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All Journal Jurnal Minfo Polgan (JMP) Buletin Sistem Informasi dan Teknologi Islam Journal of Embedded Systems, Security and Intelligent Systems Linier: Literatur Informatika dan Komputer
Sitti Rahmah Jabir
Universitas Muslim Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Mathematics Social Sciences

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

 1 
Chemical Composition and Aroma Profiling: Decision Tree Modeling of Formalin Tofu Huzain Azis; Sitti Rahmah Jabir
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.1162

Abstract

This study focuses on the analysis of the aroma quality of tofu preserved with formalin, with the goal of developing a predictive model based on its chemical composition. Utilizing a dataset that includes various chemical components such as Hydrogen, LPG, CO, Alcohol, Propane, Methane, Smoke, and temperature, this research applies a Decision Tree model. The model is validated using 5-fold cross-validation, resulting in an accuracy of 36.79%, precision of 50.82%, recall of 36.79%, and an F1-Score of 27.58%. These results indicate the model's limitations in consistent prediction, suggesting potential improvements through other methods or the addition of variables. This study provides new insights into the relationship between chemical composition and aroma quality of formalin tofu, and opens new avenues for further research in this field.
Enhancing The Quality of College Decisions Through Decision Tree and Random Forest Models Sitti Rahmah Jabir; Huzain Azis; St. Hajrah Mansyur
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.1225

Abstract

One of the key indicators of the growth of a college is the number of students that are enrolled in the institution on an annual basis. Student enrollment is a crucial element in the growth of a college, particularly in the case of private institutions. When examining students' aspirations for higher education, several studies use data mining techniques to forecast the interests of students who will pursue college. Researchers adopt various ways to extract valuable information from data. Prior research has shown that the decision tree technique outperforms alternative methods. The random forest, in addition to the decision tree, is often used for predicting data mining tasks. Given the above background information, the author will conduct a study titled "Comparative Analysis of Decision Tree and Random Forest Algorithms in Predicting College Interests." According to the study findings, the decision tree outperforms the random forest in terms of outcomes. The accuracy of the decision tree model is 0.81, whereas the accuracy of the Random Forest model is 0.74. All in all, the Decision Tree approach will be used as the ultimate outcome for the implementation of Business Analytics.
Co-Authors A. Ulfah Tenripada Dewi Widyawati Faradibah, Amaliah Herman Herman M. Taufiq Sugianto Muhammad Arfah Asis St. Hajrah Mansyur Sugiarti, Sugiarti