Prediksi Transparansi Pelanggaran Fraud sebagai Budaya Antikorupsi
Eksperimen Decision Tree
DOI:
https://doi.org/10.21787/jbp.14.2022.289-300Kata Kunci:
korupsi, antikorupsi, transparansi, data mining, klasifikasi, decision treeAbstrak
Beberapa laporan terkemuka telah menyoroti tingkat transparansi anti-korupsi yang masih belum memuaskan untuk sektor swasta di Indonesia sehingga visi anti-korupsi masih menjadi aspek yang patut dikampanyekan untuk membentuk peradaban yang maju dan berkeadilan. Penelitian ini bertujuan untuk memperoleh suatu pola pengetahuan dalam melakukan prediksi atas tingkat transparansi pengungkapan pelanggaran fraud berdasarkan pendekatan data mining. Algoritma fungsi klasifikasi pada penelitian ini adalah decision tree yang kemudian dibandingkan dengan algoritma fungsi klasifikasi lainnya, naive bayes dan k-nn. Sampel dalam penelitian ini sebanyak 141 perusahaan gabungan sektor konstruksi, pertambangan, dan perbankan, yang melantai di BEI periode 2019. Hasilnya, algoritma decision tree memberikan performa kedua terbaik dalam memprediksi tingkat transparansi korporasi, yaitu akurasi 70,92% dan tingkat AUC 0,740. Kemudian dalam hal uji beda, algoritma decision tree ada pada klaster yang sama dengan algoritma dengan performa terbaik karena hasil t-test menunjukkan bahwa tidak ada perbedaan yang signifikan. Berdasarkan pola yang dihasilkan oleh algoritma decision tree, unsur kesempatan, tekanan, dan arogansi, disimpulkan sebagai faktor kunci dalam memprediksi tingkat transparansi pengungkapan pelanggaran fraud . Salah satunya dapat dimaknai bahwa perusahaan yang diawasi oleh minimal empat orang komisaris independen berarti perusahaan tersebut cenderung diprediksikan akan lebih berani dalam mengungkapkan informasi anti-korupsi pada laporan tahunannya ke publik secara lebih luas. Penelitian ini juga merekomendasikan agar setiap instansi yang berotoritas di Indonesia menerapkan pendekatan algoritma data mining dalam memanfaatkan keunggulan volume data internal masing-masing instansi untuk memetakan strategi sosialisasi budaya anti-korupsi pada perusahaan sektor swasta.
Referensi
Abdullahi, R., & Mansor, N. (2015). Fraud Triangle Theory and Fraud Diamond Theory. Understanding the Convergent and Divergent for Future Research. International Journal of Academic Research in Accounting, Finance and Management Sciences, 5(4), 30–37. https://doi.org/10.6007/IJARAFMS/v5-i4/1823
ACFE. (2020). Report to the Nations: 2020 Global Fraud Study on Occupational Fraud and Abuse. https://legacy.acfe.com/report-to-the-nations/2020/
Argandoña, A. (2005). Private-to-Private Corruption. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.685864
Artiach, T., Lee, D., Nelson, D., & Walker, J. (2010). The Determinants of Corporate Sustainability Performance. Accounting & Finance, 50(1), 31–51. https://doi.org/10.1111/j.1467-629X.2009.00315.x
Bablani, A., Edla, D. R., & Dodia, S. (2018). Classification of EEG Data Using K-nearest Neighbor Approach for Concealed Information Test. Procedia Computer Science, 143, 242–249. https://doi.org/10.1016/j.procs.2018.10.392
Banerjee, R., Bourla, G., Chen, S., Kashyap, M., & Purohit, S. (2018). Comparative Analysis of Machine Learning Algorithms through Credit Card Fraud Detection. 2018 IEEE MIT Undergraduate Research Technology Conference (URTC), 1–4. https://doi.org/10.1109/URTC45901.2018.9244782
Bermúdez, J. R., López-Estrada, F. R., Besançon, G., Torres, L., & Santos-Ruiz, I. (2020). Leak-Diagnosis Approach for Water Distribution Networks based on a k-NN Classification Algorithm. IFAC-PapersOnLine, 53(2), 16651–16656. https://doi.org/10.1016/j.ifacol.2020.12.795
Bujlow, T., Riaz, T., & Pedersen, J. M. (2012). Classification of HTTP Traffic Based on C5.0 Machine Learning Algorithm. 2012 IEEE Symposium on Computers and Communications (ISCC), 000882–000887. https://doi.org/10.1109/ISCC.2012.6249413
Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A Novel Selective Naïve Bayes Algorithm. Knowledge-Based Systems, 192(xxxx), 105361. https://doi.org/10.1016/j.knosys.2019.105361
Chi, C. W., Hung, K., Cheng, H. W., & Tien Lieu, P. (2015). Family Firms and Earnings Management in Taiwan: Influence of Corporate Governance. International Review of Economics & Finance, 36, 88–98. https://doi.org/10.1016/j.iref.2014.11.009
Christian, N., Basri, Y. Z., & Arafah, W. (2019). Analysis of Fraud Pentagon to Detecting Corporate Fraud in Indonesia. International Journal of Economics, Business and Management Research, 3(08), 1–13. https://www.researchgate.net/publication/335060762
Domas, Z. K. S., & Subagio. (2022). Fraud Hexagon Analysis on the Less-Transparent Anti-corruption Disclosures. 3rd National Conference Accounting and Fraud Auditing.
