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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Sisfo: Jurnal Ilmiah Sistem Informasi
Muhammad Fikry
UIN Sultan Syarif Kasim Riau
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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Analisis sentimen komentar youtube terhadap Anies Baswedan sebagai bakal calon presiden 2024 menggunakan metode naive bayes classifier Chely Aulia Misrun; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4790

Abstract

One of the figures as a presidential candidate is Anies Baswedan, the former governor of DKI Jakarta who received many awards and has an effective work program policy for problems in the DKI Jakarta area. Many comments about Anies Baswedan as a 2024 presidential candidate are found on YouTube social media. Youtube facilitates users to provide comments in response to videos which can be used as sentiment analysis information to find out positive comments and negative comments. The algorithm used in this research is the naïve bayes classifier. There are five main processes in this research, namely data collection, text preprocessing, word weighting (TF-IDF), classification (Naïve Bayes Classifier) and testing. From 1009 comment data on Indonesian-language youtube related to the Anies Baswedan video as a 2024 presidential candidate. Based on the analysis results, there are 610 positive comments and 399 negative comments. The accuracy result using the naïve bayes classifier algorithm is 78% which is obtained by using a comparison of 90% training data and 10% test data.
Application of ADASYN and Bayesian Optimization to Random Forests for Cervical Cancer Classification Restu Kharrisa Andini; Iis Afrianty; Muhammad Fikry; Fadhilah Syafria
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26973

Abstract

Accurate early detection is crucial for reducing mortality rates from cervical cancer. However, the application of machine learning to medical data is often hindered by class imbalance, causing prediction results to be biased toward the majority class. On the other hand, the process of parameter search using conventional methods such as GridSearchCV requires significant computational time. Therefore, this study proposes the application of the ADASYN (Adaptive Synthetic Sampling) method and Bayesian optimization to the Random Forest algorithm. In its implementation, ADASYN is used to adaptively synthesize minority data samples to rebalance their distribution. Meanwhile, Bayesian optimization serves to determine the optimal hyperparameter combination through a faster probabilistic approach. Model evaluation was conducted across four testing scenarios with training-to-test data splits of 90:10, 80:20, and 70:30. Findings from this study indicate that the standard Random Forest algorithm still produces biased predictions. However, classification performance improved significantly after the model was combined with ADASYN and Bayesian Optimization. The optimal results were achieved at a 70:30 ratio, recording accuracy of 98.06%, precision of 97.03%, recall of 99.13%, and an F1-score of 98.07%, with a computation time of 32.66 seconds. Overall, the proposed model successfully addresses data imbalance while reducing optimization time, enabling it to predict biopsy diagnoses with high precision.
Co-Authors Chely Aulia Misrun Elin Haerani Elvia Budianita Fadhilah Syafria Iis Afrianty Restu Kharrisa Andini