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All Journal Jurnal Penelitian Komunikasi dan Opini Publik Jurnal Teknologi dan Sistem Komputer Indonesian Journal of Electrical Engineering and Computer Science INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS
Fitriyaningsih, Ike
Institut Teknologi Del
Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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MACHINE LEARNING: PROSPERITY OF RAINFALL, WATER DISCHARGE, AND FLOOD WITH WEB APPLICATION IN DELI SERDANG Ike Fitriyaningsih; Yuniarta Basani; Lit Malem Ginting
Jurnal Penelitian Komunikasi dan Opini Publik Vol 22, No 2 (2018): JURNAL PENELITIAN KOMUNIKASI dan OPINI PUBLIK - Desember 2018
Publisher : BPSDMP Kominfo Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (803.346 KB) | DOI: 10.33299/jpkop.22.2.1752

Abstract

Flood event predictions can provide information to the surrounding community to prepare themselves for future. With the development of informatics, currently web-based applications are very accessible. PHP (Hypertext Preprocessor) is a programming language in the form of a script that can be implemented dynamically with HTML. PHP is used to build web-based applications and is implemented with other software. Software R is a command line based application that can be used to complete Machine Learning calculations quickly. In this study Backpropagation Neural Network (BP-NN) is used to predict rainfall and water discharge. Whereas Support Vector Machine (SVM) is used to predict flood events. The case study data used was Deli Serdang District in North Sumatra which often flooded. In this study, rainfall data was taken from three points or stations. The nearest river water debit is used to also affect flood events. Ensemble Machine Learning (BP-NN and SVM) uses the PHP programming language and R software is used for prediction. Using rainfall data from Kualanamu station, Tuntungan and Sampali as well as Sungai Ular water debit 1 January 2016-31 December 2017, the accuracy of flood prediction from this application is 94.4%.
Prediksi Kejadian Banjir dengan Ensemble Machine Learning Menggunakan BP-NN dan SVM Ike Fitriyaningsih; Yuniarta Basani
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 3, Year 2019 (July 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (211.983 KB) | DOI: 10.14710/jtsiskom.7.3.2019.93-97

Abstract

This study aims to examine the prediction of rainfall and river water debit using the Back Propagation Neural Network (BP-NN) method. Prediction results are classified using the Support Vector Machine (SVM) method to predict flooding. The parameters used to predict rainfall with BP-NN are minimum, maximum and average temperature, average relative humidity, sunshine duration, and average wind speed. The debit of Ular Pulau Tagor river is predicted by BP-NN. BPNN and SVM modeling using software R. Daily climate data from 2015-2017 were taken from three stations, namely Sampali climatology station, Kualanamu meteorological station, and Tuntung geophysics station. Prediction of river water debit is for 6 days and 30 days in the future. The best dataset is a 6 day prediction with a combination of 60% training and 40% testing. Flood prediction accuracy with SVM was 100% in predicting flood events for the next 6 days.
An empirical evaluation of phrase-based statistical machine translation for Indonesia slang-word translator Kyrie Cettyara Eleison; Sari Uli Inggrid Hutahaean; Sarah Christine Tampubolon; Teamsar Muliadi Panggabean; Ike Fitriyaningsih
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1803-1813

Abstract

The use of slang (non-standard language), especially in social media, is increasing. It causes reducing the level of understanding when communicating because not everyone understands slang (non-standard language). The purpose of this work is to develop a slang-word translator. The other objective is to find the minimum number of sentences and BiLingual Evaluation Understudy (BLEU) score used as a benchmark to determine that the translation is understandable. The approach used in this project is a Phrase-based statistical machine translation (PBSMT) approach, suitable for low resource language, with a dataset of 100,000 sentences taken from the comments column of several online political news portals. The comments are then manually translated to produce a parallel corpus of non-standard language-standard language. The sample sentences are taken from the dataset then distributed using questionnaires to obtain the human understanding level regarding the translation result. The result of the implementation is a BLEU score of 64 and the minimum number of sentences to have an understandable machine translation is 500. The conclusion drawn from the distributed questionnaires is that humans can understand the sentences produced by the translation machine.
User Responses Of The Development Of Language Laboratory System Monalisa Pasaribu; Ike Fitriyaningsih; Sopian Manurung; Jane Mitaria Sinambela; Anjelin Hutauruk; David Muliadi Butar-Butar
International Journal of Education, Information Technology, and Others Vol 3 No 2 (2020): International Journal of Education, Information Technology, and Others
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.798 KB) | DOI: 10.5281/zenodo.3965721

Abstract

The data collection of room loans, inventory, and books is an important activity as a means of documentation at the language laboratory of Del Institute of Technology. Using manual process of the borrowing and data collection, possibility of data loss may occur. Moreover, it is also laborious and inefficient in terms of time. An information system is designed to improve the existing manual system in a computerized way that it can be accessed via desktop or mobile. Using agile software in the development the information system, the application can provide complete and efficient loan information stored in a database server. Based on the user responses, the system is built in a more effective, simpler, faster, and a more structurized way.
Co-Authors Anjelin Hutauruk Basani, Yuniarta David Muliadi Butar-Butar Jane Mitaria Sinambela Kyrie Cettyara Eleison Lit Malem Ginting Pasaribu, Monalisa Sarah Christine Tampubolon Sari Uli Inggrid Hutahaean Sopian Manurung Teamsar Muliadi Panggabean