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All Journal EKONOMIS : Journal of Economics and Business Journal of Indonesian Tourism, Hospitality and Recreation SABBHATA YATRA : Jurnal Pariwisata dan Budaya
Septiani, Adeline Vinda
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Religion Arts Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Languange, Linguistic, Communication & Media Social Sciences Other

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Pemodelan ARIMA-GARCH dalam Peramalan Kurs Rupiah Terhadap Yen dengan Masalah Keheterogenan Ragam Meilania, Gusti Tasya; Septiani, Adeline Vinda; Erianti, Efita; Notodiputro, Khairil Anwar; Angraini, Yeni
Ekonomis: Journal of Economics and Business Vol 8, No 1 (2024): Maret
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/ekonomis.v8i1.1294

Abstract

The currency exchange rate is the price of a country's currency expressed into another country's currency. At the beginning of 2020, the COVID-19 pandemic affected the weakening and changes in the Rupiah exchange rate against hard currencies, one of which was the Japanese Yen. This affects the expectations of LCS cooperation between Indonesia and Japan in terms of increasing the value of trade to investment between the two countries. Therefore, forecasting the upcoming currency exchange rate is indispensable to determine the upcoming macroeconomic policy. ARIMA is a commonly used quantitative method to forecast future data using past data patterns. The weakness of this method arises when the data violates the assumption of homogeneity of variety that often occurs in financial data, one of which is currency exchange rate data. The ARCH/GARCH model is an effective model for data with uncertain diversity characteristics. However, there is potential to combine ARIMA and ARCH/GARCH into an ARIMA-ARCH/GARCH hybrid model to obtain forecasting results with greater accuracy. In this study, the minimum return data on the Indonesian Rupiah (IDR) exchange rate against the Japanese Yen (JPY) shows the results that the ARIMA(0,0,1) model provides RMSE accuracy of 0.008. While the best forecasting model that can be used to forecast the maximum return data of the IDR exchange rate against JPY is ARIMA(1,0,0)-GARCH(1,1) with a small RMSE accuracy of 0.014. The forecasting results for the minimum return data for buying and selling are expected to strengthen the exchange rate. Meanwhile, the forecasting results for the maximum return data for buying and selling are expected to experience exchange rate weakening.
ANALISIS SENTIMEN ULASAN GOOGLE MAPS SEBAGAI BIG DATA UNTUK EVALUASI DAN PENGEMBANGAN PARIWISATA KABUPATEN BELITUNG TIMUR Septiani, Adeline Vinda; Irsyadinnas
SABBHATÃ YATRA : Jurnal Pariwisata dan Budaya Vol 6 No 2 (2025): SABBHATA YATRA : Jurnal Pariwisata dan Budaya
Publisher : STABN Raden Wijaya Wonogiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53565/sabbhatayatra.v6i2.2236

Abstract

Pariwisata merupakan sektor strategis yang berperan penting dalam meningkatkan perekonomian daerah dan kesejahteraan masyarakat. Kabupaten Belitung Timur memiliki potensi wisata alam, budaya, dan religi yang beragam, namun tren kunjungan wisatawan dalam lima tahun terakhir menunjukkan fluktuasi yang signifikan. Penelitian ini bertujuan untuk menganalisis persepsi wisatawan terhadap sembilan destinasi wisata utama di Kabupaten Belitung Timur melalui pendekatan analisis sentimen berbasis big data menggunakan ulasan Google Maps periode 2020–2025. Penelitian ini menggunakan desain kuantitatif dengan metode analisis teks, melibatkan 1.933 ulasan yang telah melalui tahap pembersihan dan pra-pemrosesan data. Analisis dilakukan menggunakan pendekatan lexicon-based sentiment analysis dengan bantuan perangkat lunak Python untuk mengklasifikasikan sentimen menjadi positif, negatif, dan netral serta visualisasi word cloud untuk mengidentifikasi kata kunci dominan. Hasil penelitian menunjukkan bahwa 44,07% ulasan bersentimen positif, 50,23% netral, dan 5,7% negatif. Destinasi dengan sentimen positif tertinggi adalah Vihara Dewi Kwan Im (70,1%), sedangkan ulasan negatif tertinggi terdapat pada Museum Andrea Hirata (13,4%). Temuan ini menegaskan pentingnya inovasi pengalaman wisata dan peningkatan kualitas fasilitas guna memperkuat daya saing pariwisata Belitung Timur secara berkelanjutan.
Analisis sentimen ulasan Google Maps pada daya tarik wisata Belitung Timur: Pemanfaatan Big Data untuk rekomendasi pengembangan pariwisata daerah Septiani, Adeline Vinda; Irsyadinnas, Irsyadinnas
Journal of Indonesian Tourism, Hospitality and Recreation Vol 9, No 1 (2026): April
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jithor.v9i1.90635

