Gadjah Mada University

About: Gadjah Mada University is a education organization based out in Yogyakarta, Indonesia. It is known for research contribution in the topics: Population & Adsorption. The organization has 17307 authors who have published 21389 publications receiving 116561 citations. The organization is also known as: University of Gajah Mada & Universitas Gadjah Mada.

Kajian Model Empiris Perilaku Berwirausaha UKM DIY dan Jawa Tengah

Abstract: This research aims to test the entreprenurial behavior model. Subject in this research is micro enterprise entrepreneur in DIY and East Java. Sample selection is performed based on purposive sampling, there are 344 responder which have been fulfilling the conditions needed. Data collecting conducted by disseminating questionare. The analyzed use the Structural Equation Model (SEM). Result of research shows that the model is fit. Entrepreneurial attitude, subjective norms and self efficacy have influence to entrepreneurial behavior through intention. Self efficacy by parsial don’t have significant effect to entrepreneurial behavior and antrepreneurial intention.

Evaluasi Penetapan Kawasan Andalan: Studi Empiris di Kalimantan Selatan 1993-1999

Abstract: This paper attempts to evaluate government policy to classify a region as a key region (kawasan andalan) with special reference to South Kalimantan province. Using location quotient and logistic regression, we showed that the policy designed and based merely on regional income per capita and key subsector. The policy seems, to ignore the growth of regional income and regional specialisation. Our analysis also suggests that regional classification based on Klassen Typology is a better alternative than that of the ad-hoc “key region”. Key words: kawasan andalan, LQ, logistic regression, Klassen Typology

Isotopic evidence for initial coastal colonization and subsequent diversification in the human occupation of Wallacea

Abstract: The resource-poor, isolated islands of Wallacea have been considered a major adaptive obstacle for hominins expanding into Australasia. Archaeological evidence has hinted that coastal adaptations in Homo sapiens enabled rapid island dispersal and settlement; however, there has been no means to directly test this proposition. Here, we apply stable carbon and oxygen isotope analysis to human and faunal tooth enamel from six Late Pleistocene to Holocene archaeological sites across Wallacea. The results demonstrate that the earliest human forager found in the region c. 42,000 years ago made significant use of coastal resources prior to subsequent niche diversification shown for later individuals. We argue that our data provides clear insights into the huge adaptive flexibility of our species, including its ability to specialize in the use of varied environments, particularly in comparison to other hominin species known from Island Southeast Asia.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Abstract: Background: Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective: This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods: Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results: Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions: Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106