Fitri, F., Syukur, M., & Justisa, G. (2019). Do the Fraud Triangle Components Motivate Fraud in Indonesia? Australasian Accounting, Business and Finance Journal, 13(4), 63–72. https://doi.org/10.14453/aabfj.v13i4.5
Gorunescu, F. (2011). Data Mining (12th ed., Vol. 12). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19721-5
Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and Analysis of Logistic Regression, Naïve Bayes and KNN Machine Learning Algorithms for Credit Card Fraud Detection. International Journal of Information Technology, 13(4), 1503–1511. https://doi.org/10.1007/s41870-020-00430-y
Khadafy, A. R., & Wahono, R. S. (2015). Penerapan Naive Bayes untuk Mengurangi Data Noise pada Klasifikasi Multi Kelas dengan Decision Tree. Journal of Intelligent Systems, 1(2), 136–142. https://journal.ilmukomputer.org/index.php?journal=jis&page=article&op=view&path%5B%5D=78
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data Mining Techniques for the Detection of Fraudulent Financial Statements. Expert Systems with Applications, 32(4), 995–1003. https://doi.org/10.1016/j.eswa.2006.02.016
Kück, M., & Freitag, M. (2021). Forecasting of Customer Demands for Production Planning by Local k-nearest Neighbor Models. International Journal of Production Economics, 231, 107837. https://doi.org/10.1016/j.ijpe.2020.107837
Kusumosari, L. (2020). Analisis Kecurangan Laporan Keuangan Melalui Fraud Hexagon pada Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia Tahun 2014–2018 [Universitas Negeri Semarang]. http://lib.unnes.ac.id/40840/
Larose, D. T. (2006). Data Mining Methods and Models. John Wiley & Sons, Inc.
Li, T., Li, J., Liu, Z., Li, P., & Jia, C. (2018). Differentially Private Naive Bayes Learning Over Multiple Data Sources. Information Sciences, 444, 89–104. https://doi.org/10.1016/j.ins.2018.02.056
Lo, A. W. Y., Wong, R. M. K., & Firth, M. (2010). Tax, Financial Reporting, and Tunneling Incentives for Income Shifting: An Empirical Analysis of the Transfer Pricing Behavior of Chinese-Listed Companies. Journal of the American Taxation Association, 32(2), 1–26. https://doi.org/10.2308/jata.2010.32.2.1
Lokanan, M., & Sharma, S. (2018). A Fraud Triangle Analysis of the Libor Fraud. Journal of Forensic & Investigative Accounting, 10(2), 187–212. https://doi.org/10.25316/IR-1573
Malini, N., & Pushpa, M. (2017). Analysis on Credit Card Fraud Identification Techniques Based on KNN and Outlier Detection. 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 255–258. https://doi.org/10.1109/AEEICB.2017.7972424
Matoussi, H., & Gharbi, I. (2011). Board Independence and Corporate Fraud: The Case of Tunisian Firms (No. 620; Economic Research Forum).
Mienye, I. D., Sun, Y., & Wang, Z. (2019). Prediction Performance of Improved Decision Tree-Based Algorithms: A Review. Procedia Manufacturing, 35, 698–703. https://doi.org/10.1016/j.promfg.2019.06.011
Nguyen, L. T. T., Vo, B., Hong, T.-P., & Thanh, H. C. (2012). Classification Based on Association Rules: A Lattice-Based Approach. Expert Systems with Applications, 39(13), 11357–11366. https://doi.org/10.1016/j.eswa.2012.03.036
Niazi, K. A. K., Akhtar, W., Khan, H. A., Yang, Y., & Athar, S. (2019). Hotspot Diagnosis for Solar Photovoltaic Modules Using a Naive Bayes Classifier. Solar Energy, 190(July), 34–43. https://doi.org/10.1016/j.solener.2019.07.063
Novitasari, A. R., & Chariri, A. (2018). Analisis Faktor-Faktor yang Mempengaruhi Financial Statement Fraud dalam Perspektif Fraud Pentagon. Diponegoro Journal of Accounting, 7(4), 1–15. https://ejournal3.undip.ac.id/index.php/accounting/article/view/25572
Pambudi, A. S. (2020). Analisis Pendeteksian Financial Statement Fraud Menggunakan Beneish M-Score Model dan Data Mining.
Perols, J. (2011). Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms. AUDITING: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
Pradana, E. (2018). Analisis Penerapan Adaptive Boosting (Adaboost) dalam Meningkatkan Performasi Algoritma C4.5. Sekolah Tinggi Teknologi Pelita Bangsa.
