Prediksi Kelalaian Pinjaman Bank Menggunakan  Random Forest dan Adaptive Boosting

Sanjaya, Joseph; Renata, Erick; Budiman, Vincent Elbert; Anderson, Francis; Ayub, Mewati

doi:10.28932/jutisi.v6i1.2313

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…Since the other clustering algorithms are frequently prohibitively costly when the datasets are very large and visualization techniques are applied for rule analysis, building mathematical synopses of the entities associated with each rule, the hot spots method combines the k-means segmentation method for cluster detection. [11][12][13] expanded the spots technique by generating and exploring the rules using a learning algorithm.…”

Section: Concept Of Fraudmentioning

confidence: 99%

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

Esmail

Alsheref

Aboutabl

2023

Int. j. electr. comput. eng. syst. (Online)

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At the era of digital transformation, fraud has dramatically increased, notably in the banking industry. Annually, it now costs the world's economies billions of dollars. Daily, news of financial fraud has a negative influence on the world economy. According to the harsh loss caused by fraud, effective strategies and methods for avoiding income statement fraud have to be implemented. Also, the procedure of identification should be applied. This is regarded as a result of the development of modern technology, modern invention, and the rapidity of global communications. Actually, deterrent technologies are most effective to reduce fraud and overcome cons. So, it is necessary to find ways to overcome such deterrence by depending on developed methods to identify fraud. Data mining techniques are currently the most widely used methods for the prevention and detection of financial fraud. The use of datasets for fraud detection complies with the norms of data mining, which include feature selection, representation, data gathering and management, pre-processing, comment, and summative evaluation. Methodologies for identifying fraud are essential if we want to catch criminals after fraud prevention has failed. The greatest fraud detection strategies for locating loan banking and financial fraud are compared in this article.

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Section: Concept Of Fraudmentioning

confidence: 99%

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

Esmail

Alsheref

Aboutabl

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Random Forest (RF) ialah metode yang dapat menaikan nilai akurasi, sehingga simpul anak untuk setiap node yang dilakukan secara acak dapat meningkat, dan diperlukan untuk membuat pohon keputusan yang terdiri dari internal node, root node, dan leaf node dengan cara mengambil atribut maupun data secara acak menurut ketetapan yang berlaku [5]. Random Forest merupakan algoritma machine learning yang digunakan sebagai klasifikasi, bertugas untuk mengelompokkan data yang bergantung pada kecenderungannya, berisi kumpulan dari decision tree yang beroperasi menjadi suatu gabungan fungsional, dan dapat berjalan efisien pada data yang jumlahnya banyak [13]. Algoritma training untuk random forest menggunakan bootstrap aggregating (bagging).…”

Section: Random Forestunclassified

Model Analisis Kasus Covid-19 Di Indonesia Menggunakan Algoritma Regresi Linier Dan Random Forest

Hidayanti

Siregar

Lestari

et al. 2021

petir

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Kenaikan kasus covid-19 di Indonesia tidak bisa diperkirakan perkembangannya, sehingga menyebabkan buruknya berbagai aspek kehidupan di Indonesia. Analisis data sangat penting untuk dilakukan, dengan menganalisis maka akan dihasilkan informasi baru yang bisa digunakan untuk menunjang penelitian lebih dalam terhadap permasalahan yang sama. Penelitian ini bertujuan untuk menganalisis data covid-19 di Indonesia dengan cara mengetahui nilai akurasi yang diperoleh dalam data. Dalam penelitian ini metode yang digunakan yaitu metode regresi linier dan random forest. Hasil penelitian dapat diketahui bahwa dalam penelitian ini cara analisis yang dilakukan yaitu menggunakan perhitungan manual dengan tools microsoft excel, pemrograman bahasa python menggunakan tools google colaboratory dan perangkat lunak pengolahan data menggunakan tools rapidminer. Nilai akurasi dalam setiap metode dapat berbeda sesuai dengan tools yang digunakan. Pada metode regresi linier nilai akurasi tertinggi adalah 99,7% dengan nilai RMSE (root mean squared error) yaitu 26,19, data tersebut dianalisis menggunakan perhitungan manual dengan tools microsoft excel. Sedangkan untuk metode random forest nilai akurasi tertinggi adalah 98,4% dan dianalisis menggunakan tools rapidminer

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“…After the customer applies for a loan, the bank will validate the customer's eligibility to obtain a loan or not [2]. Loans are the main income priority for banks, but often loans will harm the bank if the customer is not smooth in paying, there will be bad loans [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Customer Loans Using Hybrid Data Mining

2022

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At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.

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Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting

Cited by 7 publications

References 7 publications

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

Model Analisis Kasus Covid-19 Di Indonesia Menggunakan Algoritma Regresi Linier Dan Random Forest

Classification of Customer Loans Using Hybrid Data Mining

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