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Record W7017634665

ANALISIS FAKTOR-FAKTOR
\nYANG MEMPENGARUHI INFLASI
\nDI INDONESIA PERIODE 2000.1 â 2011.4

2012· dissertation· en· W7017634665 on OpenAlexaboutno aff

Bibliographic record

VenueUNDIP Institutional Repository (UNDIP-IR) (Diponegoro University) · 2012
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInflation (cosmology)Exchange rateMoney supplyGross domestic productOrdinary least squaresQuarter (Canadian coin)VariablesVariable (mathematics)Real gross domestic productRegression analysis
DOInot available

Abstract

fetched live from OpenAlex

One of the problems that often occur in developing countries in implementing the country's development is how to maintain stability and economic growth. Economic stability in terms of stability regarding the price level, the level of national income and employment growth. The main objective of this study was to analyze the factors affecting inflation in Indonesia in 2000.1-2011.4 period. The variables used are: gross domestic product (GDP), the money supply in a broad sense (M2), interest rate, Bank Indonesia certificates (SBI), and the exchange rate of rupiah against the U.S. dollar.
\nThe data used in this study is time series data in the quarterly period from 2000.1 to 2011.4, using multiple linear regression with the method of Ordinary Least Square (OLS).
\nThe results of this analysis states that the variable gross domestic product and the SBI rate are positive and significant effect on inflation. While exchange rate are positive and not significant effect on inflation. In the other hand, the variables in the money supply (M2) is negative and significant effect on inflation in the quarter a year of research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.015
GPT teacher head0.189
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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