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All Journal Jurnal Pendidikan Ekonomi Undiksha Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Pendidikan Indonesia WARTA Binus Business Review INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan IRJE (Indonesian Research Journal in Education) WIDYABHAKTI Jurnal Ilmiah Populer Journal of Educational Research and Evaluation International Journal of Natural Science and Engineering Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Surya Abdimas Jurnal Ilmu Manajemen dan Akuntansi Terapan Jurnal Varian Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Educational Development (IJED) Journal of Innovation Research and Knowledge
Gede Suwardika
Universitas Terbuka
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Jurnal Varian

 1 
ANALISIS DATA PANEL PADA KINERJA REKSADANA SAHAM I Gede Agus Astapa; Gede Suwardika; I Ketut Putu Suniantara
Jurnal Varian Vol 1 No 2 (2018)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v1i2.72

Abstract

Mutual funds is another investment opportunity with a more measurable risk as well as return high enough with enough capital is affordable for the community. Mutual fund performance can be measured by several indicators.. Modeling the performance of mutual funds modeled by regression of the data panel. The regression model estimation data panel will do with the three approaches, namely the approach of common effect, fixed effects and random effects. This research purpose to know the performance of mutual funds from stock selection skill variable influences, market timing ability and level of risk with the use of panel data analysis. The results shows that the Fund's performance is affected by the stock selection skill, market timing ability, and the level of risk. Model the right approach to model the performance of mutual funds by using a random effects model.
Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart Gede Suwardika; I Ketut Putu Suniantara; Ni Putu Nanik Hendayanti
Jurnal Varian Vol 2 No 2 (2019)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v2i2.361

Abstract

The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.
Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network I Ketut Putu Suniantara; Gede Suwardika; Siti Soraya
Jurnal Varian Vol 3 No 2 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v3i2.651

Abstract

Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations
Co-Authors Agus Tatang Sopandi Agus Tatang Sopandi Agus Tatang Sopandi Agus Tatang Sopandi Agus Tatang Sopandi Agus Tatang Sopandi Dhimas Setyo Nugroho Dian Rahmani Putri Ersi Sisdianto Hariati Sawitri, Hendrin Hariati Sawitri, Hendrin Hendayanti, Ni Putu Nanik Heri Kuswanto I Gede Agus Astapa I Gede Darsana I Gede Nika Wirawan I Gede Suardika I Ketut Putu Suniantara I Ketut Putu Suniantara I Made Agus Wirahadi Putra I Made Ari Santosa I Made Ari Santosa I Made Hendra M. Pramayasa I Made Rudita I MADE RUDITA I Nyn Suraja Antarajaya I Putu Oktap Indrawan I Wayan Widana Irhamah - Joko Santoso Kadek Masakazu Kadek Masakazu Kadek Masakazu Kadek Masakazu Kadek Masakazu Komang Tri Werthi Lilis Yuningsih LUH PUTU SAFITRI PRATIWI Masakazu, Kadek Masakazu, Kadek Masakazu Ni Nyoman Supuwiningsih Nia Erlina Putra, I Gede Eka Wiantara Putu Oktap Indrawan Putu Oktap Indrawan Rahmani Putri, Dian Siti Soraya Sopandi , Agus Tatang Sopandi, Agus Tatang Sri Wahyuni. M Suniantara, I Ketut Putu T.M. Kusuma Wijaya, I Gusti Ngurah Satria