Chemical Composition and Aroma Profiling: Decision Tree Modeling of Formalin Tofu
Keywords:
Formalin Tofu, Crossvalidation, Classification, Data Science, Decision TreeAbstract
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.
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L. Saiman and R. Satra, “Analisis performa metode Support Vector Machine untuk klasifikasi dataset aroma tahu berformalin,” Indones. J. Data Sci., vol. 2, no. 2, pp. 50–61, 2021, doi: 10.56705/ijodas.v2i2.28.
S. F. Sammulia, T. Poluan, and Y. F. Yusri, “Qualitative analysis of formalin content in tofu at Jodoh Market Batam City,” J. Endur. Kaji. Ilm. Probl. Kesehat. 2020.
R. Prayogo, I. Anshory, and I. Sulistiyowati, “Formalin detection device in Tofu food with telegram monitoring,” J. Comput. …, 2023, [Online]. Available: https://jurnal.itscience.org/index.php/CNAPC/article/view/2393.
Y. D. Rahmawati, “A Qualitative Analysis Of Formalin on Tofu Circulated In Kupu Village Market, Brebes City,” KINETIKA, 2022, [Online]. Available: https://jurnal.polsri.ac.id/index.php/kimia/article/view/5072.
D. C. Feng, “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach,” Constr. Build. Mater., vol. 230, 2020, doi: 10.1016/j.conbuildmat.2019.117000.
M. Hall, “A Decision Tree-Based Attribute Weighting Filter for Naive Bayes,” in Research and Development in Intelligent Systems XXIII, London: Springer London, 2007, pp. 59–70.
M. M. Ghiasi, “Decision tree-based diagnosis of coronary artery disease: CART model,” Comput. Methods Programs Biomed., vol. 192, 2020, doi: 10.1016/j.cmpb.2020.105400.
N. A’ayunnisa, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” … J. Data Sci., 2022, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/54.
X. Han, “Pre-trained models: Past, present and future,” AI Open, vol. 2, pp. 225–250, 2021, doi: 10.1016/j.aiopen.2021.08.002.
Y. Cui, “Pre-Training with Whole Word Masking for Chinese BERT,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 29, pp. 3504–3514, 2021, doi: 10.1109/TASLP.2021.3124365.
H. Chen, “Pre-trained image processing transformer,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 12294–12305, 2021, doi: 10.1109/CVPR46437.2021.01212.
W. Ben Chaabene, “Machine learning prediction of mechanical properties of concrete: Critical review,” Constr. Build. Mater., vol. 260, 2020, doi: 10.1016/j.conbuildmat.2020.119889.
J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, pp. 107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.
M. Nabipour, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; A Comparative Analysis,” IEEE Access, vol. 8, pp. 150199–150212, 2020, doi: 10.1109/ACCESS.2020.3015966.
J. S. Dramsch, “70 years of machine learning in geoscience in review,” Advances in Geophysics, vol. 61. pp. 1–55, 2020, doi: 10.1016/bs.agph.2020.08.002.
Z. H. Zhou, Machine Learning. 2021.
D. Widyawati, A. Faradibah, and ..., “Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine,” Indones. J. …, 2023, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/76.
S. Hidayat, H. M. T. Ramadhan, and ..., “Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification,” Indones. J. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/70.
H. Song, “Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms,” Constr. Build. Mater., vol. 308, 2021, doi: 10.1016/j.conbuildmat.2021.125021.
P. Y. Tseng, “Prediction of the development of acute kidney injury following cardiac surgery by machine learning,” Crit. Care, vol. 24, no. 1, 2020, doi: 10.1186/s13054-020-03179-9.
H. Azis, F. T. Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, 2020, [Online]. Available: file:///Users/kbh/Library/Application Support/Mendeley Desktop/Downloaded/Azis, Admojo, Susanti - 2020 - Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah.pdf.
H. Azis, F. Fattah, and P. Putri, “Performa Klasifikasi K-NN dan Cross-validation pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, [Online]. Available: file:///Users/kbh/Downloads/507-2012-5-PB.pdf.
J. Cai, “Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest,” Appl. Energy, vol. 262, 2020, doi: 10.1016/j.apenergy.2020.114566.
M. Poongodi, “Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system,” Comput. Electr. Eng., vol. 81, 2020, doi: 10.1016/j.compeleceng.2019.106527.
W. Zhang, C. Wu, H. Zhong, Y. Li, and L. Wang, “Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization,” Geosci. Front., vol. 12, no. 1, pp. 469–477, Jan. 2021, doi: 10.1016/j.gsf.2020.03.007.
N. Rismayanti, A. Naswin, U. Zaky, M. Zakariyah, and D. A. Purnamasari, “Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, 2023.
E. Najwaini, T. E. Tarigan, and F. P. Putra, “Penerapan Algoritma K-Nearest Neighbors ( KNN ) pada Dataset Brain Tumor,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 1, pp. 14–19, 2023.
U. T. Yogyakarta, “Analisis Performa Algoritma Klasifikasi Decision Tree pada Dataset Pneumonia,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 1, pp. 1–10, 2023.
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