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All Journal International Journal of Electrical and Computer Engineering International Journal of New Media Technology Jurnal Sinergitas PkM & CSR Jurnal ULTIMATICS JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Overbeek, Marlinda Vasty
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences

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Journal : Jurnal ULTIMATICS

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U-TAPIS Sal-Tik : Typing Error Detection Using Random Forest Algorithm Overbeek, Marlinda Vasty; Glennardy, Bryan; Mediyawati, Niknik; Nusantara, Samiaji Bintang; Sutomo, Rudi
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3563

Abstract

The development of technology in the field of journalism has grown very rapidly. However, in the field of journalism there are still frequent deviations from the language on online news portals. This can be seen from the aspect of spelling and word usage. Spelling mistakes that occur in the news can cause the information contained in the news to be unclear and ambiguous. Based on these problems, a study was conducted to create a model to detect type error in Indonesian. This model is created using the random forest algorithm. random forest is an algorithm that works by building several decision trees and then combining the decisions from each tree that has been built and taking the most votes from the predictions of each tree so that it will produce stable and accurate predictions. The results of the accuracy of the model in the research that has been done is 100%. However, it should be noted that this 100% result is that the model is able to detect words that are already contained in the dataset. Based on the evaluation results that have been carried out, because the detected word is contained in the dataset, the accuracy issued is 100%. The built model successfully detects type error in Tribunnews news articles.
Using Convolutional Neural Network and Saliency Maps for Cirebon Batik Recognition Aditiya, Yoga; Overbeek, Marlinda Vasty; Pomalingo, Suwito
ULTIMATICS Vol 17 No 1 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i1.4026

Abstract

Cirebon Batik is one of Indonesia's cultural heritages that has its own unique patterns and motifs, reflecting the cultural richness and history of its region of origin. This study aims to address the challenges in classifying the complex motifs of Cirebon Batik by implementing Convolutional Neural Network (CNN) and Saliency Map methods. The three main motifs used are Mega Mendung, Singa Barong, and Keratonan. The dataset was obtained from various online sources and processed using image augmentation techniques. CNN is used to recognize complex visual patterns, while Saliency Map highlights important areas in the image that influence the model's decision. The results show that the developed CNN model achieved an accuracy of 82%, precision of 83%, recall of 82%, and F1-score of 82%. The use of Saliency Map provides better interpretability and enhances the understanding of the classification process
U-TAPIS Sal-Tik : Typing Error Detection Using Random Forest Algorithm Overbeek, Marlinda Vasty; Glennardy, Bryan; Mediyawati, Niknik; Nusantara, Samiaji Bintang; Sutomo, Rudi
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3563

Abstract

The development of technology in the field of journalism has grown very rapidly. However, in the field of journalism there are still frequent deviations from the language on online news portals. This can be seen from the aspect of spelling and word usage. Spelling mistakes that occur in the news can cause the information contained in the news to be unclear and ambiguous. Based on these problems, a study was conducted to create a model to detect type error in Indonesian. This model is created using the random forest algorithm. random forest is an algorithm that works by building several decision trees and then combining the decisions from each tree that has been built and taking the most votes from the predictions of each tree so that it will produce stable and accurate predictions. The results of the accuracy of the model in the research that has been done is 100%. However, it should be noted that this 100% result is that the model is able to detect words that are already contained in the dataset. Based on the evaluation results that have been carried out, because the detected word is contained in the dataset, the accuracy issued is 100%. The built model successfully detects type error in Tribunnews news articles.
Co-Authors Abigail, Tesalonika Aditiya, Yoga Erasmus Ndolu, Fajar Henri Feliana, Ferlinda Glennardy, Bryan Harwahyu, Ruki Hin, Alexander Moya Khaeruzzaman, Yaman kusnadi, adhi Mediyawati, Niknik Nusantara, Samiaji Bintang Pomalingo, Suwito Prasetya, Syarief Gerald Prawira, Oqke Remerta Noni Naatonis Surbakti, Eunike Endariahna Sutomo, Rudi Tobing, Fenina Adline Twince