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All Journal Jurnal Ilmiah Kursor Jurnal Sistem Informasi dan Informatika (SIMIKA)
Nur Huda Riyantoni
UIN Sunan Ampel Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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KOMPARASI SUPPORT VECTOR MACHINE (SVM) DAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA PERAMALAN HUJAN DI DAERAH ALBURY, AUSTRALIA Nur Huda Riyantoni; Moh. Faqih Bahreisy; Irfan Hakim; Dwi Rolliawati
Jurnal Sistem Informasi dan Informatika (Simika) Vol 6 No 1 (2023): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v6i1.2412

Abstract

Rain is one of the 4 weather conditions on earth. The rain itself is sometimes erratic. For some people, rain is sometimes seen as an obstacle in carrying out their daily activities. Therefore, predictions about rain are needed. Predictions about rain really help people in carrying out their daily activities. The research aims to compare the predictive performance of SVM and ARIMA in forecasting rain. The data used in this case study research is data regarding daily weather for 10 years in the Albury area, Australia from 2008-2017 with a lot of 3040 data. The results obtained from SVM for forecasting rain using daily weather data for 10 years in the Albury area, Australia with the best accuracy rate with the SVM model is 97.532% with an error rate of 2.468%, while in the ARIMA model, the results are MAE 0.181, RMSE 0.254 and MAPE 0.159. So it can be concluded that the ARIMA model has a better performance in predicting rain than the SVM method.
Modified BERTopic using IndoSBERT for topic modeling in Bahasa Nur Huda Riyantoni; Khalid Sjamsuri; Subhan Nooriansyah
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.497

Abstract

The vast amount of textual data -particularly undergraduate theses abstracts produced in the digital age- makes it difficult for readers to identify the topics contained within them. Topic modeling facilitates readers in identifying topics within a collection of textual data. One method for topic modeling is BERTopic. BERTopic is a framework for topic modeling that utilizes the BERT model in embedding stage. This study use IndoSBERT and multilingual SBERT in the BERTopic embedding stage to determine which model performs better in generating topic for a dataset of Indonesian-language undergraduate theses abstract. The topic generated using these IndoSBERT and multilingual SBERT embedding are then evaluated using the topic coherence and topic diversity metrics. The research results show that topics generated by IndoSBERT have higher topic coherence and topic diversity scores, than those generated by multilingual SBERT. These results indicate that IndoSBERT is better in generating topics with topic coherence and topic diversity than multilingual SBERT. The contribution of this research lies in the modification of the BERTopic embedding stage using IndoSBERT for topic modelling in Bahasa. This is because the use of IndoSBERT has so far been limited for text classification task.Key words: Bahasa Indonesia, BERTopic, IndoSBERT, Information System, Topic modelling.
Co-Authors Dwi Rolliawati Irfan Hakim Khalid Sjamsuri Moh. Faqih Bahreisy Subhan Nooriansyah