Abstract

AbstractTourism plays an important role in economic growth, cultural identity, and community welfare. East Belitung Regency has diverse tourism potential; however, data from 2020–2024 indicate fluctuations in visitor numbers due to the COVID-19 pandemic and management issues related to tourism attractions. This study analyzes tourist perceptions of nine major tourism attractions through Google Maps reviews, utilizing big data to generate regional tourism policy recommendations. Data were collected through SerpAPI scraping for the 2020–2025 period, yielding 1,933 valid reviews analyzed using a lexicon-based sentiment analysis and word cloud visualization in Jupyter Notebook. The results show that neutral sentiment dominates (50.23%), followed by positive (44.07%) and negative (5.7%). Vihara Dewi Kwan Im and Pantai Serdang emerge as leading tourism attractions, while Kampoeng Fifi and the Replika SD Laskar Pelangi are dominated by neutral reviews, indicating the need for attraction innovation. Meanwhile, the Museum Andrea Hirata and Pantai Nyiur Melambai record relatively higher negative reviews related to pricing, facilities, and cleanliness. Word cloud analysis confirms that natural beauty, spiritual value, and cultural icons are the main strengths, while cleanliness and facility management remain key challenges. These findings highlight the importance of online review big data as an early warning system for adaptive and sustainable tourism policy. AbstrakPariwisata berperan penting dalam pertumbuhan ekonomi, identitas budaya, dan kesejahteraan masyarakat. Kabupaten Belitung Timur memiliki potensi wisata beragam, tetapi data 2020–2024 menunjukkan fluktuasi kunjungan akibat pandemi COVID-19 dan masalah pengelolaan daya tarik wisata. Penelitian ini menganalisis persepsi wisatawan terhadap sembilan daya tarik wisata utama melalui ulasan Google Maps, dengan memanfaatkan big data untuk rekomendasi kebijakan pariwisata daerah. Data diperoleh melalui scraping SerpAPI periode 2020–2025, menghasilkan 1.933 ulasan valid yang dianalisis menggunakan sentiment analysis berbasis kamus dan visualisasi word cloud pada Jupyter Notebook. Hasil menunjukkan sentimen netral mendominasi (50,23%), diikuti positif (44,07%) dan negatif (5,7%). Vihara Dewi Kwan Im dan Pantai Serdang menjadi daya tarik wisata unggulan, sedangkan Kampoeng Fifi dan Replika SD Laskar Pelangi didominasi ulasan netral sehingga memerlukan inovasi atraksi. Museum Andrea Hirata dan Pantai Nyiur Melambai mencatat ulasan negatif lebih tinggi terkait harga, fasilitas, dan kebersihan. Analisis word cloud menegaskan keindahan alam, nilai spiritual, dan ikon budaya sebagai kekuatan utama, sementara kebersihan dan manajemen fasilitas tetap menjadi tantangan. Temuan ini menekankan pentingnya big data ulasan daring sebagai early warning system untuk kebijakan pariwisata adaptif dan berkelanjutan. 
Co-Authors Angraini, Yeni Erianti, Efita Irsyadinnas Irsyadinnas, Irsyadinnas Meilania, Gusti Tasya