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All Journal Science and Technology Indonesia Paradigma
Noviyanto
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Environmental Science Materials Science & Nanotechnology Physics

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Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions Noviyanto; Wahyudi, Mochamad; Sumanto, Sumanto
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3306

Abstract

This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.
Biodiversity and Analysis of Antioxidant and Antibacterial Activity of Endophytic Fungi Extracts Isolated from Mangrove Avicennia marina Noviyanto; Widjajanti, Hary; Elfita
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.1.139-151

Abstract

Avicennia marina, a mangrove species commonly found along coastal areas, plays both ecological and pharmacological roles, with its plant parts exhibiting antioxidant and antibacterial activities. This study aimed to investigate the diversity of endophytic fungi from various organs of A. marina collected from mangrove ecosystems and to explore and analyze their antioxidant and antibacterial activities. Endophytic fungi were isolated from the roots, stems, and fruits of A. marina using PDA medium and were morphologically identified. Each fungal isolate was cultivated in PDB medium for 4 weeks under static conditions, followed by extraction to obtain concentrated extracts. Antioxidant and antibacterial activities were assessed using the DPPH method and disk diffusion assay. A total of 23 fungal isolates were obtained from the roots, stems, and fruits of A. marina. The identification results showed that the root isolates had the highest genus diversity, followed by the stem and fruit isolates. The highest distribution of antioxidant and antibacterial activities was observed in the endophytic fungal extracts from fruits, followed by those from roots and stems. Notably, the majority of the 23 endophytic fungal extracts exhibited strong antioxidant and antibacterial activities. Isolates AMF3 and AMF6showed the most potent antioxidant activity, classified as very strong, with IC50 values below 20 ug/mL. Morphological identification revealed AMF3 as Neopestalotiopsis sp. and AMF6 as Aspergillus niger. This study highlights the potential of Neopestalotiopsis sp. and Aspergillus niger endophytic fungi from A. marina fruits as sources of natural antioxidant and antibacterial compounds, offering valuable insights for biotechnological applications of mangrove-associated endophytes.
Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions Noviyanto; Wahyudi, Mochamad; Sumanto, Sumanto
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3306

Abstract

This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.
Co-Authors Elfita Hary Widjajanti Mochamad Wahyudi Sumanto Sumanto