Implementation of Business Intelligence and Data Mining in Money Changer Transaction Analysis (Case Study of PT. Gemilang Artha Valindo)

I Komang Ram Pramartha Utama; Made Adi Paramartha Putra; I Nyoman Purnama

doi:10.59395/ijadis.v6i1.1355

Authors

I Komang Ram Pramartha Utama Primakara University
Made Adi Paramartha Putra Primakara University
I Nyoman Purnama Primakara University

DOI:

https://doi.org/10.59395/ijadis.v6i1.1355

Keywords:

Business Intelligence``, Data Mining, currency, Prediction, RMSE

Abstract

This study aimed to implement Business Intelligence (BI) and Data Mining for analyzing currency exchange transactions at PT. Gemilang Artha Valindo to support data-driven decision-making. Transaction data was analyzed using Power BI to generate visualizations, including a pie chart for transaction frequency by currency type, a bar chart for the number of buy and sell transactions per currency, and a line chart for monthly average exchange rate fluctuations. The pie chart indicated that the AUD currency dominated transactions, contributing 51.95% of the total. The bar chart revealed that AUD buy transactions accounted for 63.22% of total AUD transactions, while the line chart showed that GBP and EUR had the highest average exchange rates, reaching Rp20,835 and Rp17,700, respectively. The exchange rate prediction process utilized three algorithms: Linear Regression, K-Nearest Neighbors (KNN), and Random Forest. Their performances were evaluated using Root Mean Squared Error (RMSE). The Random Forest algorithm produced the most accurate predictions with the lowest RMSE value of 134.63, followed by KNN and Linear Regression. These findings highlight the importance of leveraging BI and Data Mining to transform transaction data into valuable insights, enabling more informed business decisions.

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References

[1] P. Besar Bisnis Penukaran Valuta Asing, M. Changer, F. Pratama Putra, A. Purwanto, and B. Agung Martono, “Prosiding SEMANIS: Seminar Nasional Manajemen BisnisVolume 1, Nomor 1 Tahun 2023 Fakultas Ekonomi dan Bisnis Universitas Pelita Bangsa,” 2023.

[2] M. Anhar Mahebu and R. Sefina Samosir, “Visualisasi Data Penjualan CV. Waskat Karya Metal Menggunakan Pendekatan Business intelligence,” Jurnal Sains dan Teknologi, vol. 10, no. 02, pp. 138–147, Sep. 2023. DOI: https://doi.org/10.53008/kalbiscientia.v10i2.2143

[3] M. Wisnu Alfiansyah, I. N. Switrayana, and L. Mulawarman, “Peran Business Intelligence Dalam Meningkatkan Kinerja Usaha Mikro, Kecil, Dan Menengah (UMKM),” Jurnal Ekonomi dan Bisnis, vol. 1, no. 1, pp. 13–19, 2024, [Online]. Available: https://e.journal.titannusa.org/index.php/economist

[4] A. Fauzi, T. Zaidan Rizqullah, A. Hayatunisa, R. Ramadhan, S. Supriadi, and H. Bramley, “Business Intelligence: Peran dan Fungsinya Dalam Membantu Decision Makers Membuat Keputusan,” Jurnal Ilmu Manajemen Terapan, vol. 4, no. 2, 2022, doi: 10.31933/jimt.v4i2.

[5] A. W. Iswara, H. Setiadi, and A. Wijayanto, “Implementation of Business Intelligence for Quality Support of RSUD Ir. Soekarno Sukoharjo with Data Warehouse,” Jurnal Ilmiah Teknologi dan Informasi, vol. 9, pp. 18–23, Jun. 2020.

[6] M. Sholeh, E. Kumalasari Nurnawati, and U. Lestari, “Penerapan Data Mining dengan Metode Regresi Linear untuk Memprediksi Data Nilai Hasil Ujian Menggunakan RapidMiner,” Jurnal Informatika Sunan Kalijaga, vol. 8, no. 1, pp. 10–21, 2023, [Online]. Available: https://archive.ics.uci.edu/ml/datasheets.php. DOI: https://doi.org/10.14421/jiska.2023.8.1.10-21

[7] D. S. O. Panggabean, E. Buulolo, and N. Silalahi, “Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda,” JURIKOM (Jurnal Riset Komputer), vol. 7, no. 1, pp. 56–62, Feb. 2020, doi: 10.30865/jurikom.v7i1.1947. DOI: https://doi.org/10.30865/jurikom.v7i1.1947

[8] P. W. Rahayu et al., BUKU AJAR DATA MINING, Q. Jambi: PT. Sonpedia Publishing Indonesia, 2024. [Online]. Available: https://www.researchgate.net/publication/377415198

[9] A. Suhendar and T. Hikmatunnisa, “Penerapan Business Intelligence Pada Peluang Jenis Usaha Baru Usaha Mikro Kecil Menengah Dengan Menggunakan Teknologi Online Analytical Processing,” JSiI (Jurnal Sistem Informasi), vol. 9, no. 2, pp. 115–118, Sep. 2022, doi: 10.30656/jsii.v9i2.5183. DOI: https://doi.org/10.30656/jsii.v9i2.5183

[10] E. Marvaro and Samosir. Ridha Sefina, “Penerapan Business Intelligence dan Visualisasi Informasi di CV. Mitra Makmur Dengan Menggunakan Dashboard Tableau,” Jurnal Sains dan Teknologi, vol. 8, no. 2, pp. 37–46, Aug. 2021. DOI: https://doi.org/10.53008/kalbiscientia.v8i2.197

[11] Y. Supriyanto, M. Ilhamsyah, and U. Enri, “Prediksi Harga Minyak Kelapa Sawit MenggunakanLinear Regression Dan Random Forest,” Jurnal Ilmiah Wahana Pendidikan, vol. 8, no. 7, May 2022.

