Vehicle Adoption Trend Analysis Based on Engine Displacement Segmentation Using Data Mining on Samsat Registration Data
DOI:
https://doi.org/10.59211/mjpjetl.v4i1.305Keywords:
Vehicle Registration; Mann-Kendall; STL Decomposition; Trend Analysis; Data MiningAbstract
Indonesia's South Sumatra province generates substantial four-wheeled vehicle registration activity, yet the administrative records that document this activity have rarely been subjected to systematic analytical scrutiny. This study draws on Samsat vehicle registration data from 2025 and the first quarter of 2026 to examine how new vehicle adoption patterns differ across engine displacement (CC) segments. The dataset spanned 15 Excel files: Dataset A covered the full year 2025 (17,867 units), while Dataset B captured Q1 2026 (6,190 units). Because column naming conventions varied across files, a keyword-matching-based preprocessing pipeline was developed to handle integration. The analysis combined the Mann-Kendall Trend Test, Sen's Slope Estimator, STL Decomposition, Kruskal-Wallis Test, and Mean Absolute Percentage Error (MAPE) as an out-of-sample projection benchmark.
The Mann-Kendall test returned no statistically significant monotonic trend (p = 0.7105), which indicates that the proportional split between engine displacement categories stayed broadly stable over the observation window. Low-displacement vehicles (≤1,500 cc) held a consistent majority, averaging 66.10% of monthly registrations, throughout the period. STL Decomposition revealed a seasonal component responsible for roughly 50.02% of total data variance; the low-displacement segment peaked in November, while the high-displacement segment peaked in March. Projection accuracy, measured by MAPE, came in at 14.46% for the low-displacement segment (Accurate) and 37.92% for the high-displacement segment (Moderately Accurate). Based on these results, Samsat's administrative data proves to be highly useful for practical applications. Regional governments can utilize this data to improve public service planning and estimate regional own-source revenue (PAD) more accurately
Downloads
References
GAIKINDO, “Indonesian Automobile Industry Data.” Accessed: May 11, 2026. [Online]. Available: https://www.gaikindo.or.id/indonesian-automobile-industry-data/
[2] Directorate General of Fiscal Balance, “Guidelines for the Collection of Motor Vehicle Tax and Motor Vehicle Transfer Tax Institution: Ministry of Finance of the Republic of Indonesia.” Accessed: May 11, 2026. [Online]. Available: https://djpk.kemenkeu.go.id/?
[3] A. Meydian dan T. M. Z. S. Machmud, “Kebijakan Low Cost Green Car dalam Industri Otomotif Nasional,” J. Ekon. Indones., vol. 10, no. 3, pp. 257–279, 2022, doi: 10.52813/jei.v10i3.149.
[4] F. Teknik and U. A. Jambi, “Imti Tsalil Amri, Santoso,” Penerapan Data Min. Untuk Menganalisis Tingkat Kelarisan Penjualan Aksesori Mobil. Menggunakan Algoritma. K-Means, vol. 1, no. 2, pp. 25–30, 2018.
[5] F. G. Walelang, C. E. J. C. Montolalu, and D. Hatidja, “Penerapan Metode Autoregressive Integrated Moving Average ( ARIMA ) dalam Model Intervensi Fungsi Step untuk Memprediksi Penjualan Mobil di PT . Hasjrat Abadi Cabang Tendean Manado,” Indones. J. Intell. Data Sci., vol. 01, no. 01, pp. 35–42, 2023.
[7] W. Afifi, D. R. Nastiti, and Q. Aini, “Clustering K-Means pada Data Ekspor (Studi Kasus: PT Gaikindo),” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 1, pp. 45–50, 2020, doi: 10.24176/simet.v11i1.3568.
[8] H. B. Mann, “Nonparametric Tests Against Trend,” Econometrica, vol. 13, no. 3, pp. 245–259, Jul. 1945, doi: https://www.jstor.org/stable/1907187.
[9] Robert B Cleveland, William S. Cleveland, Jean E. McRae, and Irma Terpenning, “STL: A Seasonal-Trend decomposition Procedure Based on Loess,” 1990.
[10] A. Hendrawan and N. P. Wijaya, “Data-Driven Traffic for Infrastructure Planning : An LSTM Approach Using Indonesian Road-Vehicle Trends,” vol. 8, no. 2, pp. 1416–1432, 2026, doi: 10.63158/journalisi.v8i2.1516.
[11] E. Wijaya and Y. Tri Widayati, “Optimalisasi Performa Penjualan Di Pt Mustika Jaya Lestari Menggunakan Algoritma K-Nn Dan Arima (2021-2022) Optimizing Sales Performance At Pt Mustika Jaya Lestari Using K-Nn and Arima Algorithms (2021-2022),” J. Inf. Technol. Comput. Sci., vol. 7, no. 5, pp. 1628–1639, 2024.
