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39 results on '"Annas, Suwardi"'

3. Simulation of the Abbaji Ammotere Case for Silariang Using Mathematical Modeling in Bugis Society

Author

Rudi, Annas Suwardi, Side Syafruddin, Irwan, and Mulbar Usman

Subjects
Information technology, T58.5-58.64
Abstract

Silariang is a form of deviant behavior that is consciously carried out by men and women by taking shortcuts in marriage without getting the blessing of the family. The causes of silariang are not getting parental approval, cash problems, promiscuity, pregnancy out of wedlock. Abbaji ammotere is an alternative solution to improve relationships with family. This research aims to build a SEIR models. This model is divided into four classes, namely the class of people who have the potential to carry out silariang, the class of people who are affected by carrying out silariang, the people who carry out silariang, and the people who return to their parents (Ammotere Abbaji). The data used is primary data obtained by distributing questionnaires to 204 samples which were carried out offline for silariang perpetrators and online for vulnerable and affected communities. The real data results of the SEIR type model produce a basic reproduction number (R0) of 0,20381767, which means that the range class will experience a significant increase in the sense that at any time they will be affected and practice silariang, while for classes that have practiced silariang this will decrease over a certain period of time.

Published
2024
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4. Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

Author

Ahmar, Ansari Saleh, Guritno, Suryo, Abdurakhman, Rahman, Abdul, Awi, Alimuddin, Minggi, Ilham, Tiro, M. Arif, Aidid, M. Kasim, Annas, Suwardi, Sutiksno, Dian Utami, Dewi, S. Ahmar, Kurniawan, H. Ahmar, Ahmar, A. Abqary, Zaki, Ahmad, Abdullah, Dahlan, Rahim, Robbi, Nurdiyanto, Heri, Hidayat, Rahmat, Napitupulu, Darmawan, Simarmata, Janner, Kurniasih, Nuning, Abdillah, Leon Andretti, Pranolo, Andri, Haviluddin, Albra, Wahyudin, and Arifin, A. Nurani M

Subjects
Statistics - Methodology
Abstract

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data., Comment: 13 pages

Published
2018
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9. Stroke type classification model based on risk factors using resilient backpropagation neural networks.

Author

Annas, Suwardi, Aswi, Aswi, Abdy, Muhammad, Poerwanto, Bobby, and Fa'rifah, Riska Yanu

Subjects
*STROKE, *VASCULAR diseases, *HEMORRHAGIC stroke, *DISEASE risk factors, *ISCHEMIC stroke, *BLOOD sugar
Abstract

Stroke is a disease among the top 10 causes of death in the world and Indonesia. Stroke is divided into two, namely ischemic stroke and hemorrhagic stroke. Stroke can happen to anyone at any age with the characteristics of blood vessels experiencing blockage or rupture resulting in a crisis of blood supply that carries oxygen to the brain. The risk factors for stroke are the same as heart disease or other blood vessel diseases such as hypertension, diabetes, and cholesterol. One way to prevent stroke is to minimize several diseases that cause clogged or ruptured blood vessels. This study aims to predict the results of the classification of stroke types based on 6 factors, namely age, gender, cholesterol levels, length of stay (LOS), history, and blood sugar levels of stroke patients. The classification method chosen is one part of machine learning, namely the resilient backpropagation neural network (RBNN) because it is in accordance with the type of data used. The results showed that the prediction results of stroke type classification using six predictors were included in the good category and reached the optimum at the use of 5 nodes in the hidden layer and the resulting error was 9.26. The results of the evaluation of classification predictions using the confusion matrix also get good results, namely with an accuracy of 80% and an F1-score of 82% with a precision of 72% and a sensitivity of 95%. This result will be more optimal if there is the handling of imbalanced data. [ABSTRACT FROM AUTHOR]

Published
2023
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32. New naphthoquinone derivatives from Fusarium napiforme of a mangrove plant.

Author

Supratman, Unang, Hirai, Nami, Sato, Shiho, Watanabe, Keiko, Malik, Abdul, Annas, Suwardi, Harneti, Desi, Maharani, Rani, Koseki, Takuya, and Shiono, Yoshihito

Subjects
MANGROVE plants, NAPHTHOQUINONE, PLANT-fungus relationships, FUSARIUM, PSEUDOMONAS aeruginosa, ENDOPHYTIC fungi
Abstract

Two new naphthoquinone derivatives, 6-hydroxy-astropaquinone B (1) and astropaquinone D (2) as well as the known compound 3-O-methyl-9-O-methylfusarubin (3) were isolated from Fusarium napiforme, an endophytic fungus isolated from the mangrove plant, Rhizophora mucronata. The structures of 1 and 2 were determined by 1D and 2D NMR spectroscopic analyses. Compounds 1, 2 and 3 exhibited moderate antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa. Furthermore, 1, 2 and 3 were phytotoxic action in lettuce seeding at a concentration of 30 μg · mL−1. [ABSTRACT FROM AUTHOR]

Published
2021
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