[12] A. Rusydi and F. N. Hasan, “Implementasi business intelligence untuk visualisasi kekuatan sinyal internet di Indonesia menggunakan platform tableau,” TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 1, pp. 132–141, Jan. 2023, doi: 10.37373/tekno.v10i1.378. DOI: https://doi.org/10.37373/tekno.v10i1.378

[13] M. R. Raharjo and A. P. Windarto, “Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 1, p. 317, Jan. 2021, doi: 10.30865/mib.v5i1.2745. DOI: https://doi.org/10.30865/mib.v5i1.2745

[14] Sugiyono, Metode penelitian kuantitatif, kualitatif, dan R&D, 3rd ed. Bandung: Alfabeta, 2021.

[15] M. F. Zulkarnain, N. P. N. Ardiyanti, I. W. W. K. Sandi, I. D. N. T. Hendrawan, and I. B. M. Mahendra, “Perancangan dan Implementasi Data Warehouse Penjualan (Studi Kasus: Northwind Sample Database),” Jurnal Elektronik Ilmu Komputer Udayana, vol. 10, no. 1, pp. 175–188, 2021, [Online]. Available: https://docs.yugabyte.com/latest/sample-data/northwind/. DOI: https://doi.org/10.24843/JLK.2021.v10.i01.p20

[16] E. Bahar, N. Irmalia Azizah, A. Sri Hayuningsih, and D. R. Agushinta, “Analisis Data Pasien Ibu Hamil Menggunakan Metode Business Intelligence,” Indonesian Journal of Business Intelligence, vol. 6, no. 2, pp. 116–123, 2023, doi: 10.21927/ijubi.v6i2.3831.

[17] M. D. Riyanda and Suyanto, “Implementasi Business Intelligence Pada Analisis Perkembangan Hasil Pertanian Provinsi Sumatera Selatan,” Journal of Computer and Information Systems Ampera, vol. 1, no. 3, pp. 174–184, Sep. 2020, [Online]. Available: https://journal-computing.org/index.php/journal-cisa/index DOI: https://doi.org/10.51519/journalcisa.v1i3.44

[18] F. A. Sariasih, “Implementasi Business Intelligence Dashboard dengan Tableau Public untuk Visualisasi Propinsi Rawan Banjir di Indonesia,” Jurnal Pendidikan Tambusai, vol. 6, pp. 14424–14431, 2022.

[19] P. R. Linear, U. Prediksi, H. Beras, D. Indonesia, V. Arinal, and M. Azhari, “Penerapan Regresi Linear Untuk Prediksi Harga Beras Di Indonesia,” Jurnal Sains dan Teknologi, vol. 5, no. 1, 2023, doi: 10.55338/saintek.v5i1.1417.

[20] L. Sari, A. Romadloni, and R. Listyaningrum, “Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest,” Infotekmesin, vol. 14, no. 1, pp. 155–162, Jan. 2023, doi: 10.35970/infotekmesin.v14i1.1751. DOI: https://doi.org/10.35970/infotekmesin.v14i1.1751

[21] N. Eka Pratiwi, L. Suryadi, F. Ardhy, and P. Riswanto, “Penerapan Data Mining Prediksi Penjualan Mebel Terlaris Menggunakan Metode K-Nearest Neighbor(K-NN) (Studi Kasus : Toko Zerita Meubel),” JUSIM (Jurnal Sistem Informasi Musirawas), vol. 7, no. 2, 2022. DOI: https://doi.org/10.32767/jusim.v7i2.1697

[22] S. R. Cholil, T. Handayani, R. Prathivi, and T. Ardianita, “Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa,” IJCIT (Indonesian Journal on Computer and Information Technology), vol. 6, no. 2, pp. 118–127, 2021. DOI: https://doi.org/10.31294/ijcit.v6i2.10438

[23] N. Ichsan, H. Fatah, T. Wahyuni, and E. Ermawati, “Implementasi Orange Data Mining untuk Prediksi Harga Bitcoin,” Jurnal Responsif, vol. 4, no. 2, 2022, [Online]. Available: https://investing.com/crypto/bitcoin/historical- DOI: https://doi.org/10.51977/jti.v4i2.762

[24] E. Fitri, “Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah,” Journal of Applied Computer Science and Technology (JACOST), vol. 4, no. 1, 2023, doi: 10.52158/jacost.491. DOI: https://doi.org/10.52158/jacost.v4i1.491

[25] H. Akbar, M. Rivaldo Ayomi, and Y. Donni Haryanto, “Analisis Perbandingan Model Machine Learning dalam Prediksi Suhu Permukaan Laut Menggunakan Data Model Reanalysis ECMWF,” Seminar Nasional Hasil Penelitian Kelautan Dan Perikanan Tahun 2022, 2022, [Online]. Available: https://cds.climate.copernicus.eu/.