Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Bina Praja Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IPTEKIN : Jurnal Kebijakan Pembangunan dan Inovasi
Kori Cahyono
Badan Penelitian dan Pengembangan Provinsi Riau
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Penggunaan Media Interaktif Berbasis Web untuk Meningkatkan Motivasi dan Hasil Belajar (Studi Kasus di Universitas Abdurrab Pekanbaru Riau) Kori Cahyono
Jurnal Bina Praja: Journal of Home Affairs Governance Vol. 5 No. 4 (2013)
Publisher : Research and Development Agency Ministry of Home Affairs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21787/jbp.05.2013.243-252

Abstract

AbstrakMotivasi dan hasil belajar adalah dua faktor penting dalam menentukan efektivitas proses pembelajaran. Hasil belajar mata kuliah pemrograman berorientasi objek siswa rendah selama dua tahun terakhir. Hal ini mungkin disebabkan oleh bentuk materi yang masih abstrak sehingga motivasi belajar siswa rendah. Penelitian tindakan kelas bertujuan menggambarkan proses pembelajaran menggunakan media interaktif berbasis web untuk meningkatkan motivasi dan hasil belajar siswa. Obyek penelitian adalah siswa jurusan Teknik Informatika Abdurrab University. Penelitian dilakukan dalam dua siklus penelitian tindakan. Setiap siklus penelitian terdiri dari empat tahap yaitu perencanaan, tindakan, observasi dan refleksi. Untuk mengukur hasil penelitian menggunakan tiga instrumen yaitu kuesioner motivasi, lembar observasi, dan lembar evaluasi hasil belajar. Hasil analisis data menunjukkan bahwa ada peningkatan motivasi siswa sebesar 17,65%, dan peningkatan hasil belajar sebesar 44,12%.AbstractMotivation and learning outcomes are two important factors in determining the effectiveness of the learning process. The results of student learning object-oriented programming is low during the last two years. This may be caused by a form of matter that remains abstract so that students motivation is low. Classroom action research aims to describe the process of learning to use web-based interactive media to enhance student motivation and learning outcomes. Object of research is a student majoring in Computer Science Abdurrab University. The study was conducted in two cycles of action research. Each cycle consisted of four phases of planning, action, observation and reflection. To measure the results of the study used three instruments, namely the motivation questionnaires, observation sheets, and evaluation of learning outcomes sheet. The results of data analysis showed that there was an increase of 17.65% student motivation, and improved learning outcomes for 44.12%.
Moving Objects Semantic Segmentation using SegNet with VGG Encoder for Autonomous Driving Wahyudi Setiawan; Kori Cahyono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 2, May 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i2.1203

Abstract

Segmentation and recognition become the general steps to identify objects. This research discusses pixel-wise semantic segmentation based on moving objects. The data from the CamVid video which is a collection of autonomous driving images. The image data consist of 701 images accompanied by labels. The segmentation and recognition of 11 objects contained in the image (sky, building, pole, road, pavement, tree, sign-symbol, fence, car, pedestrian and bicyclist) is representing. This moving object segmentation is carried out using SegNet which is one of the Convolutional Neural Network (CNN) methods. Image segmentation on CNN generally consists of two parts: Encoder and Decoder. VGG16 and VGG19 pre-trained networks are used as encoders, while decoders are the upsampling of encoders. Network optimization uses stochastic gradient descent of Momentum (SGDM). The test produces the best recognition was road objects with an accuracy of 0.96013, IoU 0.93745, F1-Score 0.8535 using VGG19 encoder, while when using VGG16 encoder accuracy was 0.94162, IoU 0.92309, and F1-Score 0.8535.
Optimasi Penjadwalan Kuliah Di Universitas Lancang Kuning Menggunakan Algoritma Pewarnaan Graf Dan Backtracking Kori Cahyono
Jurnal Kebijakan Pembangunan dan Inovasi Vol. 2 No. 2 (2017)
Publisher : Badań Perencanaan Pembangunan Dearth, Penelitian dań Pengembangan Provinsi Riau

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

Abstract

Penjadwalan kuliah di Universitas Lancang Kuning (Unilak) dilakukan pada setiap semester dan disesuaikan dengan kurikulum yang digunakan. Pembuatan jadwal kuliah merupakan suatu combinatorial complex problem yang membutuhkan waktu dan proses analisa agar dihasilkan jadwal yang tidak cross dengan jadwal lain. Penyusunan jadwal harus memperhatikan syarat-syarat (constaint) dan kriteria optimalisasi penjadwalan, sehingga sangatlah perlu adanya suatu penjadwalan otomatis yang dengan cepat dapat mengantisipasi perubahan dan juga mudah digunakan agar lebih efisien. Salah satu metode heuristik yang telah digunakanuntuk menyelesaikanpermasalahan tersebut adalah dengan menggunakan pendekatan pewarnaan graf (graph coloring) dan untuk memperoleh jadwal yang optimal digunakan algoritma backtracking. Hasil penelitian menunjukkan bahwa algoritma pewarnaan graf dan backtracking dapat menyelesaikan permasalahan optimasi penjadwalan secara terstruktur dan dapat digunakan untuk meningkatkan layanan proses pelaksanaan pembelajaran.
Co-Authors Wahyudi Setiawan