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All Journal Sistemasi: Jurnal Sistem Informasi Jambura Journal of Biomathematics (JJBM) Jurnal Ekonomi Indonesia
Jane, Giani Jovita
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics

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Measuring ocean physical asset account using machine learning approaches Jane, Giani Jovita; Tasriah, Etjih; Pramana, Setia
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 13 No 3 (2024): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v13i3.563

Abstract

The blue economy concept has been adapted as a strategy in setting development programmes and public policies in managing Indonesia’s marine resources. As a supporting instrument, accurate field data is needed when compiling the ocean account. Meanwhile, the support of qualified resources is needed during the field data collection process. Research on mapping water areas using satellite technology and machine learning techniques in producing water maps, especially in coastal areas. The approach is suitable for arranging a physical asset account, which is a component of the ocean account framework. So far, no research has implemented these developments to produce ocean physical asset account. Therefore, this study will cover in arranging the account by utilising Sentinel-2 imagery and implementing Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost) machine learning methods, which according to previous studies are superior methods for mapping water areas. The modelling results show that there isan extensive change in coral, seagrass, and mixed ecosystem types (a combination of coral, seagrass, and macroalgae ecosystems) between 2020 and 2023.
Coastal Ecosystem Classification Using Satellite-Based Machine Learning Approaches Jane, Giani Jovita; Alifatri, La Ode; Tasriah, Etjih; Pramana, Setia
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30466

Abstract

Sebagai negara kepulauan yang kaya akan sumber daya alam, Indonesia memiliki potensi ekonomi kelautan yang besar. Untuk mempertahankan potensi ekonomi ini dalam jangka panjang, ekonomi biru diperlukan sebagai konsep dalam menetapkan program pembangunan dan kebijakan publik. Salah satu cara untuk mengimplementasikan konsep tersebut adalah dengan menyusun neraca laut, yang kerangka kerjanya mengimplementasikan konsep ekonomi biru dalam bentuk neraca lingkungan. Neraca laut dapat dianggap mendukung pembentukan kebijakan dan program nasional suatu negara. Oleh karena itu, data spasial yang akurat yang mencerminkan kondisi terkini sangat penting untuk menyusun neraca ini. Namun, pengumpulan data tersebut dapat memakan biaya dan sumber daya yang besar, sehingga menjadi tantangan untuk memastikan ketersediaan informasi yang terkini dan akurat. Dalam konteks ini, sumber data alternatif dapat memberikan solusi yang layak. Penelitian sebelumnya telah berhasil membuktikan bahwa pemodelan pembelajaran mesin juga citra satelit Sentinel-1 dan Sentinel-2 mampu memetakan wilayah pesisir, seperti wilayah pasang surut dan bentik. Oleh karena itu, penelitian ini mencoba mengklasifikasikan ekosistem pesisir Taman Nasional Karimunjawa dengan memanfaatkan citra Sentinel-1 dan Sentinel-2 dan membandingkan hasil klasifikasi dari tiga metode pembelajaran mesin, yaitu Random Forest (RF), Support Vector Classification (SVC), dan Extreme Gradient Boosting (XGBoost), dan menganalisis perubahan ekosistem antara tahun 2020 dan 2023. Hasilnya menunjukkan bahwa RF memberikan hasil terbaik dalam melakukan klasifikasi untuk daerah bentik yang mencapai 0,77 dan 0,78 dalam skor F1 dan Koefisien Korelasi Matthew (MCC), sedangkan model SVC berhasil mencapai 0,83 dalam skor F1 dan MCC memberikan hasil terbaik untuk daerah pasang surut. Selanjutnya, luas terumbu karang dan padang lamun menurun masing-masing sebesar 6,524 km 2 dan 1,39 km 2 . Sedangkan, luas mangrove, kawasan terbangun, dan hutan menunjukkan sedikit perubahan.
Utilizing Google Trends Data to Examine the Impact of Unemployment Rates on Indonesia's Gross Domestic Product Jane, Giani Jovita; Hasabi, Rafif; Purnatadya, Sinatrya Dwi; Kartiasih, Fitri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.3603

Abstract

Abstract Data related to the economy have varying frequencies and have delays in publication time. Such as data on the Open Unemployment Rate (OUR) with a semi-annual frequency and Gross Domestic Product at Constant Prices (riil GDP) according to expenditure with a quarterly frequency. So, frequency conversion is required to conduct simple regression modelling using these data. On the other hand, big data such as Google Trends is an additional predictor to estimate OUR and GDP data to overcome delays in publication time. Then the estimated data is modelled to investigate the effect of OUR on GDP. Data conversion uses the Chow-Lin method, while estimation with Google Trends data uses robust regression. The study shows that the estimation results using Google Trends as an additional predictor provide more accurate results than without Google Trends data for OUR and GDP data. Based on the robust regression results, it can be concluded that the OUR has a negative and significant effect on GDP. The findings provide valuable insights for supporting sustainable economic policy and further research on economic analysis.
Co-Authors Alifatri, La Ode Fitri Kartiasih Hasabi, Rafif Pramana, Setia Purnatadya, Sinatrya Dwi Tasriah, Etjih