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Indah Suryani

Universitas Nusa Mandiri

Author-ID : 3366202

Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

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Implementation of Machine Learning Algorithms for Early Detection of Cervical Cancer Based on Behavioral Determinants Duwi Cahya Putri Buani; Indah Suryani
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.453

Cervical cancer is a disease that affects women and has the highest mortality rate after breast cancer. Early detection of cervical cancer is critical at this time, so cervical cancer patients are decreasing. Many women, especially in Indonesia, are less concerned about the dangers of cervical cancer, even though if detected earlier, this disease will be easier to treat. One alternative for early detection can use machine learning algorithms. The machine learning algorithms used in this study are Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), SVM, and Random Forest. In this study, a random under-sampling method was employed, which had no uses in any prior research. This technique makes the accuracy of the five algorithms even better. The research results show that NB has an accuracy rate of 91.67%, LR has an accuracy rate of 87.5%, DT has an accuracy rate of 81.81%, SVM has an accuracy rate of 75%, and RF has the highest accuracy rate of 94.45%. This research shows that the best model is RF or Random Forest

PREDICTING THE BITCOIN PRICE USING LINEAR REGRESSION OPTIMIZED WITH EXPONENTIAL SMOOTHING Indah Suryani; Hani Harafani
Jurnal Riset Informatika Vol. 3 No. 3 (2021): June 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i3.88

Bitcoin is one of the most popular cryptocurrencies today. In the current pandemic conditions that hit the world due to Covid-19, bitcoin is expected to be used as an investment when the level of economic uncertainty is high. In this study, the data used is bitcoin price data which is included in time series data. One of the commonly used methods for prediction in time series is the linear regression method. To be able to develop the prediction results, a data transformation technique is used using the popular method, namely exponential smoothing. In the exponential smoothing method, optimization of the alpha parameter is carried out to be able to boost the prediction results from linear regression. And from the experimental results, it is evident that the optimization of the alpha parameter in exponential smoothing can improve the prediction performance of linear regression with the results of the comparison of RMSE with the t-test which has resulted in significant differences.

Implementation of Machine Learning Algorithms for Early Detection of Cervical Cancer Based on Behavioral Determinants Duwi Cahya Putri Buani; Indah Suryani
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Cervical cancer is a disease that affects women and has the highest mortality rate after breast cancer. Early detection of cervical cancer is critical at this time, so cervical cancer patients are decreasing. Many women, especially in Indonesia, are less concerned about the dangers of cervical cancer, even though if detected earlier, this disease will be easier to treat. One alternative for early detection can use machine learning algorithms. The machine learning algorithms used in this study are Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), SVM, and Random Forest. In this study, a random under-sampling method was employed, which had no uses in any prior research. This technique makes the accuracy of the five algorithms even better. The research results show that NB has an accuracy rate of 91.67%, LR has an accuracy rate of 87.5%, DT has an accuracy rate of 81.81%, SVM has an accuracy rate of 75%, and RF has the highest accuracy rate of 94.45%. This research shows that the best model is RF or Random Forest.

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Journal : Jurnal Riset Informatika