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Mandita, Fridy
Universitas 17 Agustus 1945 Surabaya
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A Child Growth and Development Evaluation Using Weighted Product Method Januantoro, Ardy; Mandita, Fridy
Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.7613

Abstract

Child development is one of the factors that must be considered in improving a country's education. The level of maturity of human resources is able to maximize starting from childhood. The guidebook of the Ministry of Education and Culture of the Republic of Indonesia (Kemendikbud RI) in 2018 contained six indicators to assess children's learning ability, namely: 1) Moral, 2) Social, 3) Language, 4) Cognitive, 5) Motor, and 6) Art. This study implements these indicators to evaluate children's growth and development. The evaluation method uses the Weighted Product Method (WPM). WPM provides a ranking of the result of the evaluation. In addition, WPM also has an assessment of Beneficial and non-beneficial as a more relevant assessment between indicators. Data were collected by questionnaire at kindergarten schools with the respondents' age average of 5-6 years. The results will be calculated with indicators criteria weights given. The test results recommended for students between 0.65 to 0.62 are as follows: Mahmud, Diko, Cindy, Denny, and Riko. The kindergarten manager can use these recommendations to increase the student's aptitude.
PEMBUATAN RANCANG BANGUN PENERIMAAN SISWA BARU SEBAGAI SARANA PENINGKATAN LAYANAN PADA TK LIYA CIPUNEGARA SURABAYA Mandita, Fridy; Januantoro, Ardy
Jurnal Berdaya Mandiri Vol. 5 No. 2 (2023): JURNAL BERDAYA MANDIRI (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v5i2.4794

Abstract

Education is a key aspect in improving Human Resources (HR). Education starts at an early age so that future generations are ready to face increasingly fierce and highly competitive competition. Liya Kindergarten (TK) is one of the educational institutions that participates in providing educational services and focuses on early childhood education. The age range of Kindergarten students is expected to be able to meet the competency criteria for entering Elementary School (SD). In providing information services to the public regarding Kindergarten Liya, it is often difficult to provide up-to-date information to the public regarding the acceptance of new students. This information is in the form of information on school activities, registration information, curriculum information and information on education costs. From these problems, the creation of a new student admissions design for schools is expected to be a solution to the problem to assist the process of registering new students. The website can provide services quickly and accurately with simple steps in registering new students. The method used in this community service is interviews with related users at the location of the community service, namely Liya Kindergarten for a certain period of time and at the end of the community service there is training on using the website to build new student acceptance for users. With this website, it is hoped that it can improve the quality of services at Kindergarten Liya to be able to provide accurate information for people who need it. In addition, it can be a means of promotion related to Kindergarten Liya and on the other hand it can increase the school's income. Keywords: Kindergarten, new student admissions, service quality, information system
PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOR DAN NAÏVE BAYES DALAM MEMPREDIKSI WAKTU KELULUSAN MAHASISWA Mandita, Fridy
Jurnal Dinamika Informatika Vol. 12 No. 2 (2023): Jurnal Dinamika Informatika Vol.12 No.2
Publisher : Universitas PGRI Yogyakarta

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Abstract

Students are an important aspect for higher education institutions, especially regarding the time of student graduation. Therefore, it is critical to know the prediction of the time length for completing studies. This study proposes creating a prediction system for student graduation rates; hence it could be a preventive measure for students to improve their learning process. This research used machine learning techniques to compare the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The experiment aimed to determine the best model, such as the amount of data collection, the number of classification classes, and the handling of imbalanced classes. Based on all experiments, the KNN method achieved higher results than the Naïve Bayes method. Applying the SMOTE oversampling technique significantly increased the difference in evaluation scores (precision, recall, F1 score, and accuracy) between 12% and 41% in the Naïve Bayes and KNN methods. The results of the 4-class prediction model using the KNN method with SMOTE get a precision value of 79%, a recall value of 78%, an F1 score of 78%, and an accuracy of 78%. In comparison, the prediction results for eight classes using the KNN method with SMOTE get precision, recall, F1 Score, and accuracy values of 93%.
Co-Authors Agus Hermanto Aidil Primasetya Armin Anton Breva Yunanda Bramantyo Kusumo Jati Januantoro, Ardy Lintang Ragadanu Arizona Rizal Kurnia Pratama Supangat Supangat