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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Talitakum : Jurnal Pendidikan Kristen Anak Usia Dini
Purba, Susi
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Religion Humanities Computer Science & IT Education Electrical & Electronics Engineering

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PENGARUH METODE PERMAINAN TRADISIONAL ENGKLEK TERHADAP PENINGKATAN BERHITUNG ANAK USIA 5-6 TAHUN DI TK BINA KREATIF SIPOHOLON Purba, Susi
JURNAL TALITAKUM: JURNAL PENDIDIKAN KRISTEN ANAK USIA DINI Vol 1 No 1 (2022): Talitakum: Jurnal Pendidikan Kristen Anak Usia Dini
Publisher : PRODI PKAUD, IAKN TARUTUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69929/talitakum.v1i1.4

Abstract

Abstract This study aims to determine how big the effect of increasing numeracy skills in children after doing the traditional engklek game method on children aged 5-6 years at TK Bina Kreatif Sipoholon, This study uses an experimental approach, with a population of all children in TK Bina Kreatif Sipoholon totaling 30 people with a sample of 30 people. Data were collected using a closed questionnaire of 30 items compiled by the author based on variable indicators according to expert theory. Questionnaire trials were conducted on 30 children aged 5-6 years outside the research sample, and their validity and reliability were tested. The results of data analysis showed that there was a positive and significant effect on increasing numeracy skills in children after using the traditional engklek game method for children aged 5-6 years at TK Bina Kreatif Sipoholon with a coefficient of determination (r2) = 17.90% and tested for the normality of the X variable. median 49.2 mean = 50.9 mode 49.251, and Y variable normality test mean = 40.2 mode = 31.5 median = 36. rcount > rtable 0.424 > 0.361 t-test > ttable 2.476 > 2.048, Y regression test = 11.56 + 0.562 Keywords: Game Method, Counting Skills, Engklek Game
A Benchmark Study of Protein Embeddings in Sequence-Based Classification Simanjuntak, Humasak Tommy Argo; Siahaan, Lamsihar; Margaretha, Patricia Dian; Manurung, Ruth Christine; Purba, Susi; Lumbantoruan, Rosni; Barus, Arlinta; Gonzales, Helen Grace B.
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77389

Abstract

Proteins play a vital role in various tissue and organ activities and play a key role in cell structure and function. Humans can produce thousands of proteins, each consisting of tens or hundreds of interconnected amino acids. The sequence of amino acids determines the protein's 3D structure and conformational dynamics, which in turn affects its biological function. Understanding protein function is very important, especially for biological processes at the molecular level. However, extracting or studying features from protein sequences that can predict protein function is still challenging: it takes a long time, is an expensive process, and has yet to be maximized in accuracy, resulting in a large gap between protein sequence and function. Protein embedding is essential in function protein prediction using a deep learning model. Therefore, this study benchmarks three protein embedding models, ProtBert, T5, and ESM-2, as a part of function protein prediction using the LSTM Model. We delve into protein embedding performance and how to leverage it to find optimal embeddings for a given use case. We experimented with the CAFA-5 dataset to see the optimal embedding model in protein function prediction. Experiment results show that ESM-2 outperforms from ProtBert and T5. On training, the accuracy of ESM-2 is above 0.99, almost the same as T5, but still above ProtBert. Furthermore, testing on five samples of protein sequence shows that ESM2 has an average hit rate of 93.33% (100% for four samples and 66.67% for one sample).
Co-Authors Barus, Arlinta Gonzales, Helen Grace B. Humasak T. A. Simanjuntak Manurung, Ruth Christine Margaretha, Patricia Dian Rosni Lumbantoruan Siahaan, Lamsihar