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All Journal Metta : Jurnal Ilmu Multidisiplin Journal of Data Science Methods and Applications
Jonathan, Kenny
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Evaluasi Efektivitas Efektif Mikroorganisme (EM) Berbasis Buah-Buahan Dan Sayur-Sayuran Dalam Menurunkan Parameter Pencemar Limbah Cair Tahu Jonathan, Kenny; Goeltom, Mangihot Tua
Metta : Jurnal Ilmu Multidisiplin Vol. 5 No. 2 (2025)
Publisher : Jayapangus Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37329/metta.v5i2.4151

Abstract

Tofu wastewater consists of proteins, carbohydrates, fats, H2S, CO2 , CH4 and NH3 which endanger the life of aquatic biota. Tofu liquid waste has high BOD, COD, and ammonia and acidic pH exceeding standards. The high content of BOD and COD causes organisms to die due to lack of oxygen. Tofu waste processing can be done by adding microorganisms to degrade organic matter so that standards can be met. Microorganisms that are able to degrade tofu liquid waste belong to lactic acid bacteria and are found in fruits and vegetables. Microorganisms created inmixed cultures containing various kinds of microorganisms are called effective microorganisms (EM). The purpose of this experiment is to analyze the effectiveness of EM made from fruits and vegetables in processing tofu wastewater at various volumes through the parameters of BOD, COD, pH, ammonia, and pH. EM is made by mixing fruits and vegetables with sugar and coconut water which are fermented for 8 days. EM was mixed with waste at volume of 10 ml, 15 ml, 20 ml, 25 ml and incubated for 5 days. Parameters such as BOD, COD, ammonia, and pH were measured before and after incubation for 5 days. The number of coliform was also counted before and after incubation with TVC method. The findings demonstrate that adding EM at a volume of 25 ml produces the greatest outcomes since it lower the percentage of COD and ammonia reduction at the highest level while keeping the percentage of DO reduction at the lowest level.
Prediksi Pengunduran Diri Karyawan Menggunakan Metode Algoritma Random Forest Prasetyo, Bima Restu; Apiliani, Lusy Pebi; Intan, Citra Nur; Jonathan, Kenny
Journal of Data Science Methods and Applications Vol. 1 No. 2 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Employee attrition is a critical issue in human resource management as it directly affects a company’s productivity and operational efficiency. Therefore, a data-driven prediction system is needed to identify potential employee resignation risks at an early stage. This study aims to build an employee attrition classification model using the Random Forest algorithm, implemented in the RapidMiner software. The dataset used in this study is derived from the IBM HR Analytics Employee Attrition Dataset. The research process includes data cleaning, attribute transformation, model building, and performance evaluation using a confusion matrix and metrics such as accuracy, precision, and recall. The results show that the Random Forest model achieved an accuracy of 91.04%, a precision of 100% for the “Yes” class, and a recall of 44.37%. Furthermore, it was found that the variables JobLevel and TotalWorkingYears significantly influence attrition status. Therefore, this model can serve as a decision support tool in identifying employee attrition risks and designing more effective, data-driven retention strategies
Co-Authors Apiliani, Lusy Pebi Intan, Citra Nur Mangihot Tua Goeltom Prasetyo, Bima Restu