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All Journal Jurnal Sains Dasar TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan IPA Indonesia
R. Kusumawati
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Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Formation of neural network models for time series data with intervention and its application in CPI data R. Kusumawati; D. U. Wutsqa; R. Subekti
Jurnal Sains Dasar Vol 3, No 2 (2014): October 2014
Publisher : Faculty of Mathematics and Natural Science, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (229.56 KB) | DOI: 10.21831/jsd.v3i2.4167

Abstract

The purpose of this study is to get an optimal forecasting model of CPI data in Yogyakarta using neuralnetwork models that is built with the involvement of the intervention effect. This case happens because of the policyof rising fuel prices. CPI education, recreation, and sport have a pattern like a step function, so that the exact RNNmodels are the models with the input as the truncated polynomial spline regression models. The results of modelpredictions both training and testing data showed high accuracy. Key words: neural network, intervention, CPI
The Effect of Differentiated Science Inquiry Learning Model Based on Teaching at the Right Level on Students’ Critical Thinking and Science Process Skills Ramlawati, Ramlawati; Sari, Nur Indah; Kusumawati, R.; Yesin, M.; Ilmi, N.; Arsyad, Arie Arma
Jurnal Pendidikan IPA Indonesia Vol. 14 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v14i1.19479

Abstract

Student cognitive diversity can be a barrier to enhancing critical thinking and science process skills. The TaRL-based DSI model addresses this by facilitating learning for students at different cognitive levels. This research aims to determine the effect of the Differentiated Science Inquiry (DSI) model based on the Teaching at the Right Level (TaRL) approach on eighth-grade junior high school students’ critical thinking and science process skills in the context of vibration and wave concepts. A non-equivalent control group design was used to address varied learning needs, with 55 students purposively selected for experimental and control groups. Data were collected using essay-based critical thinking tests and multiple-choice science process skills assessments. The DSI model applied four inquiry levels: Demonstrated Inquiry, Structured Inquiry, Guided Inquiry, and Self-Directed Inquiry based on students’ initial abilities. Data were analyzed using descriptive and inferential methods. The average critical thinking skills in the experimental group after treatment were 13.04, and in the control group were 10.1. The average science process skills in the experimental group were 19.77, and in the control group were 18. The research results confirm that the TaRL-based DSI model significantly enhances students’ critical thinking and science process skills at a significance level of α = 0.05. This research is expected to help educators implement the DSI model to accommodate diverse learning needs and enhance crucial skills in science education.
Klasifikasi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan Menggunakan Algoritma Naïve Bayes dan Support Vector Machine Umar, Huzaifah; Kusumawati, R.; Imamudin, M.; Rohman, Moh. Ainur
TIN: Terapan Informatika Nusantara Vol 4 No 11 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i11.4969

Abstract

Payment delinquency of SPP is a commonly occurring issue in school. It affects the salary of teachers and staffs alongside school’s various development program. The study aims to classify payment delinquencies using Naïve Bayes and Support Vector Machine. Research methode is Cross-Industry Standard Process for Data Mining (CRISP-DM). Method testing was carried out with 5 trials. Based on the test results, the average performance of Naïve Bayes is accuracy (62,88%), precision (65,27%), recall (77,42%) dan f1-score (70,75%). Meanwhile, the average performance of the Support Vector Machine is accuracy 63,51%), precision (62,25%), recall (94,48%) dan f1-score (75,04%).
Co-Authors Arie Arma Arsyad D. U. Wutsqa Ilmi, N. Imamudin, M. Nur Indah Sari R. Subekti Ramlawati Ramlawati Rohman, Moh. Ainur Umar, Huzaifah Yesin, M.