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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Language and Literature
-, Sutarman
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Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology

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SARCASTIC EXPRESSIONS IN MERIAQ-MERIQU DIALECT OF SASAK LANGUAGE: PRAGMATIC STUDY -, Sutarman
Humanitatis : Journal of Language and Literature Vol. 5 No. 1 (2018): Humanitatis: Journal of Language and Literature
Publisher : LPPM Universitas Bumigora Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/humanitatis.v5i1.451

Abstract

This research aims at studying the pragmatic use of sarcasm in Meriaq-Meriqu dialect of Sasak language. This research is a qualitative research. The data are collected via two techniques – participant observation and unstructured interview. The data are then analyzed via interactive model which consists of three main points: data reduction, data presentation and data clarification. The results show that there are three kinds of sarcastic expressions in Meriaq-Meriqu – word form, phrasal form and clause form. In word form, the reference used as sarcastic word is mostly from animal, characters and parts of body while the reference in phrasal forms is mostly taken from part of body, animal and characters. The clause form of sarcasm is divided into two kinds- direct and indirect sarcasm. In terms of functions, sarcasms have five functions- expression of intimacy, informal address, expression of anger, insinuation and humiliation.
Applying Feature Selection and Ensemble Models to Classify Complex Data Using Lasso Regression and Random Forest in Medical Datasets Nasution, Putri Khairiah; -, Sutarman; Sirait, Katrin Jenny
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 2, October 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss2.art7

Abstract

The rise of big data has posed major challenges for the healthcare industry on how to analyze intricate medical datasets in order to drive improved patient outcomes and clinical decision making. This paper examines the enhancement of complex medical data classification through ensemble models that are integrated with feature selection techniques such as RFE, LR, and RF. Our objectives are to enhance predictive performance with model interpretability by taking advantage of the strengths of these methods. In this research, we compare RFE, LR and RF on all three medical datasets (Breast Cancer, Pima Diabetes and Chronic Disease Classification) to investigate how effective they are in finding relevant features and how well their ensemble models can run. The findings demonstrate that, RF produces the best performance out of all methods by far, and the performance is even better when it is combined with RFE since RF + RFE performs best on all 6 datasets. Though ensemble models offer enhanced stability, their advantages vary according to the situation, and they may not yield better performance than the best single model in every case. Also, synergism between linear and nonlinear implementations was minimal. Through this study, we demonstrate that applying an appropriate feature selection approach and classification model to the complexity of the medical data is key. These findings provide important insights for improving predictive modeling in health care to inform relevant clinical decision-making and improve patient outcomes. Further studies can inves- tigate more ensemble approaches as well as applying it to a greater data set, which would improve applicability and generalizability
Co-Authors Katrin Jenny Sirait Nasution, Putri Khairiah