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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Teknologi dan Sistem Komputer SISFOTENIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Informasi dan Komputer JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi ABSYARA: Jurnal Pengabdian Pada Masyarakat Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi MDP Student Conference GEMBIRA (Pengabdian Kepada Masyarakat) AT-TAKLIM: Jurnal Pendidikan Multidisiplin
Tinaliah, Tinaliah
Universitas Multi Data Palembang
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ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP INVESTASI KEUANGAN DI INDONESIA MENGGUNAKAN METODE NAIVE BAYES Andreas Danny Agus Wahyudi; Tinaliah
AT-TAKLIM: Jurnal Pendidikan Multidisiplin Vol. 2 No. 9 (2025): At-Taklim: Jurnal Pendidikan Multidisiplin (Edisi September)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/at-taklim.v2i9.725

Abstract

Financial investment is important for Indonesian people to prepare for their future financially. In this digital era, social media such as Twitter have become popular platforms for sharing opinions and views on various topics including financial investments. By leveraging the data available on Twitter, sentiment analysis can be used to understand user views and opinions regarding financial investments in Indonesia. The Naive Bayes method can be used to perform sentiment analysis on Twitter data by utilizing probability theory to classify tweets with positive, negative or neutral views about financial investment in Indonesia. The amount of tweet data is unbalanced, so it is necessary to do SMOTE over-sampling so that the dataset is balanced and do the testing using k-fold validation so that you can see the confision matrix and get the values for accuracy, precision, recall, and f1-score. Based on the sentiments obtained from Twitter social media, it shows that Twitter social media users have positive sentiments towards financial investment in Indonesia with a total number of positive sentiments of 426 data from a total of 1000 tweet data. Unbalanced data affects the classification results, namely an accuracy of 45% with the SMOTE up-sampling method and an accuracy of 89% without using the SMOTE up-sampling method.
Klasifikasi Lesi Benign Dan Malignant Pada Rongga Mulut Menggunakan Arsitektur ResNet50 Tinaliah, Tinaliah; Elizabeth, Triana
JATISI Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.6947

Abstract

It is very important to protect the human oral cavity to avoid various oral problems, one of which is tumors and oral cancer. Cell growth in the oral cavity is divided into benign oral cavity tumors (benign), precancerous lesions, and oral cavity cancer (malignant). Image classification of benign and malignant lesions can help to determine whether cells in the oral cavity are benign or malignant. CNN is a type of neural network that can be used to extract features from an image. In this research, image classification of benign and malignant lesions will be carried out by applying the ResNet50 architecture to the CNN method. The dataset used is the Oral Image Dataset, which has two classes, namely the benign class and the malignant class. Testing is carried out using testing data from each class using the Adam and SGD optimizers. Based on the test results, it can be concluded that ResNet50 can classify images of benign and malignant lesions well using the Adam optimizer with an accuracy value of 94%.
Co-Authors Abdul Rahman Amri Amri Andreas Danny Agus Wahyudi Bautista, Christian Chandra Saputra Cyndi Monica Deky Berniawan Derry Alamsyah Dicky Pratama Dicky Pratama gasim, Gasim Hafiz Irsyad Inayatullah Inayatullah Inayatullah, Inayatullah Johannes Petrus Lucretia, Jolyn Molavi Arman Niko Pirnando, Hendra Nata Novan Wijaya Pratama, Samuel Effendi Putri, Agnes Anastasia Putri, Anisah Dhiyaa Azzahra Susanto, Rafael Ivan Triana Elizabeth, Triana Wahyu Mustika Anggara Wilcent Yoannita Yoannita, Yoannita