Pengaruh Utang Luar Negeri dan Fluktuasi Nilai Tukar Rupiah Terhadap Pertumbuhan Ekonomi Indonesia ( Studi Pada Bank Indonesia Tahun 2003-2013)
Bibliographic record
Abstract
Foreign debt to be an alternative for developing countries to cover the budget deficit. In addition, the problem of the exchange rate also affect the economic growth of a country. This research uses quantitative research. This study population using time series data of foreign debt, Rupiah exchange rate and economic growth. Determination of the sample based on time series data in the first quarter of 2003 to the fourth quarter of 2013, as many as 44 samples. The data used were obtained from the official website of Bank Indonesia (www.bi.go.id) and the Central Bureau of Statistics (www.bps.go.id). Analysis of the data used is multiple linear statistical analysis using SPSS 20.0. the result of this study showed that the partial test variable of foreign debt has a significant influence on economic growth, while exchange rates had no significant effect on economic growth. The Simulta neous test result showed that foreign debt and exchange rate has a significant effect on economic growth. Keywords: foreign debt, exchange rates, G ross D omestic P roduct (GDP), economic growth. ABSTRAK Utang luar negeri menjadi alternatif bagi negara berkembang untuk menutupi defisit anggaran tersebut. Selain itu, masalah nilai tukar mata uang juga ikut memengaruhi pertumbuhan ekonomi suatu negara. Pertumbuhan ekonomi menjadi salah satu tolak ukur untuk melihat perkembangan suatu negara. Jenis penelitian ini menggunakan penelitian kuantitatif. Populasi penelitian ini menggunakan data time series utang luar negeri, nilai tukar Rupiah dan Pertumbuhan ekonomi. Penentuan sampel berdasarkan data time series pada kuartal satu dari tahun 2003 sampai kuartal empat tahun 2013, yaitu sebanyak 44 sampel. Data yang digunakan diperoleh dari website resmi Bank Indonesia (www.bi.go.id) dan Badan Pusat Statistik (www.bps.go.id). Analisis data yang digunakan adalah analisis statistik linear berganda menggunakan program SPSS 20.0. Hasil penelitian ini menunjukkan bahwa secara parsial variabel utang luar negeri memiliki pengaruh signifikan terhadap pertumbuhan ekonomi, sedangkan nilai tukar tidak berpengaruh signifikan terhadap pertumbuhan ekonomi. Secara simultan utang luar negeri dan pertumbuhan ekonomi berpengaruh signifikan terhadap pertumbuhan ekonomi. Kata kunci : Utang Luar Negeri, nilai tukar, produk domestik bruto (PDB), pertumbuhan ekonomi.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".