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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Irene Lestaria Sinaga
Universitas HKBP Nommensen Pematangsiantar
Computer Science & IT

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Tinjauan Arsitektur Transformer Dan Penerapannya Pada Pemrosesan Bahasa Alami Marcel Alezandro Sihombing; Irene Lestaria Sinaga; Octav Kornelius Hutagaol; Judea Tirta Jordan Simamora; Alex Septama Sihite
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7096

Abstract

The rapid development in the field of natural language processing (NLP) cannot be separated from the emergence of the Transformer architecture first introduced in 2017. This architecture relies on a self-attention mechanism that allows the model to process all tokens at once, unlike the sequential approach in RNN or LSTM. This article presents a literature review of the Transformer architecture, its main components such as multi-head attention, positional encoding, and feed-forward network, as well as its various applications in NLP tasks such as machine translation, sentiment analysis, and text generation. In addition, the advantages and limitations of the Transformer compared to previous architectures are discussed, along with challenges such as high computational requirements and large memory consumption. This review is expected to provide a comprehensive basic understanding of the Transformer for researchers and practitioners in the field of machine learning.
Prediksi Jumlah Perjalanan Wisatawan Nusantara Berdasarkan Data Historis Menggunakan Artificial Neural Network Serenita Gisela Silalahi; Irene Lestaria sinaga; Stefani Silalahi
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7109

Abstract

Domestic tourist travel is an important indicator of the development of Indonesia's domestic tourism sector. Predicting the number of domestic tourist trips is essential to support data-driven planning and decision-making. This study aims to develop a prediction model for domestic tourist travel based on historical data using an Artificial Neural Network (ANN). Secondary data obtained from Statistics Indonesia (BPS) were processed through preprocessing, Min-Max normalization, sliding window pattern formation, and ANN training using the Backpropagation algorithm. Model performance was evaluated using MAE, MSE, RMSE, MAPE, and R². The results obtained MAE of 0.0582, MSE of 0.0066, RMSE of 0.0813, MAPE of 9.44%, and R² of 0.3665, indicating that the proposed ANN model is capable of providing reasonably accurate predictions of domestic tourist travel based on historical data.
Penerapan Machine Learning dalam Klasifikasi Wajah dan Benda Menggunakan Teachable Machine Berbasis Lovable Stefani Silalahi; Serenita Gisela Silalahi; Irene Lestaria Sinaga
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7111

Abstract

The rapid advancement of Artificial Intelligence (AI), particularly in the fields of Machine Learning and Computer Vision, has accelerated the development of image classification systems capable of recognizing various objects automatically. One of the platforms that facilitates the development of machine learning models without requiring advanced programming skills is Google Teachable Machine, while Lovable is an AI-based application development platform that simplifies and accelerates web application development. This study aims to implement machine learning in developing a face and object classification system using Google Teachable Machine integrated into a Lovable-based application. The research methodology consists of dataset collection involving face and object images, data preprocessing, model training using Google Teachable Machine, model export in TensorFlow.js format, integration of the trained model into the Lovable application, and system testing using both image and video inputs in real time. The evaluation was conducted to assess the system's ability to classify trained face and object categories under various testing scenarios. The outcome of this research is a web-based application capable of classifying faces and objects through image and video inputs with fast and accurate performance. This study is expected to provide an alternative approach for developing machine learning-based applications that are easy to implement while supporting the broader application of Artificial Intelligence technologies in various fields.
Co-Authors Alex Septama Sihite Judea Tirta Jordan Simamora Marcel Alezandro Sihombing Octav Kornelius Hutagaol Serenita Gisela Silalahi Stefani Silalahi