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Didit Budi Nugroho
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Journal of Science and Science Education
Published by Universitas Kristen Satya Wacana
ISSN : -     EISSN : 25983830     DOI : 10.24246
Core Subject : Science, Education,
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics
The Journal of Science & Science Education (JoSSE) publishes academic articles of conceptual, experimental, philosophical, theoretical and applied results, and reviews in the field of mathematics and natural sciences from the following subject areas: - Biology & Biology Education - Chemistry & Chemistry Education - Physics & Physics Education - Mathematics & Mathematics Education - Statistics - Computer Science.
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)" : 5 Documents clear
Selected Plant Extract in Inhibiting the Growth of Bacteria Isolated from Raw Vegetables Junar Sebua Cano
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p1-9

Abstract

In recent years, there has been a gradual revival of interest in the use of plant extract due to the wide spread belief that green plant sources are healthier and safer than the synthetic ones. Hence, the present study was carried out to evaluate the inhibitory effects of the ethanolic extract of Hibiscus rosa-sinensis (Gumamela) flower, Psidium guajava (Guava) leaf, Moringa oleifera (Malunggay) leaf, and Allium sativum (Garlic) clove on the microbial isolates from Brassica oleraceae (Cabbage), Brassica rapa (Pechay), and Cucumis sativus (Cucumber) using agar well diffusion method. Experimental research design was employed in the study. The 75%, 50%, and 25% concentrations of the extract were used as the treatments, while sodium hypochlorite and sterile distilled water were used as the positive and negative controls, respectively. Based on the results, the plant extract showed significant difference (p<0.05 and p<0.0) in inhibiting the growth of bacterial isolates. The 75% concentration of H. rosa-sinensis flower extract, and the 75%, 50%, and 25% concentrations of P. guajava leaf extract were found to inhibit the growth of bacterial isolates on raw vegetables, and their antimicrobial activities differed significantly (p< 0.05). On the other hand, M. oleifera leaf and A. sativum clove extract were found not effective in inhibiting the growth of bacteria on raw vegetables.
Development and Assessment of “Chem this Vlab” Learning Package Alvin Larida
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p10-17

Abstract

This study determined the difficulty level of answering Chemistry problems of the STEM students of Libertad National High School. The respondents of the study were seventy-three (73) purposely selected students for the school year 2020-2021. Thereafter, the researcher developed a Learning Package in Chemistry to augment the learning in the new normal. It will be known as Chem in this VLab Learning Activities that focus on Chemical Solutions, Thermodynamics, and Chemical Kinetics. To validate the quality of the learning package, selected experts assessed the tool on three components: content, technical, and instructional qualities. The results revealed that students had poor performance in answering Chemistry problems and find Chemistry as difficult subject to learn. Therefore, learning activities are developed to aid their understanding on the selected topics. The instructional material was rated as a good instructional tool in learning Chemistry by the experts and recommended to be utilized in the online distance learning modality.
Effects of 3-Dimensional Computer Simulation Secondary School Students’ Academic Achievement in Chemistry Iyabode Nike Ojelade; C G Ekpo; Busayo Gbemisola Aregbesola
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p18-25

Abstract

The study investigated the effect of 3-Dimensional Computer Simulation on Secondary School Students’ Academic Achievement in Chemistry, Ibadan Oyo State. Two research questions and two hypotheses were raised to guide the study. The study adopted pre-test, post-test non-randomized control group design quasi experimental design. The total population of the study comprised of all the 70,843 SSII chemistry students in Ibadan, Oyo State. A simple random sampling technique, using balloting without replacement was used to select two intact classes from the forty-two public secondary schools of mixed gender in the two Local Government Areas. The two schools selected constituted 127 students, 62 students (37 males and 25 females and convention group comprised of 65 students (35 male and 30 female). Two instruments were used to collect data in the study: Chemistry Achievement Test (CAT) and Chemistry Retention Test (CRT) with the reliability coefficients of 0.86 and 0.87 respectively using Kuder Richardson 21 (KR-21). Data were analyzed using mean and standard deviation were used to answer the research questions while t-Test was used to test the null hypotheses. Findings revealed that there was significant difference in the mean achievement score of secondary school students taught chemistry using 3-D computer simulation and their counterparts in control group. There was no significant difference in the mean achievement score of male and female students taught chemistry using 3D-computer simulation. These implied that 3-D computer simulation can be used to enhance students’ academic achievement in chemistry. It was therefore recommended among others that 3-D computer simulation strategy (3-DCS) was found effective in improving students’ academic achievement. Therefore. It can be recommended that curriculum planner can integrate this strategy as one of the strategies recommended for use by chemistry teachers in the curriculum.
Comparison between Multiple Linear Regression Method and K-Nearest Neighbor Method for Regression on Iris Data Adi Setiawan
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p26-35

Abstract

This study aims to determine the statistics used in regression models such as RMSE, MAPE, MAE and R2 using the KNN method for regression. The measure of the goodness of the method used is MAPE. The data used is iris data which has been used by many people as an example of data. Variations in the proportion of test data were carried out by 10%, 20%, 30% and 40%. In the proportion of test data of 20%, successively obtained the results that MAPE for case 1, case 2 and case 3 is 5.885 %, 7.778%, 6.979% while in case 4 is 19.341%. As a result, it is obtained that predictions using the KNN method successfully predict/forecast with highly accurate forecasting in case 1, case 2 and case 3 while in case 4 the KNN method predicts with good forecasting.
Realized Volatility Forecasting for AI-Mining Sensor Data Using the Multi Layer Perceptron Method Obed Christian Dimitrio; Didit Budi Nugroho; Hanna Arini Parhusip; Atyanta Nika Rukmasari
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p36 - 43

Abstract

This study aims to predict the Realized Volatility (RV) value from AI-Mining sensor data for the period 23 May to 6 June 2022 by using the Multi Layer Preceptron (MLP) method. MLP is the simplest method of artificial neural network. Based on the results obtained after doing MLP with the Python language on Google Colab, the predicted RV value for each data shows a movement in value that is almost similar to the original RV value. The Root Mean Squares Error (RMSE) value for each data prediction is relatively small, which indicates that the MLP method provide accurate prediction on the use of the AI-Mining sensor data to forecast RV.

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