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All Journal Jurnal Bina Praja Applied Information System and Management
Domas, Zico Karya Saputra
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Humanities Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Prediksi Transparansi Pelanggaran Fraud sebagai Budaya Antikorupsi: Eksperimen Decision Tree Domas, Zico Karya Saputra; Subagio; Rizkiawan, M.
Jurnal Bina Praja Vol 14 No 2 (2022)
Publisher : Research and Development Agency Ministry of Home Affairs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21787/jbp.14.2022.289-300

Abstract

Several prominent reports have highlighted the unsatisfactory level of anti-corruption transparency for the private sector in Indonesia. Hence, the anti-corruption vision is still an aspect that deserves to be campaigned for to form an advanced and just civilization. This study aims to obtain a pattern of knowledge in predicting the level of transparency of disclosure of fraud violations based on a data mining approach. The classification function algorithm in this study is a decision tree which is then compared with other classification function algorithms, naive Bayes, and k-in. The sample in this study is 141 companies combined in the construction, mining, and banking sectors, which are on the IDX for the 2019 period. As a result, the decision tree algorithm provides the second-best performance in predicting the level of corporate transparency, namely an accuracy of 70.92% and an AUC level of 0.740. Then in terms of different tests, the decision tree algorithm is in the same cluster as the algorithm with the best performance because the t-test results show no significant difference. Based on the pattern generated by the decision tree algorithm, the elements of opportunity, pressure, and arrogance are considered key factors in predicting the level of transparency of disclosure of fraud violations. One of them can be interpreted that a company that is supervised by a minimum of four independent commissioners means company tends to be predicted to be more daring in disclosing anti-corruption information in its annual report to the wider public data mining algorithms utilizing the advantages of each agency's internal data volume to map anti-corruption cultural socialization strategies in private sector companies.
Peningkatan Performa Decision Tree dengan AdaBoost untuk Klasifikasi Kekurangtransparanan Informasi Anti-Korupsi Domas, Zico Karya Saputra; Rakhmadi, Roby
Applied Information System and Management (AISM) Vol. 5 No. 2 (2022): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v5i2.24887

Abstract

Di era big data saat ini, peran teknik data mining sangatlah dibutuhkan terkait kebutuhan pengambilan keputusan yang akurat. Algoritma decision tree telah lazim diterapkan untuk menemukan pola klasifikasi karena mudah diinterpretasikan namun harus senantiasa dievaluasi tingkat performanya. Adaboost merupakan salah satu metode untuk meningkatkan performa algoritma decision tree. Eksperimen dilakukan pada 141 sampel perusahaan yang melantai di Bursa Efek Indonesia sektor konstruksi-infrastruktur, pertambangan-perminyakan, dan sektor perbankan pada periode 2019, dengan menerapkan teknik adaboost pada decision tree dengan parameter maximum depth dan confidence yang diuji dalam enam skenario berbeda berdasarkan informasi anti-korupsi pada pengungkapan laporan tahunan perusahaan. Hasil ekperimen decision tree untuk akurasi sebesar 69,5%, AUC-optimistis 0,826, dan AUC 0,756, sedangkan rerata hasil dari enam skenario decision tree versi  adaBoost untuk akurasi sebesar 71,16%, AUC-optimistis 0,8905, dan AUC 0,744, sehingga dapat disimpulkan bahwa pengklasifikasian atas prediksi klasifikasi dengan metode adaBoost layak diterapkan sebagai upaya alternatif untuk meningkatkan tingkat performa yang lebih baik. 
Co-Authors Amrie Firmansyah Rahman, Muhammad Abdul Izzatur Rakhmadi, Roby Rizkiawan, M. Subagio