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All Journal KADIKMA Sharia Oikonomia Law Journal Journal Collabits
Anggraini, Devi Dwi
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Mathematics

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PENGEMBANGAN INDIKATOR 4C’s YANG SELARAS DENGAN KURIKULUM 2013 PADA MATA PELAJARAN MATEMATIKA SMA/MA KELAS XII SEMESTER I Anggraini, Devi Dwi; Sunardi, Sunardi; Kurniati, Dian
Kadikma Vol 8 No 3 (2017): Desember 2017
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v8i3.6905

Abstract

Abstract. This research aims to describe development process of 4C’s indicators that in accordance the curriculum of 2013 in mathematics subject of first semester XII grade of Senior High School/MA. According to P21 4C’s abilites are critical thinking, communication, collaboration, and creativity.Mathematics subjects of first semester XII grade of Senior High School/MA which consist of 3 subjects namely plane geometry, three dimensional objects geometry, ang mathematical statistics. This research based on modified plomp model. This research only uses four phases namely inital investigation phase, designing phase, realisation/construction phase, and test, evaluation, and revision phase. Validity test was conducted by 5 validators which consists of 2 mathematics education lecturer and 3 mathematics teacher. From the data analysis can be concluded that 4C’s indicator that were developed are valid. Keywords: 4C's Indicators, Curriculum 2013, Plomp's Development Model
The Role of Corporate Governance and Financial Performance on the Value of ISSI Indexed Food and Beverage Companies in Indonesia Rahma, Sukma Aulia; Nur, Silva Maulida; Anggraini, Devi Dwi; Kunasari, Kunasari; Andni, Riyan; Sironi, Manuela
Sharia Oikonomia Law Journal Vol. 1 No. 2 (2023)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/solj.v1i2.109

Abstract

This study comprises food and beverage firm listed on the ISSI index for the year 2017 through 2021 and is driven by the impact of corporate governance and financial performance on company value on the Indonesia Stock Exchange (IDX). The purpose of this study was to investigate the relationship between financial success measured by ROA (return on Asset) and ROE (Return on Equity) and Managerial Ownership (MO), Institutional Ownership (IO), Independent Commissioners (IC), and Audit Commissioners (AC), all of which are factors in good corporate governance. The methode used is quantitative axplanatory using multiple linear regression analysis and sampling using purposive sampling with reference to the two theories applied to research which consist of planned signaling theory and agency relationship theroy. There is the application of Good Corporate Governance in the analysis of a company’s finances and there are important components in the form of Return of Asset (ROA), Return of Equity (ROE), Good Corporate Governance and Financial Performance, according to Managerial Ownership (MO), Institutional Ownership (IO), Independent Commissioners (IC), and the Audit Committee (AC), can enhance a company’s worth. Despite having a favorable impact and not being statistically significant, return on assets, managerial ownership, institutional ownership, and independent commissions all have positive effects.
A Data Science Approach to Cancer Patient Classification Using Support Vector Machine and Random Forest Anggraini, Devi Dwi; Salsabila, Mutiara Rizky; Kamila, Keisya Rizkia; Sari, Yunita Sartika
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37642

Abstract

The increasing availability of healthcare data has encouraged the application of data science and machine learning techniques in medical research. Cancer patient datasets contain numerical demographic and clinical attributes that can be utilized for classification tasks; however, complex feature relationships and limited feature relevance remain key challenges. This study aims to analyze cancer patient data and compare the performance of Support Vector Machine and Random Forest algorithms for gender classification. The dataset used in this study consists of numerical features, including patient age, tumor size, number of examined lymph nodes, number of positive lymph nodes, body mass index, and survival duration measured in months. The research methodology includes data preprocessing, exploratory data analysis, model development, and performance evaluation. Feature normalization and data splitting are applied to ensure a fair comparison between models, while exploratory analysis is conducted to examine data distribution and relationships among variables. Both classification models are trained under identical experimental settings and evaluated using accuracy as the primary performance metric. The results indicate that both algorithms can classify cancer patients with satisfactory accuracy. Support Vector Machine demonstrates slightly better performance compared to Random Forest, suggesting its effectiveness in handling numerical data with complex decision boundaries. The findings highlight the importance of appropriate algorithm selection and feature utilization in healthcare data analysis.
Co-Authors Andni, Riyan Dian Kurniati Kamila, Keisya Rizkia Kunasari, Kunasari Nur, Silva Maulida Rahma, Sukma Aulia Salsabila, Mutiara Rizky Sari, Yunita Sartika Sironi, Manuela Sunardi Sunardi