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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Emerging Statistics and Data Science Journal
Fazira, Nabila Dwi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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TRANSFORMER-BASED OPTICAL CHARACTER RECOGNITION APPROACH FOR IDENTIFYING MOTOR VEHICLES WITH OVERDUE TAXES Fazira, Nabila Dwi; Fauzan, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1597-1608

Abstract

The high growth in the number of motorized vehicles in Indonesia has given rise to special attention in managing traffic administration, especially in relation to vehicle taxes. To present innovative solutions in vehicle tax administration, this research was conducted to detect the five-year tax status of motor vehicles in Indonesia using the Transformer Optical Character Recognition (TrOCR) model. The aim of this research is to evaluate the performance of the TrOCR model in recognizing text on motor vehicle number plates in Indonesia and classifying number plates that have and have not paid tax. The data used is primary data in the form of images of motor vehicle number plates taken around the Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia, using a purposive sampling with constraints on the representation of each class. Although the data collection was limited to this location, Indonesian vehicle plates follow a standardized format, with regional differences primarily in the prefix letters. Additionally, the university attracts students from various regions who often use vehicles registered in their home provinces. Consequently, the collected dataset reflects a diverse range of number plates, making it a reasonable representation of motor vehicle plates across Indonesia. The research results show that the TrOCR model succeeded in achieving a Character Error Rate (CER) value of 2.9% with a data configuration of 90% for training and 10% for testing, and using 8 epochs. Evaluation of model performance indicates that overall text detection is very effective in classifying the five-year tax status of motor vehicles. Although there are some prediction errors, the overall performance of the model can be considered good and is able to provide reliable information regarding the five-yearly vehicle tax status
Peramalan Jumlah Pekerja Migran Indonesia Menggunakan Metode Triple Exponential Smoothing with Damped Parameter : Peramalan Jumlah Pekerja Migran Indonesia Menggunakan Metode Triple Exponential Smoothing with Damped Parameter Fazira, Nabila Dwi; Kesumawati, Ayundyah
Emerging Statistics and Data Science Journal Vol. 2 No. 2 (2024): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol2.iss.2.art15

Abstract

Pekerja migran Indonesia adalah setiap warga negara Indonesia yang sedang, akan, atau telah bekerja di luar wilayah Indonesia dengan menerima gaji. Indonesia merupakan salah satu negara pengirim pekerja migran terbesar, dimana sebagian besar dari mereka bekerja pada sektor dengan gaji rendah. Namun, dalam beberapa tahun terakhir, terjadi penurunan signifikan dalam jumlah pengiriman pekerja migran Indonesia. Penurunan ini disebabkan oleh pandemi Covid-19 yang menyebabkan peningkatan angka kasus, dan akibatnya pemerintah memberlakukan moratorium pada pengiriman pekerja migran pada tahun 2020. Hal ini mengakibatkan penurunan jumlah pekerja migran Indonesia sebesar 59% dan penurunan remitansi sebesar 17.5% dibandingkan dengan tahun 2019. Penelitian ini bertujuan untuk menilai jumlah pekerja migran Indonesia dari tahun 2023 hingga 2024 sebagai dasar untuk menyusun rencana strategis. Data yang digunakan adalah data sekunder jumlah pekerja migran Indonesia dari tahun 2018 hingga 2022. Metode penelitian yang digunakan adalah Triple Exponential Smoothing with Damped Parameter, yaitu metode untuk meramalkan periode ke depan. Hasil penelitian menunjukkan bahwa model tipe multiplicative adalah yang terbaik. Hasil prediksi menunjukkan jumlah pekerja migran Indonesia pada bulan Januari 2023 sebanyak 25,923, Februari 2023 sebanyak 24,192, Maret 2023 sebanyak 26,427, dan seterusnya. Berdasarkan hasil ini, prediksi menunjukkan bahwa jumlah pekerja migran Indonesia cenderung turun pada tahun 2023 hingga 2024. Model ini baik untuk peramalan karena memiliki nilai MAPE sebesar 20.84% dengan akurasi sebesar 79.16%.
Co-Authors Achmad Fauzan, Achmad Kesumawati, Ayundyah