned tourist destinations, such as Raja Ampat and Cenderawasih Bay, making the tourism sector one of the key drivers of regional economic growth. However, restrictions on mobility and social interaction during the Covid-19 pandemic led to a significant decline in tourist arrivals in the province. Post-pandemic recovery strategies thus require timely and location-specific data. Google Maps Reviews represent a form of big data that is both up to date and geographically precise, making it useful for assessing and improving the quality of tourism services. This study employs the IndoBERT model for sentiment classification (None, Neutral, Positive, and Negative) across four aspects of tourism: attraction, facilities, accessibility, and price, as reflected in Google Maps reviews. The selected model demonstrates high performance, achieving an F1-score of 71.30% and an accuracy of 93.25%. Findings reveal that the pandemic significantly influenced visitor sentiment, evidenced by a rise in negative reviews during and after the pandemic. This suggests that existing recovery strategies have not been fully effective. Word cloud and thematic map analyses further indicate that the absence or inadequacy of basic facilities and poor accessibility are the primary complaints among tourists. Conversely, the price aspect remained relatively stable, with no substantial increase in negative sentiment, indicating that tourists are more sensitive to service quality than cost. These findings underscore the urgent need for comprehensive improvements in infrastructure and accessibility to support the post-pandemic recovery of tourism in West Papua.
Co-Authors Ibad, Syaikhul Japany, Adham Malay Nugroho, Wisnu Adi Agung Rodliyah, Ratih Salis, Dian Rahmawati Yogi Ramadhan