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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Geneologi PAI : Jurnal Ilmiah Bidang Pendidikan Agama Islam Integrated Lab Journal Jurnal Aplikasi Ilmu Teknik Industri (JAPTI) Abdi Masya Educate: Journal of Community Service in Education International Journal of Education and Literature (IJEL) Geo-Image Journal
Sriyanto
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

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Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction Apriadi, Eko Aziz; Sriyanto; Lestari, Sri; Yusuf Irianto, Suhendro
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13418

Abstract

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments
Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms Juwariyem; Sriyanto; Sri Lestari; Chairani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13448

Abstract

Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data includes risk factors related to stunting, such as maternal nutritional status, child nutritional intake, access to health services, sanitation, and other socioeconomic factors. This secondary data analysis can provide an overview of the prevalence of stunting and the contributing factors. To overcome this, the right solution is needed, one solution that can be used is data mining techniques, where data mining can be used to carry out analysis and predictions for the future, and provide useful information for business or health needs. Based on this analysis, this research will use the Bagging method and Random Forest Algorithm to obtain the accuracy level of stunting predictions in toddlers. Bagging or Bootstrap Aggregation is an ensemble method that can improve classification by randomly combining classifications on the training dataset which can reduce variation and avoid overfitting. Random Forest is a powerful algorithm in machine learning that combines decisions from many independent decision trees to improve prediction performance and model stability. By combining the Bagging method and the Random Forest algorithm, it is hoped that it will be able to provide better stunting prediction results in toddlers. This research uses a dataset with a total of 10,001 data records, 7 attributes and 1 attribute class. Based on the test results using the Bagging method and the Random Forest algorithm in this research, the results obtained were class precision yes 91.72%, class recall yes 98.84%, class precision no 93.55%, class recall no 65.28%, and accuracy of 91.98%.
Co-Authors Adi, Arya Andre, Dendy Apriadi, Eko Aziz Baidowi, Achsin Bambang Margono Chairani Darodjat Deni Andriyansyah Dwi, Fajar Faris Salman Faza Jamaldi, Agus Juwariyem Khofifah Dwi Yuliyanti Lia Lindawati M. Fikri Amrullah Putra, Edwin Rezki, Boriro Salsabila Anandara Yahya SRI LESTARI Sri Lestari Suhartoyo Suwarno Wibisono, Otniel Yusuf Irianto, Suhendro Zain, Ghiffary