Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science Tarling : Journal of Language Education
Salsabila, Aulia
Unknown Affiliation
Computer Science & IT Languange, Linguistic, Communication & Media

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of the Naïve Bayes Algorithm to Predict New Student Admissions Salsabila, Aulia; Nasution, Marnis; Irmayanti, Irmayanti
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5363

Abstract

New student admissions are critical to the success of an educational institution because they determine the existence and financial sustainability of that institution. The number of prospective students who register changes every year. The school cannot anticipate the number of students who will come. Additionally, data on prospective students who enroll is collected annually without being analyzed to extract valuable information. The school must make predictions to estimate the number of new students in the next school year. Predictions are essential for effective planning, both in the long and short term. This research aims to apply the Naïve Bayes algorithm with Gaussian type to predict new student admissions. To find out whether the Naïve Bayes algorithm works well, an evaluation matrix is used. The methods applied include the dataset collection process, data preprocessing, split data training and testing, feature engineering, the implementation of Naïve Bayes, and results evaluation. The dataset is divided into 70% training data and 30% testing data. The research results show an accuracy score of 86.11% during training and an accuracy score of 90.62% during model testing, with an increase of 4.51%. These results show that there is no indication of overfitting in the machine learning algorithm used. The evaluation matrix produces an accuracy score of 90.62%, precision of 100%, recall of 90.62%, and f1-score of 95.08%. From the results of the evaluation matrix scores, it can be concluded that the naive Bayes algorithm with Gaussian type succeeded in predicting new student admissions well.
An Analysis of English Summative Assessment Item of Merdeka Curriculum Based on Bloom's Taxonomy at SMP N 4 Cilacap Salsabila, Aulia; Wahidiyati, Irra
Tarling : Journal of Language Education Vol. 8 No. 1 (2024): Juni 2024
Publisher : Prodi PBA Fakultas Tarbiyah dan Ilmu Keguruan UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tarling.v8i1.10609

Abstract

This study aims to determine the compatibility of the summative assessment item of the Merdeka Curriculum used at SMP N 4 Cilacap. This research uses a quantitative descriptive method based on Bloom's Taxonomy operational verb tables. There are 47 questions for the midterm assessment and 50 questions for the semester assessment. So, there are 97 English summative assessment items given by the teacher and used to measure students' level of understanding. The summative assessment items studied consisted of the 2022/2023 academic year midterm and semester assessments. The results of this research are that questions in the LOTS category have a higher percentage than the HOTS category. In midterm assessment items, questions in the LOTS category reached 78%. Meanwhile, in semester assessment items, the number of items that have the LOTS category is 66%. So, based on this research, items categorized as LOTS dominate the Summative Assessment items at SMP N 4 Cilacap. So, this research shows that the summative assessment items at SMP N 4 Cilacap are incompatible with the Merdeka Curriculum. Based on these results, it is hoped that the preparation of assessment items in the following year will be further improved so that students can think more critically by the level of thinking in the Merdeka Curriculum.
Co-Authors Irmayanti Irmayanti, Irmayanti Nasution, Marnis Wahidiyati, Irra