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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Slamet Wiyono
Politeknik Harapan Bersama, Tegal, Indonesia
Computer Science & IT Engineering

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Mobile-based Assignment Reminder Application for Students and Lecturers Kukuh Yulian Santoso; Taufiq Abidin; Slamet Wiyono
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.285 KB) | DOI: 10.55123/jomlai.v1i2.962

Abstract

This research is motivated by the large number of student activities that sometimes make students forget or overlook the activities they have to do on time. One of the activities that sometimes forget or even get overlooked is assignments. Assignments are activities carried out by a group of people in carrying out learning activities. The purpose of this study is to design an android-based task reminder application that can remind students about lecture assignments, be able to remember students about the deadline for assignment collection, and other information regarding lecture activities. The application design method used is the waterfall method. Research This study was tested using white-box and black-box methods. The test results show that the application is correct, has no errors in terms of logic and function, and can functionally produce the expected output.
Comparison of Borda and NRF (Normalized Rating Frequency) in Recommender System Taufiq Abidin; Slamet Wiyono
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (225.707 KB) | DOI: 10.55123/jomlai.v1i3.1026

Abstract

The Collaborative Filtering method is a popular method in making recommender systems. Although CF is a popular method, it has major problems, namely cold start and sparsity . Several studies have been conducted to treat cold starts and sparsity. One way to overcome cold start and sparsity is the Borda calculation method. Research using the Borda method has been carried out a lot but has not utilized the rating optimally. The NRF method is a new method offered to maximize the use of ratings. By using dummy test data, the NRF method is more effective than Borda in calculating recommendation scores.
Co-Authors Kukuh Yulian Santoso Taufiq Abidin