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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Marcel Alezandro Sihombing
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.
Implementasi ANN untuk Prediksi Angkutan Barang Kereta Api Berbasis Data Historis Irene Sinaga; Marcel Alezandro Sihombing; Serenita Silalahi; 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.v10i2.7112

Abstract

Railway transportation plays an important role in supporting goods distribution in Indonesia, particularly on the islands of Java and Sumatra. Changes in freight transportation volume over time form historical patterns that can be utilized for forecasting purposes. This study aims to implement an Artificial Neural Network (ANN) to predict railway freight transportation volume using historical data published by Statistics Indonesia (BPS) for the 2019–2026 period. The data were processed through preprocessing stages, including data cleaning, sliding window construction, Min-Max normalization, and splitting into training and testing datasets. The ANN model was developed using the Backpropagation algorithm and implemented in Google Colab utilizing TensorFlow and Keras libraries. The implementation results indicate that the model successfully learned historical data patterns, as demonstrated by the decreasing training loss during training and prediction results that closely followed the actual data trend. The findings indicate that ANN can be effectively applied as a time series forecasting approach for predicting railway freight transportation volume.
Co-Authors Alex Septama Sihite Irene Lestaria Sinaga Irene Sinaga Judea Tirta Jordan Simamora Octav Kornelius Hutagaol Serenita Silalahi Stefani Silalahi