Puspitha, M. Y., & Yasa, G. W. (2018). Fraud Pentagon Analysis in Detecting Fraudulent Financial Reporting (Study on Indonesian Capital Market). International Journal of Sciences: Basic and Applied Research, 42(5), 93–109. https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/9628
Salim, F. (2018). Anti Corruption Measurements in Business: Transparency in Corporate Reporting (TRAC) and Beyond.
Sasongko, N., & Wijayantika, S. F. (2019). Faktor Resiko Fraud terhadap Pelaksanaan Fraudulent Financial Reporting (Berdasarkan Pendekatan Crown’s Fraud Pentagon Theory). Riset Akuntansi Dan Keuangan Indonesia, 4(1), 67–76. https://doi.org/10.23917/reaksi.v4i1.7809
Shaheen, M., Zafar, T., & Ali Khan, S. (2020). Decision Tree Classification: Ranking Journals Using IGIDI. Journal of Information Science, 46(3), 325–339. https://doi.org/10.1177/0165551519837176
Singh, D., Choudhary, N., & Samota, J. (2013). Analysis of Data Mining Classification With Decision Tree Technique. Global Journal of Computer Science and Technology Software & Data Engineering, 13(13), 1–5.
Sitorus, Z., Saputra S., K., & Sulistianingsih, I. (2021). C4.5 Algorithm Modeling for Decision Tree Classification Process Against Status UKM. Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology - ICEST. https://doi.org/10.5220/0010046105360540
Soni, K. B., Chopade, M., & Vaghela, R. (2021). Credit Card Fraud Detection Using Machine Learning Approach. Applied Information System and Management (AISM), 4(2), 71–76. https://doi.org/10.15408/aism.v4i2.20570
Sopian, Pratama, R. S., & Subagio. (2020). The Indonesia’s Anti Corruption Strategies: A Gap Analysis to the UNCAC’S Preventive Measurarements. Test Engineering and Management, 83, 12087–12108. http://www.testmagzine.biz/index.php/testmagzine/article/view/5824
Sumanto, S., Marita, L. S., Mazia, L., & Ratnasari, T. W. (2021). Analisis Kelayakan Kredit Rumah Menggunakan Metode Naïve Bayes untuk Mengurangi Kredit Macet. Applied Information System and Management (AISM), 4(1), 17–22. https://doi.org/10.15408/aism.v4i1.20274
Syarif, L. M. (2021). Memaknai CPI 2020 yang Menurun. Kemitraan Partnership.
Tarjo, T., & Herawati, N. (2017). The Comparison of Two Data Mining Method to Detect Financial Fraud in Indonesia. GATR Accounting and Finance Review, 2(1), 01–08. https://doi.org/10.35609/afr.2017.2.1(1)
Transparency International. (2020). Corruption Perceptions Index. https://www.transparency.org/en/cpi/2020
Transparency International Indonesia. (2016). Transparency in Corporate Reporting.
Transparency International Indonesia. (2017). Transparency in Corporate Reporting: Perusahaan Terbesar Indonesia. https://ti.or.id/transparency-in-corporate-reporting/
Transparency International Indonesia. (2018). Transparency in Corporate Reporting.
Triguero, I., García‐Gil, D., Maillo, J., Luengo, J., García, S., & Herrera, F. (2019). Transforming Big Data Into Smart Data: An Insight on the Use of the K-nearest Neighbors Algorithm to Obtain Quality Data. WIREs Data Mining and Knowledge Discovery, 9(2), 1–24. https://doi.org/10.1002/widm.1289
Vousinas, G. L. (2019). Advancing Theory of Fraud: The S.C.O.R.E. Model. Journal of Financial Crime, 26(1), 372–381. https://doi.org/10.1108/JFC-12-2017-0128
Wahono, R. S., Herman, N. S., & Ahmad, S. (2014). Neural Network Parameter Optimization Based on Genetic Algorithm for Software Defect Prediction. Advanced Science Letters, 20(10), 1951–1955. https://doi.org/10.1166/asl.2014.5641
Widodo, A., & Fanani, Z. (2020). Military Background, Political Connection, Audit Quality and Earning Quality. Jurnal Akuntansi, 24(1), 84–99. https://doi.org/10.24912/ja.v24i1.658
Wirawan, C. (2020). Teknik Data Mining Menggunakan Algoritma Decision Tree C4.5 untuk Memprediksi Tingkat Kelulusan Tepat Waktu. Applied Information System and Management (AISM), 3(1), 47–52. https://doi.org/10.15408/aism.v3i1.13033
Wu, W., Johan, S. A., & Rui, O. M. (2016). Institutional Investors, Political Connections, and the Incidence of Regulatory Enforcement Against Corporate Fraud. Journal of Business Ethics, 134(4), 709–726. https://doi.org/10.1007/s10551-014-2392-4
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2022 Penulis
Artikel ini berlisensiCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
