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All Journal Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi INTEGER: Journal of Information Technology Indonesian Journal of Information System
Budianto, Herman
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Klasifikasi Topeng Pandawa dengan SVM Sanjaya, Andi; Setyati, Endang; Budianto, Herman
INTEGER: Journal of Information Technology Vol 5, No 1: April 2020
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (236.222 KB) | DOI: 10.31284/j.integer.2020.v5i1.910

Abstract

Klasifikasi merupakan tahapan tingkat lanjut dari sebuah keilmuan computer vision. Karena tujuan dari sebuah aplikasi rekognisi yaitu mengenali. Cara mengenali yaitu dengan cara klasifikasi. Banyak metode klasifikasi yang ada, namun pada penelitian ini menggunakan Support Vector Machine (SVM). SVM dipilih karena bisa mengatasi data dengan dimensi yang sangat besar tanpa mereduksi data, bekerja dengan data linier atau nonlinier dan membuat sebuah hyperplane yang memisahkan data antar kelas. Pada penelitian ini menggunakan data patung pandawa dengan lima kelas. Lima kelas terdiri dari kelas yudhistira, bima, arjuna, nakula dan sadewa. Kernel yang digunakan pada penelitian ini menggunakan  Radial Basis Function (RBF). Hasil ujicoba pada penelitian mempunya rata-rata akurasi sebesar 0,848.
The Adoption of Blended Learning in Non-Formal Education Using Extended Technology Acceptance Model Kurniawan, Ridho; Pramana, Edwin; Budianto, Herman
Indonesian Journal of Information Systems Vol 4, No 1 (2021): August 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v4i1.4415

Abstract

This study aims to determine the influencing factors for understanding the intention of the learners in Non-Formal Education to use Blended Learning. In addition, it aims to investigate the relationships of the factors in a theoretical model. This study was conducted due to the lack of research in the world that discusses the adoption of Blended Learning in Non-Formal Education in Developing Countries such as Indonesia. Blended Learning at Non-Formal Education in the Covid-19 era is needed because the education institution has a limited place to accommodate more learners. A questionnaire based on google form was used to collect data. A sample of 566 users of Blended Learning from Non-Formal Education Institutions in Indonesia were used.  All variables from the theoretical model are measured using existing scales.  Structural Equation Model (SEM) was used to analyze the theoretical model.  SPSS and Amos were used as the software tools. This research contributes to the theoretical understanding of Blended Learning adoption as well as practice and provide guidance for Non-Formal Education to successfully implementing Blended Learning in their institutions. From the thirteen initial hypotheses, there are nine significant hypotheses. Three hypotheses with the largest magnitude are SI -> PU, CE -> PEU, and PU -> BI.  SI is the most influencing factor in the adoption of blended learning at non-formal education institutions.
Model Architecture of CNN for Recognition the Pandava Mask Sanjaya, Andi; Setyati, Endang; Budianto, Herman
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2029.638 KB) | DOI: 10.25139/inform.v5i2.2740

Abstract

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.
Co-Authors Andi Sanjaya Edwin Pramana, Edwin Endang Setyati, Endang RIDHO KURNIAWAN, RIDHO