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All Journal Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi Knowledge Engineering and Data Science
Wako Uriu
Chikushi Jogakuen University
Computer Science & IT Earth & Planetary Sciences Education Engineering

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Performance of Ensemble Classification for Agricultural and Biological Science Journals with Scopus Index Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rosyid; Agung Bella Putra Utama; Wako Uriu
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p137-142

Abstract

The ensemble method is considered an advanced method in both prediction and classification. The application of this method is estimated to have a more optimal output than the previous classification method. This article aims to determine the ensemble's performance to classify journal quartiles. The subject of agriculture was chosen because Indonesia is an agricultural country, and the interest of researchers in this field shows a positive response. The data is downloaded through the Scimago Journal and Country Rank with the accumulation in 2020. Labels have four classes: Q1, Q2, Q3, and Q4. The ensemble applied is Boosting and Bagging with Decision Tree (DT) and Gaussian Naïve Bayes (GNB) algorithms compiled from 2144 instances. The Boosting meta-ensembles used are Adaboost and XGBoost. From this study, the Bagging Decision Tree has the highest accuracy score at 71.36, followed by XGBoost Decision Tree with 69.51. The third is XGBoost Gaussian Naïve Bayes with 68.82, Adaboost Decision Tree with 60.42, Adaboost Gaussian Naïve Bayes with 58.2, and Bagging Gaussian Naïve Bayes with 56.12 results. This paper shows that the Bagging Decision Tree is the ensemble method that works optimally in this subject classification. This result suggests that the ensemble method can still fail to produce an ideal outcome that approaches the SJR system.
Development of MOOC content with STEM approach and its influence on students’ geographical literacy Purwanto Purwanto; Felix Andika Dwiyanto; Wako Uriu
Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi Vol 29, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um017v29i12024p60-77

Abstract

This research aims to develop MOOC content with a STEM approach using the Schoology Learning Management System (LMS) and apply the developed product to assess its impact on students' geographical literacy abilities. The research falls under the category of Research and Development (R&D) with a mixed-method analysis. The development model used in this study is ASSURE. The research design implemented the one-group pretest-posttest design with the N-gain score test when a significant difference was found between the average pretest and posttest scores through the paired sample T-Test. The N-gain score was then used to determine the product’s effectiveness based on Heke’s category. Then, the trial subjects were selected by purposive sampling method based on the location and school criteria. The data collection instruments used were interviews and questionnaires. The study's findings show that the MOOC content developed using a STEM approach on the LMS Schoology is highly commendable, with a score of 98.8 percent. Furthermore, the analysis indicates a significant improvement in students' geographical literacy skills, as demonstrated by the N-gain test results, which show a high gain score of 0.7105. These outcomes confirm that using the STEM approach in MOOC content within the LMS Schoology is effective in enhancing students' geographical literacy.
Co-Authors Agung Bella Putra Utama Aji Prasetya Wibawa Felix Andika Dwiyanto Harits Ar Rosyid Nastiti Susetyo Fanany Putri Purwanto Purwanto