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All Journal Jupiter Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmu Komputer, Sistem Informasi, Teknik Informatika (JILKOMSITI)
Yusuf Irianto, Suhendro
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science

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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
Pemanfaatan SIG Untuk Model Pemetaan Zona Potensial Penangkapan Ikan di Perairan Lampung Rachmadi, Rachmadi; Triloka, Joko; Yusuf Irianto, Suhendro; Sriyanto, Sriyanto
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13994437

Abstract

The waters in the Lampung region have significant potential as fishing areas. Fisheries potential in the Lampung region is supported by various factors, including a climate that supports plankton growth and a fertile ecosystem, as well as a variety of habitats that are suitable for various types of fish. In addition, traditional coastal activities such as fishing, boat fishing, and fish farming in ponds are also an important part of the local economy. However, so far fisheries management is still not optimal, it is important to manage fisheries potential optimally and sustainably. This research aims to present a geographic information system modeling regarding weather changes in the Lampung region and its surroundings in order to optimize fishing activities in Lampung waters. Data analysis uses Aqua MODIS data extraction as well as wind data gridding and description. The research results show that the waters in Lampung Province have significant potential regarding the presence of fish based on SST distribution.   Keywords— Geographic information system, weather data, lampung waters, aqua modis  
Perbandingan Performa Algoritma Naive Bayes, Random Forest dan K-Nearest Neighbor pada Prediksi Calon Jemaah Haji Indonesia yang Berpotensi Membatalkan Haji Setiadi, Feri; Widi Nugroho, Handoyo; Yusuf Irianto, Suhendro
JURNAL ILMU KOMPUTER, SISTEM INFORMASI, TEKNIK INFORMATIKA Vol 5 No 1 (2026)
Publisher : PT Akom Media Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengidentifikasi model klasifikasi yang paling efektif dalam memprediksi calon jemaah haji yang berpotensi membatalkan pendaftaran hajinya serta menentukan fitur-fitur yang paling berpengaruh terhadap keputusan tersebut. Penelitian ini membandingkan tiga model klasifikasi, yaitu Naïve Bayes, Random Forest, dan K-Nearest Neighbor (k-NN), dengan menggunakan dataset jemaah haji dari Siskohat Kantor Kementerian Agama Kabupaten Pringsewu. Selain itu, penelitian ini juga menerapkan metode seleksi fitur Recursive Feature Elimination Cross Validation (REFCV) untuk mengidentifikasi fitur-fitur yang paling relevan dalam mempengaruhi pembatalan haji. Hasil penelitian menunjukkan bahwa model Random Forest memberikan performa terbaik dengan akurasi, presisi, dan recall yang lebih tinggi dibandingkan model Naïve Bayes dan k-NN, baik sebelum maupun setelah seleksi fitur. Fitur-fitur seperti 'usia', 'pekerjaan', dan 'alamat' ditemukan sebagai atribut yang paling signifikan dalam mempengaruhi pembatalan haji. Penerapan metode REFCV terbukti meningkatkan akurasi model, khususnya pada model Random Forest yang mencapai akurasi 95% setelah seleksi fitur. Penelitian ini menyimpulkan bahwa model Random Forest dengan seleksi fitur REFCV merupakan kombinasi yang paling efektif dalam memprediksi pembatalan pendaftaran haji, serta memberikan rekomendasi bagi pengelola haji dalam meningkatkan akurasi prediksi dan efisiensi pengelolaan data jemaah haji.
Co-Authors Abda Abda Rachmadi, Rachmadi Setiadi, Feri Sri Lestari Sriyanto Triloka, Joko Widi Nugroho, Handoyo