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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Tecnoscienza Jurnal Indonesia : Manajemen Informatika dan Komunikasi International Journal Software Engineering and Computer Science (IJSECS) Jurnal Nasional Cosphi Nusantara of Engineering (NOE) Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Journal Of Informatics And Busisnes Empiris Jurnal Pengabdian Pada Masyarakat (EJPPM) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Andi Bode
Universitas Ichsan Gorontalo
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic Bode, Andi; Lamasigi, Zulfrianto Y; Drajana, Ivo Colanus Rally
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1381.118-123

Abstract

Academic services are actions taken by state and private universities to provide convenience for student’s academic activities. During the current covid-19 pandemic, every university remains active in academic activities. This study aimed to apply the K-Nearest Neighbor algorithm in predicting the level of student satisfaction with online lectures at University Ichsan Gorontalo. Our main aim was to obtain quantitative information to measure student satisfaction with online lectures during the pandemic, which should be taken into account when making decisions. K-Nearest Neighbor is a non-parametric Algorithm that can be used for classification and regression, but K-Nearest Neighbor are better if feature selection is applied in selecting features that are not relevant to the model. Feature Selection used in this research is Forward Selection and Backward Elimination. Seeing the results of experiments that have been carried out with the application of the K-nearest Neighbor algorithm and the selection feature, the results of the forecasting can be used for consideration or policy in decision making. The highest level of accuracy in the K-Nearest Neighbor algorithm model used Forward Selection with an accuracy rate of 98.00%. Thus, the experimental results showed that feature selection, namely forward selection, was a better model in the relevant selection variables compared to backward elimination.
Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor Lamasigi, Zulfrianto Yusrin; Bode, Andi
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.1025.322-333

Abstract

Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data and image testing of batik motifs, the K-Nearest Neighbor classification method will be used based on texture feature extraction value obtained. In this experiment, 5 experiments will be carried out based on angles 0degrees, 45degrees, 90degrees, 135degrees, and 180degreesusing the values of k is1, 3, 5, and 7. The confusion matrix will be used to calculate the accuracy level of the K-Nearest Neighbor classification. From the results of experiments carried out using training data as many as 607 images and testing as many as 344 images in five classes used with angles of 0 degrees, 45degrees, 90degrees, 135degrees, 180degrees, and values of k are 1, 3, 5, and 7, getting the highest accuracy results is at an angle of 135degreesand 180degreeswith a value of k is 1 of 89.24% and the lowest is at an angle of 90degreeswith a value of k is 3 of 67.44%. This shows that the Gray level co-occurrence matrix method is good for extracting the texture features of batik motifs from five major islands in Indonesia, it is evidenced by the results of the average accuracy of the classification obtained.
Co-Authors Abd Rahmat karim Haba Abdul Yunus Labolo Ahmad Asdiranto A Marisang Azminuddin I. S. Azis Balok, Admin Budy Santoso Dedi Setiawan Djailani, Moh. Adrian Drajana, Ivo Colanus Rally Fatma YR Dunggio Idris, Irma Surya Kumala Ikbal Iskandar Irma Surya Kumala Idris Irvan A. Salihi Ivo Colanus Rally Drajana Jorry Karim Labolo, Abdul Yunus Merin Nurlaisa Abbas Mochammad Sakir Moh. Adrian Djailani Moh. Yasir Mohamad Efendi Lasulika Mooduto, Sarlis Muhamad, Azhar Muis Nanja Nur, Muh Jabal Nur, Muh. Jabal Posumah, Risky Randa Ramadhani, Syarifah Fitrah Risky Randa Posumah Rofiq Harun Sarlis Mooduto Serwin Siska Udilawaty Sunarto Taliki Swastiani Dunggio Syahrir Abdussamad Syarifah Fitrah Ramadhani Talodo, Moh. Arif N Tangahu, Rizqi Adiputera Utiarahman, Siti Andini Warid Yunus Yasin Aril Mustofa Yulianti Lasena Yurike Musa Zohrahayaty Zulfrianto Yusrin Lamasigi