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All Journal Jiko (Jurnal Informatika dan komputer) Science Map Journal
Munazat Salmin
Universitas Khairun
Chemistry Computer Science & IT Education Mathematics Physics Social Sciences

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PERBANDINGAN MODEL REGRESI WEIBULL DAN REGRESI COX PROPOSIONAL HAZARD Mayawi Mayawi; Nurhayati Nurhayati; Novita Serly Laamena; Ariestha Widyastuty Bustan; Munazat Salmin; Taufan Talib
Science Map Journal Vol 4 No 2 (2022): Science Map Journal
Publisher : Jurusan Pendidikan MIPA FKIP Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jmsvol4issue2pp49-60

Abstract

This study aims to determine the model of Acute Myocardial Infarction using the Weibull Regression and Cox Proportional Hazard Regression methods to determine the factors that significantly influence the length of time for recovery of acute myocardial infarction patients. The results of this study indicate that the factors that significantly influence the recovery time of acute myocardial infarction patients are age, onset, number of secondary diagnoses and duration of pain. Based on the AIC value, the Weibull regression model is the best regression model because the AIC value is smaller, namely 292.883 compared to the Cox Proportional Hazard regression model with an AIC value of 493.3971
AN LSTM-BASED APPROACH FOR INDONESIAN NEWS CATEGORIZATION: PERFORMANCE ANALYSIS OF HYPERPARAMETER TUNING AND PREPROCESSING Iwan La Udin; Firman Tempola; Abdul Mubarak; Muhammad Sabri Ahmad; Munazat Salmin; Saiful Do Abdullah
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10783

Abstract

News disseminated through internet-based systems or news portals is generally classified into specific categories, such as politics, sports, economy, entertainment, technology, health, and others. Currently, this categorization is performed manually, requiring a thorough reading of the entire news content. To address this inefficiency, an automatic classification system for Indonesian news articles is necessary to categorize them based on predetermined categories. This research employs a Natural Language Processing (NLP) approach and implements the Long Short-Term Memory (LSTM) architecture. The study was conducted using several testing scenarios, including (1) hyperparameter tuning of the learning rate to 0.01 and 0.001, (2) the application and omission of stemming, and (3) various dataset comparison ratios of 60:40, 70:30, 80:20, and 90:10. The evaluation utilized a dataset of 10,000 articles across 5 categories and was measured using accuracy, precision, recall, and f-measure metrics. From the three scenarios, seven training models were generated. The second model, with a learning rate of 0.001, without stemming, and a 90:10 dataset ratio, achieved the highest accuracy of 90.7%, with average precision, recall, and f-measure scores of 91%. The third and fourth models, which applied stemming, did not demonstrate a performance improvement, both yielding an accuracy of 89%. The fifth model, with a 60:40 dataset ratio, produced an accuracy of 90%, while the sixth and seventh models, with 70:30 and 80:20 ratios, resulted in accuracies of 79% and 88%, respectively.
Co-Authors Abdul Mubarak Bustan, Ariestha Widyastuty Iwan La Udin Laamena, Novita Serly Mayawi Mayawi Muhammad Sabri Ahmad Nurhayati Saiful Do Abdullah Taufan Talib Tempola, Firman