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All Journal POSITRON Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Vebriyanti, Lo Mei Ly
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Computer Science & IT Energy Mathematics Physics

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Sunlight Assisted Degradation of Linear Alkylbenzene Sulfonate by Floating Catalyst TiO2-Coconut Fiber Sugandi, Didiek; Agustiawan, Deri; Wijayanto, Ericco; Vebriyanti, Lo Mei Ly; Panaya, Gabriela Yenti Landang; Wahyuni, Nelly
POSITRON Vol 13, No 1 (2023): Vol. 13 No. 1 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v13i1.58251

Abstract

The increasing number of laundry businesses in Pontianak causes increased laundry waste, which is dangerous for health and the environment because anionic surfactants such as Linear Alkylbenzene Sulfonate (LAS) are hard degradable. Photocatalyst is a method that can be used to degrade the LAS structure. TiO2 carried in coconut fiber can optimize sunlight irradiation in degrading LAS content when light reaches the water's surface. This study aims to determine the characteristics and optimum activity time of photocatalyst TiO2-coconut fiber in degrading LAS. Photocatalyst characterization was carried out using XRD, XRF, and DR-UV, while the optimum activity test of photocatalysts in degrading LAS was carried out using a UV-Vis spectrophotometer. XRD diffractogram analysis showed the peaks of coconut fiber at 2θ = 22.2º, 34.8º and TiO2 at 2θ = 25.3º, 37.8º, 48.1º, 55.1º, and 62.1º. The TiO2 attached to the fiber after being synthesized was 21.12%. The band gap of TiO2 and TiO2-coconut fiber is 3.21 and 3.18 eV, with light absorption at 386.5 and 390.3 nm. Photocatalyst was carried out in LAS with a mass ratio of TiO2 and coconut fiber of 20:80; 30:70; 40:60, and 50:50 w/w with a time range of 0, 30, 60, 90, and 120 minutes. The results of photocatalysis of TiO2-coconut fiber in a ratio of 20:80 w/w showed the optimum photocatalytic activity at 120 minutes with the highest degradation rate of 80.43%. This research is expected to be applied as an alternative to handling LAS in laundry industry waste.
Analisis Kelayakan Kredit Menggunakan Classification Tree dengan Teknik Random Oversampling Vebriyanti, Lo Mei Ly; Martha, Shantika; Andani, Wirda; Rizki, Setyo Wira
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.24182

Abstract

Credit is providing money or bills based on the agreement between a bank and another party. Lending is inseparable from bad credit risk, so credit analysis must be conducted on prospective debtors before approving a proposed loan. This research aims to analyze creditworthiness using a Classification Tree as a classification method with Random Oversampling to overcome imbalanced data. This study uses secondary data on the status of debtors from a bank in West Kalimantan. Research data amounted to 800 data samples consisting of collectability variables as target variables and 10 independent variables, namely limit, rate, tenor, total installments, age, salary, premium and admin, agency, type credit, and type need. The Classification Tree method with Random Oversampling is used to overcome imbalanced data. Classification begins with data preprocessing, then the data is divided into training and test data with proportions of 70:30, 80:20 and 90:10 for each treatment without Random Oversampling and with Random Oversampling. Next, a classification model is formed using training data, and the classification model is validated using test data. After that, an overall evaluation of the model is carried out to determine the best model used in the classification process. Based on the research results, the best model is the model Classification Tree with Random Oversampling in proportion 70:30, with an accuracy value of 89.17%, specificity of 75.00%, and recall of 89.66%. The model can be used to classify current and non-current debtor data. The most influential variable in classifying debtor status is the total installment variable.
Co-Authors Agustiawan, Deri Andani, Wirda Martha, Shantika Nelly Wahyuni Panaya, Gabriela Yenti Landang Rizki, Setyo Wira Sugandi, Didiek Wijayanto, Ericco