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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Rosni Lumbantoruan
Institut Teknologi Del
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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TopC-CAMF: Sistem Perekomendasi Matrix Factorization Berbasis Top Context Rosni Lumbantoruan; Paulus Simanjuntak; Inggrid Aritonang; Erika Simaremare
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 4: November 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v11i4.5399

Abstract

Online activities have been more and more vital as the digital business has expanded. Users can conduct most activities online such as online shops, hotel bookings, or online educations and courses. A large number of social users are drawn to the abundance of goods available on the Internet. The huge amount of information makes it impossible for social users to navigate it properly and efficiently. Many companies have offered a personalization to tackle this issue. It is proven that the personalized recommendation systems are able to suggest items to users based on their interests and needs that best suit them, which can be captured from user’s contextual information. However, most of the studies capture this contextual information from the predefined contexts such as location and time. In this study, the personalized user context from the user’s text review that they posted as they gave rating to an item was obtained. To this end, a new approach based on the matrix factorization recommendation model, TopC-CAMF, was proposed. TopC-CAMF investigates and finds the most important contexts or needs for each user by leveraging the deep learning model. First, all important contexts from user’s text reviews were extracted. The next step was representing user preferences with the variations of most important contexts, namely top 5, top 10, top 15, top 20, and top 25 contexts. Then, the best top context variation was evaluated and the optimal one was used as the input for the matrix factorization method in providing better recommendations. Extensive experiments using three real datasets were conducted to prove the effectiveness of the TopC-CAMF in terms of root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), normalized discounted cumulative gain (NDCG), and Recall.
Penilaian Kesamaan Entity Relationship Diagram dengan Algoritme Tree Edit Distance Humasak Simanjuntak; Rosni Lumbantoruan; Wiwin Banjarnahor; Erisha Sitorus; Magdalena Panjaitan; Sintong Panjaitan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 1: Februari 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1265.561 KB)

Abstract

Main competency in database learning is ability to design Entity Relationship Diagram (ERD). Generally, lecturer gives task to students to design an ERD with some requirements. These ERDs are then assessed by comparing them with the answers. In practice, the process takes long time and it is possible that the lecturer grades the students inconsistently. Furthermore, plagiarism could be occured without being noticed by the lecturer. This research aims to design and build an application that assess similarity of ERD. The application apply tree edit distance algorithm in checking ERD similarity. ERD is exported into XMI document and then processed using the tree edit distance algorithm. The results show that ERD similarity value depends on number of insert, delete, and rename operation in tree edit distance Algorithm rather than number of difference component.
Studi dan Analisis Hyperparameter Tuning IndoBERT Dalam Pendeteksian Berita Palsu Anugerah Simanjuntak; Rosni Lumbantoruan; Kartika Sianipar; Rut Gultom; Mario Simaremare; Samuel Situmeang; Erwin Panggabean
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i1.8532

Abstract

The rapid advancement of communication technology has transformed how information is shared, but it has also brought concerns about the proliferation of false information. A recent report by the Ministry of Communication and Informatics in Indonesia revealed that around 800,000 websites were involved in spreading false information, underscoring the seriousness of the problem. To combat this issue, researchers have focused on developing techniques to detect and combat fake news. This research centers on using IndoBERT-base-p1 for fake news detection and aims to enhance its performance through three methods to tune the hyperparameter value of the model namely: Bayesian optimization, grid search, and random search. After comparing the outcomes of the three hyperparameter tuning methods, Bayesian Optimization emerged as the most effective approach. Achieving a precision of 88.79%, recall of 94.5%, and F1-score of 91.56% for the “fake” label, Bayesian Optimization outperformed the other hyperparameter tuning methods as well as the model using the fine-tuning hyperparameter value. These findings emphasize the importance of hyperparameter tuning in improving the accuracy of fake news detection models. Utilizing Bayesian Optimization and optimizing the specified hyperparameters, the model demonstrated superior performance in accurately identifying instances of fake news, providing a valuable tool in the ongoing battle against disinformation in the digital realm.
Co-Authors Anugerah Simanjuntak Ayunisa Mindari Banjarnahor, Wiwin Barus, Arlinta Claudia, Marwani Elisa Margareth Sibarani Erika Simaremare Erisha Sitorus Erwin Panggabean Gonzales, Helen Grace B. Humasak T. A. Simanjuntak Inggrid Aritonang Kartika Sianipar Lubis, Anju Ucok Magdalena Panjaitan Manurung, Ruth Christine Margaretha, Patricia Dian Mario Simaremare Monica Verawati Sitorus Panjaitan, Magdalena Panjaitan, Sintong Paulus Simanjuntak Purba, Susi Rajagukguk, Nico Rut Gultom Samuel Situmeang Siahaan, Lamsihar Sintong Panjaitan Sitorus, Erisha Suhendrowan Putra Sinaga Wiwin Sry Adinda Banjarnahor