[12] P. Candra Susanto, D. Ulfah Arini, D. Marlita, L. Yuntina, and E. Saribanon, “Mixed Methods Research Design Concepts: Quantitative, Qualitative, Exploratory Sequential, Embedded and Parallel Convergent,” Int. J. Adv. Multidiscip., vol. 3, no. 3, pp. 471–485, 2024.
[13] A. R. Fadhilah, A. H. Nasution, and W. Monika, “Comparative Analysis of LSTM, FB Prophet, and Moving Average Methods for Fuel Sales Prediction: A Time Series Forecasting Approach,” Build. Informatics, Technol. Sci., vol. 6, no. 3, pp. 1321–1331, 2024, doi: 10.47065/bits.v6i3.5877.
[14] Y. Kurnia, R. Arijanto, W. Layanda, and D. Surya Dwi Putra, “Web-Based Car Sales Prediction System Using the ARIMA (Autoregressive Integrated Moving Average) Model for Optimizing Automotive Marketing Strategies,” Rubinstein Multidiscip. J., vol. 4, no. 1, 2025, doi: 10.31253/rubin.v4i1.4039.
[15] Y. Haruki, S. Ishikura, K. Demachi, and T. Hayashi, “Contextual Graph Embeddings: Accounting for Data Characteristics in Heterogeneous Data Integration,” Proc. IEEE Int. Conf. Big Data, BigData, no. 2025, pp. 2843–2852, 2025, doi: 10.1109/BigData66926.2025.11400991.
[16] F. Sidqi, I. D. Sumitra, J. Dipati, U. No, and J. Barat, “5537-Article Text-17390-1-10-20210907”.
[17] F. Aditya, E. Gusmayanti, and J. Sudrajat, “Rainfall trend analysis using Mann-Kendall and Sen’s slope estimator test in West Kalimantan,” IOP Conf. Ser. Earth Environ. Sci., vol. 893, no. 1, 2021, doi: 10.1088/1755-1315/893/1/012006.
[18] A. K. Gupta, “Estimation of the Mean and Standard Deviation of a Normal Population from a Censored Sample,” Biometrika, vol. 39, no. 3/4, pp. 260–273, May 1952, doi: 10.2307/2334023.
[19] Rosmelina Deliani Satrisna, Aniq A. Rohmawati, and Siti Sa’adah, “Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model,” Int. J. Inf. Commun. Technol., vol. 7, no. 1, pp. 21–30, 2021, doi: 10.21108/ijoict.v7i1.554.
[20] R. A. K. Sherwani, H. Shakeel, W. B. Awan, M. Faheem, and M. Aslam, “Analysis of COVID-19 data using neutrosophic Kruskal Wallis H test,” BMC Med. Res. Methodol., vol. 21, no. 1, pp. 1–7, 2021, doi: 10.1186/s12874-021-01410-x.
[21] S. A. Sinaga, “Implementasi Metode Arima (Autoregressive Integrated Moving Average) Untuk Prediksi Penjualan Mobil,” J. Glob. Technol. Comput., vol. 2, no. 3, pp. 102–109, 2023, doi: 10.47065/jogtc.v2i3.4013.
[22] H. Khofaria, I. Iskandar, and Suhadi, “Analisis Curah Hujan Ekstrem di Kepulauan Maluku dan Hubungannya dengan Fenomena ENSO Analysis of Extreme Rainfall in The Maluku Islands And its Relationship with The ENSO Phenomenon,” J. Ilmu Fis. dan Pembelajarannya, vol. 8, no. 2, pp. 80–105, 2024.
[23] Y. A. Saputra and D. Mahdiana, “Analisis Penerapan Machine Learning , Deep Learning , dan Data Mining dalam Prediksi Penjualan di Industri Otomotif,” Magister Ilmu Komputer , Fakultas Teknologi Informasi , Universitas Budi Luhur , Jakarta , Indonesia; Sistem Informasi , Fakultas Teknologi. Pendidik. dan Teknol. Indones., vol. 5, no. 6, pp. 1743–1755, 2025.
[24] I. G. T. Suryawan, I. K. N. Putra, P. M. Meliana, and I. G. I. Sudipa, “Performance Comparison of ARIMA, LSTM, and Prophet Methods in Sales Forecasting,” Sinkron, vol. 8, no. 4, pp. 2410–2421, 2024, doi: 10.33395/sinkron.v8i4.14057
Downloads
Published
Issue
Section
URN
License
Copyright (c) 2026 Muhammad Rafi Herdian, Fathoni Fathoni
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors grant the journal the right of first publication. which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited. Authors are responsible for ensuring that any third-party materials included in their work have proper permissions.
