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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Teknologi Informatika dan Komputer Jurnal Diferensial MATHunesa: Jurnal Ilmiah Matematika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komputer Indonesia INOVTEK Polbeng - Seri Informatika Prosiding Seminar Nasional Ilmu Teknik
Alfan Rizaldy Pratama
Universitas Pembangunan Nasional Veteran Jawa Timur
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Public Health Transportation

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Implementation of Temporal Fusion Transformer (TFT) for Short-Term Sales Prediction of Telkomsel Data Packages in East Java Muhammad Azkiya Akmal; Trimono; Alfan Rizaldy Pratama
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3268

Abstract

The development of the cellular telecommunications industry has driven an increasing demand for fast, stable, and affordable data services. Accurate forecasting of data package sales is a significant challenge for telecommunications operators due to high demand fluctuations and the complexity of time series patterns. This study aims to implement a Temporal Fusion Transformer (TFT) model based on Seasonal-Trend Decomposition using Loess (STL) to predict short-term sales of Telkomsel data packages in East Java. The data used are sales transactions with hourly time resolution from January to June 2024, focusing on the five data packages with the highest transaction volume. The STL method is applied in the pre-processing stage to separate the trend, seasonal, and residual components, which are then used as additional features in the TFT modeling. Model performance is evaluated using Mean Absolute Error (MAE) and Quantile Risk (q-Risk). The results show that the TFT model is able to produce accurate predictions with an MAE value of 3.6941 and an average q-Risk of 0.0808. Furthermore, interpretability analysis revealed that historical sales variables, seasonal components, and calendar variables significantly contributed to the prediction results. These findings indicate that the STL-based TFT approach is effective for short-term sales forecasting and has the potential to support data-driven operational decision-making in the telecommunications sector.
Co-Authors Abda Abda Adziima, Andri Fauzan Andri Faudzan Adziima Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Melinda Putri Dadet Pramadihanto Difta Alzena Sakhi Dwito Armono, R. Haryo Friza Nur Fatmala Gede Susrama Mas Diyasa, I idhom, Mohammad Jauharis Saputra, Wahyu Syaifullah Karina Auralia Kristanaya, Mirechelin Marta, Bayu Sandi Muhammad Azkiya Akmal Muhammad Nasrudin Nariswari, Naura Ulayya Putra Pratama, Firmansyah Ramadhan Anniswa, Iqbal Saputra, Wahyu S.J. Sari, Dewi Mutiara Shindi Shella May Wara Susrama Mas Diyasa, I Gede Syah, Maulidya Prastita Syaifullah J. S, Wahyu Trimono Wahyu Syaifullah J S Zufar Abdullah Rabbani