Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program

Abstract

Informatics study program at Nurul Jadid University does not have a general concentration of knowledge, so that sometimes the selection of elective courses by students is not quite right. This study aims to classify the concentration of knowledge with a data mining approach which can then be used as a recommendation for selecting elective courses by students. In this study, we implement a machine learning algorithm to provide recommendations to students regarding what interests are more suitable to be taken based on the values ​​of prerequisite courses in previous semesters. Student data was obtained from the Head of the Center for Data and Information Systems (PDSI) at Nurul Jadid University with 70 student data from Nurul Jadid University batch 2018. The machine learning algorithm used is Neural Network with Python programming language, the tools used are Google Collab. At the beginning of data collection, then pre-processing is carried out to prepare the dataset in order to get good results, and model training is carried out. After training on the model, then further testing is carried out on the model to determine the performance of the model. The result of the accuracy value in the training model process is 0.83 or 83% and the accuracy of the test data is 0.79 or 79%.

Identifikasi Tingkat Kesadaran Pengemudi dari Data Video dengan Menggunakan Convolutional Long Short Term Memory

“…Metode yang umum digunakan untuk mendeteksi kantuk pengemudi adalah dengan menggunakan model pembelajaran mesin. Pembelajaran mesin adalah bagian dari bidang kecerdasan buatan (Artificial Intelligence) yang melibatkan pemrograman untuk mengaktifkan komputer agar dapat berperilaku cerdas seperti manusia, bahkan mereka dapat meningkatkan pemahaman mereka melalui pengalaman yang diperoleh secara otomatis selama pelatihan [3]. Akan tetapi model pembelajaran mesin biasanya hanya dapat mengenali pola dari sebuah data tanpa memperhatikan deret waktu pada data sekuensial atau time series.…”

Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Rumah Layak Atau Tidak Layak Huni (Studi Kasus: Desa Bulu Kecamatan Kraksaan Kabupaten Probolinggo)

“…Berikut beberapa teknik pengelolaan data pada penelitian ini, yaitu seleksi atribut, transformasi data, pembersihan data, serta normalisasi data. [8]…”