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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE STRING (Satuan Tulisan Riset dan Inovasi Teknologi)
Shedriko Shedriko
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Computer Science & IT Mathematics

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Perbandingan Algoritma SVM dan KNN dalam Mengklasifikasi Kelulusan Mahasiswa pada Suatu Mata Kuliah Shedriko Shedriko
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 6, No 2 (2021)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3.042 KB) | DOI: 10.30998/string.v6i2.9160

Abstract

Two different algorithms will use different approaches. Like KNN (K-Nearest Neighbour) and SVM (Support Vector Machine), with the former calculates the closest distance between two instances, while the latter calculates the existence of hyperplane that separates two resulted classes. By using train data consisting of anomaly or noise data and using real measurement normally given by a lecturer to the students in the class to determine the students’ pass or fail in one subject, the research finds the difference between the two algorithms. The research is conducted in the University of XYZ on IIT (Introduction to Information Technology) subject. With the use of Orange Data Mining software, the research aims to give information about the suited algorithm for prediction or classification of student’s success in a related subject or others. It uses quantitative analysis with KNN dan SVM algorithms methods. Based on an assessment of several parameters, KNN is better than SVM, but SVM is better than KNN in obtaining a passing threshold value.  
BINARY LOGISTIC REGRESSION IN DETERMINING AFFECTING FACTORS STUDENT GRADUATION IN A SUBJECT Shedriko Shedriko
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol 4 No 1 (2021): Jurnal Teknologi dan Open Source, June 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i1.1401

Abstract

Good communication and coordination between lecturers are needed in delivering material by different lecturers to ensure the relatively uniform quality of education. Knowing the success information from several classes to predict other classes, should be completed by significant parameters used in the algorithm. This research is using a quantitative analysis method with binary logistic regression methodology in determining critical factors of train data on “Introduction to Information Technology” subject in the university of XYZ. Several statistical testing are conducted to give the expected results using software excel with Real Statistics add-ins and Orange Data Mining in testing the pass-prediction from the given data training. The successive model can also be used to classify graduation for the different subjects.
Perbandingan Optimizer Adagrad, Adadelta dan Adam dalam Klasifikasi Gambar Menggunakan Deep Learning Shedriko Shedriko; Muhammad Firdaus
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 1 (2023)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i1.16564

Abstract

Image recognition technology has developed rapidly in recent times. There are many methods springing up in its use. One of them is the Convolutional Neural Network (CNN) as used in this research. The method is used to detect image patterns from the shape of the arrangement of the fingers of one hand as a signal from the identification of the numbers 0 to 9 in SIBI (Indonesian Sign Language System). The problem of the research is that many optimizers emerge in a deep learning method. Therefore, selecting the right optimizer itself is a challenge that can be used as the next reference for input images that do not go through the previous pre-processing stage. The aim of the research is to get the best accuracy score from the comparison of 3 optimizers and their relations to processing time. The conclusion obtained shows that AdaDelta optimizer that has existed for a long time can provide better results than Adam which is the development of the last optimizer.
Co-Authors Muhammad Firdaus