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All Journal Jurnal Informatika dan Teknik Elektro Terapan JAIS (Journal of Applied Intelligent System) Jurnal Informasi dan Komputer Jurnal Teknik Informatika (JUTIF) CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Tedc Jurnal Bangkit Indonesia Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Journal of Informatics and Electronics Engineering Journal of Economics, Accounting, Tax and Management
Ari Sudrajat
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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Indonesian Language Hoax News Classification Basedn on Naïve Bayes Ari Sudrajat; Ratna Rizky Wulandari; Elvathna Syafwan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5985

Abstract

Hoax news in Indonesia causes various problems, therefore it is necessary to classify whether a news is in the hoax category or is valid. Naive Bayes is an algorithm that can perform classification but has a weakness, namely the selection of attributes that can affect accuracy so that it needs to be optimized by giving weights to attributes using the TF-IDF method. Classification using Naive Bayes and using TF-IDF as attribute weighting on a dataset of 600 data resulted in 82% accuracy, 84% precision, and 89% recall. The suggestion put forward is that it is better to use a larger number of datasets in order to produce higher accuracy.
Naive Bayes Performance in Analysis of Public Opinion Sentiment Against COVID-19 Ayu Hendrati Rahayu; Ari Sudrajat
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7134

Abstract

The huge impact caused by the COVID-19 pandemic has made many people express their opinions on Twitter social media. There are various responses given by the community that are negative and positive. The dataset comes from kaggle with more than 750 tweets of data. Classification designed by the Naive Bayes method. Implementation through preprocessing, case folding, tokenizing, stopword removal, TF-IDF, and cross validation has been able to produce quite high accuracy. After classification, validation will be carried out with Cross Fold Validation. The best value is on cv5 where accuracy = 0.847, precision = 0.855, recall = 0.83, and f1 score = 0.842.
Watermarking using DCT and DWT on Pneumonia images Sudrajat, Ari; Rahayu, Ayu Hendrati
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.8914

Abstract

Watermarking is a branch of the data hiding technique. Watermarking is a technique used to insert a copyright label on an image, so that the copyright of the image can be protected. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are techniques that can be used to watermark. In this study, the Discrete Cosine Transform and Discrete Wavelet Transform methods will be used to watermark images to 5 different host images. In the tests carried out, watermarking techniques will be compared using DCT, DWT, DCT-DWT combination and DWT-DCT combination. The results obtained in this study were the highest PSNR value obtained at 41.931, the highest SSIM obtained 0.99515, the highest entropy was also obtained at 7.4186, The best UACI value is 0.0071158 and the best NCPR value is obtained at 93.9068% then, for the best CC value is obtained at 0.99953. As well as the NCC value, the value obtained is the same all in each test, namely with a value of 1.
Improving Heart Disease Severity Prediction Using SMOTE for Imbalanced Data Rahayu, Ayu Hendrati; Sudrajat, Ari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11180

Abstract

The heart disease is a prevalent and potentially fatal condition affecting individuals worldwide. In this study, we address the challenge of predicting the severity of heart disease using supervised learning techniques. Leveraging a dataset comprising various demographic and clinical attributes, we propose a solution that employs machine learning models to accurately predict the severity level of heart disease. Among the evaluated models, Random Forest emerges as the top performer, showcasing exceptional precision, recall, accuracy, and F1-score across all severity levels, with an overall accuracy of 98.8%. This highlights the robustness of the Random Forest model in accurately classifying instances across different severity levels. Following closely behind, the KNN algorithm demonstrates commendable performance, achieving an accuracy of 92% and showcasing competitive precision, recall, and F1-score values, particularly for higher severity levels. Despite its notable aspects, XGBoost ranks third among the evaluated models, with an accuracy of 90.4%. While XGBoost excels in certain aspects, such as recall for Level 3 severity, it falls short in overall performance compared to Random Forest and KNN. For future research, exploring ensemble methods that combine the strengths of different algorithms could yield even better classification results, providing avenues for further improvement in predicting the severity of heart disease
Co-Authors Adiwijaya, Fariz Hasabi Agus Setiawan Apnena, Riri Damayanti Ayu Hendrati Rahayu Burjulius, Renol Diky Fermana Elvathna Syafwan Gerinata Ginting Heri Hermawan Indra Budi Iqtait, Musab Islam, Hussain Md Mehedul Islam, Moch Ziad Al Lena, Sonty Novita Lestari Anggreini Nugraha, Bagus Rian Octanza, Tahta Rizaldi Putri, Maura Yuliana Rahayu, Ayu Hendrati Rahmawati, Hani Ratna Rizky Wulandari Ratna Rizky Wulandari Rismayana, Aris Haris Rohmah, Neng Ela Siti Rohmayani, Dini Sari Rahmawati, Sari Sifaul Anwar Siti Aminah Wardhani, Shaffira Kusuma