Implementasi Metode Deteksi Tepi Laplacian dan Jarak Euclidean untuk Identifikasi Tanda Tangan

Afandi, Mas Aly; Purnama, Sevia Indah; Crisianti, Risa Farid

doi:10.25077/jnte.v9n1.756.2020

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…The next stage is to apply K-Means Clustering algorithm to the dataset when significant frequency deviations of more than ±5% are absent or to conduct more in-depth data analysis. The Euclidean Distance formula in (3) is used in K-Means algorithm [18]- [20].…”

Section: B K-means Clustering Algorithmmentioning

confidence: 99%

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Wiryadinata,

Sugiharto,

Tarno

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

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Section: B K-means Clustering Algorithmmentioning

confidence: 99%

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Wiryadinata,

Sugiharto,

Tarno

2023

J. Inf. Syst. Eng. Bus. Intell.

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“…Citra digital juga memiliki kanal warna dasar yang membentuknya yang terdiri dari merah, hijau dan biru sehingga dikenal dengan citra RGB [9]. Bentuk persamaan matrik dari citra digital ditulis seperti pada persamaan 1 sebagai berikut [8]:…”

Section: Gambar 3 Koordinat Citra Digitalunclassified

Klasifikasi Jenis Kayu Menggunakan Esktrasi Fitur Gray Level Co-Occurence Matrix dan Multilayer Perceptron

Wibowo

Erwanto

Kusumastutie

2021

JNTE

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Jenis kayu umumnya dicirikan dari warna, corak dan serat karena kayu secara fisik memiliki ciri yang hampir sama. Untuk menentukan jenis kayu, diperlukan pengalaman dan pengetahuan tentang kayu. Penelitian ini menerapkan teknologi pengolahan citra digital menggunakan metode ekstraksi fitur GLCM (gray level co-occourrence matrix) untuk menghasilkan nilai ekstraksi fitur pada tesktur kayu. Parameter nilai dihasilkan pada ekstraksi fitur GLCM adalah correlation, contrast, energy dan homogeneity. Hasil dari parameter GLCM kemudian digunakan sebagai data untuk klasifikasi jenis kayu menggunakan metode MLP (multilayer perceptron). Dalam penelitian ini digunakan 4 jenis kayu sebagai pengujian dan datasheet, yaitu kayu jati, sengon, mahoni dan mindi. Hasil dari penelitian ini, diperoleh tingkat akurasi terbaik pada data validasi sebesar 88,75 %. Keluaran dari penelitian ini berupa data softmax dengan menggunakan metode MLP dengan nilai error pada data training yang mendekati target sebesar 0.029421 dengan epoch 1.000 iterasi.

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“…Terdapat berbagai macam penelitian yang menggunakan teknik pengolahan citra digital ini seperti yang dilakukan [1] pada penelitian tersebut mengklasifikasikan keramik menjadi empat kategori kualitas yang menggunakan operasi selisih piksel putih sebagai parameter penentunya. Selain itu [2] juga menerapkan teknik pengolahan citra digital dalam proses inspeksi logam yang mana menurutnya teknik ini tepat karena inspeksi logam membutuhkan proses yang cepat.…”

Section: Pendahuluanunclassified

Optimasi Parameter Pengukuran Dimensi dan Defect Ubin Keramik dengan Metode Taguchi

Atmaja¹,

Herliansyah²

2021

JSC

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The ceramics industry in Indonesia has a large contribution to the growth of various aspects in Indonesia. But in reality, there is still an imbalance between exports and imports for ceramic products. One way is to improve the quality of the ceramic industry in Indonesia. In fact, the ceramic quality inspection process in the ceramic industry is still done manually which can make mistakes in identifying defects. In this study, the design of variable identification system of ceramics was carried out specifically in the area of ceramics and dry spot defects on ceramic surfaces using image processing. Whereas to get a low error rate against the applicable variables, a design of experiment with the Taguchi approach is carried out. The results show that 50 cm distance, 300 lux light, 1x resize and 0.06 threshold can produce an image that has the smallest error value when identifying ceramic area and dry spot defects on the ceramic surface.

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Implementasi Metode Deteksi Tepi Laplacian dan Jarak Euclidean untuk Identifikasi Tanda Tangan

Cited by 6 publications

References 4 publications

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Klasifikasi Jenis Kayu Menggunakan Esktrasi Fitur Gray Level Co-Occurence Matrix dan Multilayer Perceptron

Optimasi Parameter Pengukuran Dimensi dan Defect Ubin Keramik dengan Metode Taguchi

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