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All Journal Foristek JUSIFO : Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING CCIT (Creative Communication and Innovative Technology) Journal Jurnal Teknik Informatika (JUTIF) Jurnal Abmas Negeri (JAGRI) The Indonesian Journal of Computer Science
Rinianty, Rinianty
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PUBLIC SENTIMENT ANALYSIS OF 'DIRTY VOTE' DOCUMENTARY FILM ON TWITTER USING NAÏVE BAYES WITH GRID SEARCH OPTIMIZATION Bagaskara, Febrian Chrissma; Syahrullah, Syahrullah; Hendra, Andi; Lamasitudju, Chairunnisa; Rinianty, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2682

Abstract

The film "Dirty Vote" provides a realistic depiction of alleged fraud issues within Indonesia's democratic system, released ahead of the 2024 elections. This has sparked various public opinions, both in favor of and against the film, potentially affecting the stability of Indonesia’s democratic system. The aim of this research is to analyze the public's reaction to the "Dirty Vote" documentary, which could serve as a consideration for assessing public awareness in rationally responding to a film and improving the quality of democracy in Indonesia. This research will test the accuracy of data used in classification using the Naive Bayes Classifier based on collected Twitter data. The evaluation results of the Naive Bayes model for sentiment classification showed an accuracy of 86%, with a precision of 84% and a recall of 91%. When compared to the implementation of hyperparameter tuning using grid search with a stratified k-fold combination and parameter configurations for alpha: [0,1], binarize: [0.0], and fit prior: [true, false], better results were obtained with an accuracy of 90%, a precision of 87%, and a recall of 94%. This demonstrates that using parameter optimization methods from grid search can help improve the accuracy of a classification model. It is hoped that this research will contribute significantly to the development of Indonesia’s democratic system, particularly in raising public awareness to think more rationally and critically when evaluating and analyzing a film.
APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Afiah, Nurul Anggun; Syahrullah, Syahrullah; Ardiansyah, Rizka; Laila, Rahmah; Pohontu, Rinianty
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3874

Abstract

Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.
Co-Authors Afiah, Nurul Anggun Amriana Amriana Andi Hendra Ayu Hernita Bagaskara, Febrian Chrissma Chairunnisa Ar. Lamasitudju Deni Luvi Jayanto Deny Wiria Nugraha Dwi Shinta Angreni Fajar, Moh Fitri, Siti Fitriana Kartika, Rina Laila, Rahma Lamasitudju, Chairunnisa Mariani Mariani Nisa, Chairunisa Nouval Trezandy Lapatta Nurhikmah Supardi Rahmah Laila Rasmita, Hajra Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar, Ryfial Septiano Anggun Pratama Siti Fitriana Sri Khaerawati Nur Sukardi Sukardi Syahrullah Syahrullah Wirdayanti Wongkar, Noel Marcell Jonathan Yanti, Wirda Yusuf Anshori