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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Telematika : Jurnal Informatika dan Teknologi Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jiko (Jurnal Informatika dan komputer) Jurnal Teknologi Terpadu Nusantara Science and Technology Proceedings International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal
Muhaimin, Amri
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : Nusantara Science and Technology Proceedings

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Intermittent Data Forecasting using Kernel Support Vector Regression Muhaimin, Amri; Setyowati, Endah; Maulida H, Kartika; Sari, Allan Ruhui Fatma
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4105

Abstract

Forecasting involves making future estimates. Forecasting methods are commonly employed to predict stock prices, monetary distribution, and weather conditions. To generate accurate forecasts, it is crucial that the data used is consistent, comprehensive, and unchanging. Some data can be readily predicted, while some poses a considerable challenge. An illustration of this is found in discontinuous data, which is notably hard to forecast. Discontinuous data is marked by frequent instances of zero values due to sporadic events. For instance, when tracking the sales of aircraft or other products, sales do not transpire daily, causing recorded data to often register as zero. Various techniques have been explored to handle this kind of data. In this particular study, the chosen method is support vector regression. This method is capable of predicting discontinuous data with a quality level of 1.004, which is lower than traditional approaches like exponential smoothing.
Sentiment Analysis in Social Media: Case Study in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Abstract

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.
Forecasting The Number of Traffic Accidents in Purbalingga Regency on 2023 Using Time Series Model Trimono; Muhaimin, Amri; Selayanti, Nabilah
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4168

Abstract

Accident data from Satlantas Purbalingga Regency shows that in 2022 there is an increase in the number of traffic accidents in the Purbalingga Regency. In the future, the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant, residual independent test, residual normality test, and the smallest Mean Square Error value. According to data forecasting results the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So the necessary action from the relevant agencies to cope with the increasing number of traffic accidents in the Purbalingga Regency.
Co-Authors Abdul Hamid Alamiyah, Syifa Syarifah Damaliana, Aviolla Terza Dwi Arman Prasetya Endah Setyowati Fahrudin, Tresna Maulana Hanif Assalmi, Fityan Heidy Arviani Kusuma, Ade Lisanthoni, Angela Maulida H, Kartika Prabowo, Hendri Rahmawati, Adinda Aulia Regita Putri Permata Sa'diyah, Ilmatus Sari, Allan Ruhui Fatma Sari, Allan Ruhui Fatmah Selayanti, Nabilah Sri Hidayati Suhartono Syaifullah Jauharis Saputra, Wahyu Taufik, Ikbar Athallah Trimono Trimono, Trimono Widiwurjani