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All Journal JOIV : International Journal on Informatics Visualization International Journal of Active Learning
Puspasari, Magdalena Dwi
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Computer Science & IT Education

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Cognitive Ability Profiles of Junior High School Students with High Mathematical Abilities in Numbers Material Based on TIMSS Domain Puspasari, Magdalena Dwi; Mampouw, Helti Lygia
International Journal of Active Learning Vol 4, No 1 (2019): April 2019
Publisher : International Journal of Active Learning

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.023 KB) | DOI: 10.15294/ijal.v4i1.20022

Abstract

The study aimed to describe the cognitive ability of junior high school students in answering questions on  whole and fraction numbers based on TIMSS domain. This is a descriptive qualitative research involving three junior high school students with high mathematical ability as the subjects, i.e.: KV, DA, and TE. The data colletion instruments were TIMSS questions on whole numbers and fraction and the interview guidelines. The data were analyzed by using TIMSS cognitive domain in the sections of knowing, applying and reasoning. The results indicated that the knowing stated in written by KV and in mind by DA. Meanwhile TE?s ability was limited to compute fractions and measure. While KV applied mathematical concepts in written, DA and TE applied them verbally. TE was less accurate in using the concept of whole numbers. In the context of reasoning, KV was able to propose various solutions, DA had a single solution to fraction qeustions, and TE was confined to fraction questions. The subjects made their conclusions only in written.
Data Exploration Using Tableau and Principal Component Analysis Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Puspasari, Magdalena Dwi
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.952

Abstract

This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.
Co-Authors Hanna Arini Parhusip Helti Lygia Mampouw Heriadi, Adrianus Herry Santosa, Petrus Priyo Suryasatriya Trihandaru