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All Journal International Journal of Advances in Applied Sciences Jurnal Ilmiah FIFO Bina Insani ICT Journal Jurnal Teknik Informatika STMIK Antar Bangsa JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Business Intelligence (IJUBI) Jurnal Sistem Informasi dan Informatika (SIMIKA) Akrab Juara : Jurnal Ilmu-ilmu Sosial Abditeknika - Jurnal Pengabdian Kepada Masyarakat Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Bulletin of Information Technology (BIT) Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Jurnal Pengabdian Kreatif Cemerlang Indonesia Software Development Digital Business Intelligence and Computer Engineering Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Jurnal Informatika Dan Tekonologi Komputer Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA)
Muhammad Muharrom
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Journal : Bulletin of Information Technology (BIT)

 1 
Analisis Penggunaan Orange Data Mining untuk Prediksi Harga USDT/BIDR Binance Muhammad Muharrom
Bulletin of Information Technology (BIT) Vol 4 No 2: Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i2.654

Abstract

The results of implementing Orange Data Mining for predicting USDT/BIDR prices are displayed in the Test and Score widget. In the conducted test, RMSE and MAE values were obtained for each model. The K-Nearest Neighbor (K-NN) method had RMSE and MAE values of 0.002 and 0.002, while the Support Vector Machine (SVM) method had RMSE and MAE values of 0.0003 and 0.002. The Linear Regression method had RMSE and MAE values of 0.0000 and 0.000. Based on these RMSE and MAE values, it can be concluded that the Linear Regression method is the best method for predicting changes in USDT/BIDR prices compared to the K-Nearest Neighbor and SVM methods. Further research is needed to investigate this best method for future studies. It is recommended that future research compares the Linear Regression method with other methods using Orange tools or implements other relevant tools.
Analisis Komparasi Algoritma Data Mining Naive Bayes, K-Nearest Neighbors dan Regresi Linier Dalam Prediksi Harga Emas Muharrom, Muhammad
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.986

Abstract

The results of implementing Orange Data Mining for forecasting the value of the Gold Price are displayed on the Test and Score widget. RMSE and MAE values were obtained from each model from the test. The RMSE and MAE values for the K-Nearest Neighbor (K-NN) method are 0.007 and 0.006, respectively, while for the Support Vector Machine (SVM) method are 0.006 and 0.005. The RMSE and MAE values for the Linear Regression method are 0.004 and 0.003, respectively. Compared to the K-Nearest Neighbor and SVM methods, the Linear Regression method is the best at predicting changes in Gold prices based on the RMSE and MAE data mentioned above. For future research, this best practice method needs to be studied more deeply. It is recommended for future research to compare the Linear Regression method with alternative approaches using the Orange tool set or other related tools.
Co-Authors Achmad Sumbaryadi, Achmad Ade Setiawan Adi Candra, Adi Aditya, Aditya Pratama Afif Saktiansyah Akmaludin Akmaludin, Akmaludin Aulia, Annisa Bonni Nurtanaya Bonny Mulyawan Cut Dara Devi, Anggita Sari Dimas Indra Andhika Dwi Yuni Utami Edhi Prayitno Edhi Prayitno, Edhi Ellya Verawati Faiz, Zulfa Al Harmono, Feri Prasetyo Herlawati Herlawati Jafar, Rhaishudin Rumandan Juarni Siregar Juniar, Muhammad Deva Kadinar Novel KOMALASARI, YULI Kudiantoro Widianto, Kudiantoro M. Fariz A, M. Fariz A Moh. Erkamim Mulyawan, Bonny Musriatun Napiah Nababan, Dina Madelin Nicodias Palasara Niesa, Chaeroen Nuhdin, Burhan Nunung Hidayatun Rafli Achmad Sah, Andrian Saktiansyah, Afif Samudi Tika Adilah M Tirta, Lusi Dara Yuli Komalasarii Yuniar, Rivaldi Nazar