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All Journal Journal Of Artificial Intelligence And Software Engineering
Mustainul Abdi
Politeknik Negeri Lhokseumawe
Computer Science & IT

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Aplikasi Manajemen Literasi Membaca dan Rekomendasi Buku Berbasis Android Menggunakan Metode Content-Based Filtering Sharhan Anhar; Muhammad Khadafi; Mustainul Abdi
Journal of Artificial Intelligence and Software Engineering Vol 4, No 1 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i1.5400

Abstract

Membaca buku adalah sebuah kegiatan yang dapat membantu kita untuk menambah pengetahuan dan wawasan. Minat baca di Indonesia saat ini cenderung rendah, yang disebabkan oleh beberapa faktor seperti diantaranya kurangnya waktu luang, kurangnya referensi buku untuk menentukan buku yang ingin dibaca, dan semakin canggih kemajuan teknologi yang menyebabkan orang lebih suka menghabiskan waktu untuk bermain handphone. Untuk menyelesaikan permasalahan tersebut dapat dilakukan dengan menggunakan aplikasi manajemen literasi membaca dan rekomendasi buku berbasis Android. Tujuan dari aplikasi ini adalah untuk memudahkan dalam mengelola kegiatan membaca serta memberikan rekomendasi buku yang bisa dibaca selanjutnya dan mengingatkan pengguna untuk membaca buku. Metode yang digunakan pada sistem ini adalah Content-Based Filtering. Dengan adanya aplikasi ini, pengguna bisa dengan mudah men-track progress membaca buku. Penerapan algoritma Content-Based Filtering pada aplikasi ini dapat memberikan buku-buku rekomendasi yang relevan dengan pengguna berdasarkan pengujian kuesioner yang dilakukan pada 10 responden dengan nilai rata-rata 3.92 dari skala hingga 5. Dengan hasil tersebut algoritma Content-Based Filtering dapat digunakan untuk memberikan rekomendasi buku.  Abstract Reading books is an activity that can help us to increase knowledge and insight. Reading interest in Indonesia today tends to be low, which is caused by several factors such as lack of free time, lack of book references to determine which books to read, and increasingly sophisticated technological advances that cause people to prefer to spend time playing cellphones. To solve these problems, it can be done by using an Android-based reading literacy management and book recommendation application. The purpose of this application is to make it easier to manage reading activities and provide recommendations for books that can be read next and remind users to read books. The method used in this system is Content-Based Filtering. With this application, users can easily track the progress of reading books. The implementation of the Content-Based Filtering algorithm in this application can provide recommended books that are relevant to users based on questionnaire testing conducted on 10 respondents with an average value of 3.92 on a scale of up to 5. With these results the Content-Based Filtering algorithm can be used to provide book recommendations.
Application of the Random Forest Method for UKT Classification at Politeknik Negeri Lhokseumawe Al Khaidar; Muhammad Arhami; Mustainul Abdi
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6131

Abstract

Classification is the systematic grouping of objects, ideas, books, or other items into specific classes based on similar characteristics. One of its applications is in the grouping of tuition fees, which are fees paid each semester or academic year based on the student's economic ability. However, there are several issues, such as the placement of underprivileged students into fee groups that are still not appropriate and the limited accuracy of the grouping process due to it being done manually. To address these issues, a classification system was designed using the Random Forest method. Random Forest is a machine learning algorithm that combines multiple decision trees for more accurate predictions. Testing the Random Forest method using cross-validation shows an average accuracy of 95%. Evaluation with a confusion matrix shows an accuracy of 94%, with varying values of precision, recall, and f1-score for each group.
Co-Authors Al Khaidar Arhami, Muhammad Muhammad Khadafi Sharhan Anhar