Klasifikasi Data Aging Tunggakan Nasabah Menggunakan Metode Decision Tree Pada ULaMM Unit Kolaka

Sarimuddin, Sarimuddin; Sari, Jayanti Yusmah; Mail, Muhammad; Masalu, Muh. Ariyandhi; Aristika, Reski Surya; Nurfagra, Nurfagra

doi:10.19184/isj.v5i1.16964

Cited by 4 publications

(7 citation statements)

References 1 publication

(2 reference statements)

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“…Sedangkan Decision tree disis lain adalah bagan alir dengan dengan bentuk pohon (tree) dimana setiap node (internal node) mewakili cabang dan atributnya menunjukkan hasil pengujian atau nilai atribut, sedangkan pada daunnya digunakan untuk mewakili kelas atau distribusi kelas. Decision tree juga biasanya digunakan untuk melakukan pengenalan pada pola statistic (Sarimuddin, 2020) Pada penelitian yang sudah diteliti oleh (Ristasari Dwi Septiana, 2021)…”

Section: Pendahuluanunclassified

Perbandingan Algoritma NBC dan Decision Tree pada Sentimen Analisis Mengenai Vaksinasi Covid-19 Di Indonesia

Agtira¹,

Handayani

Masruriyah

2023

remik

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Perkembangan Twitter di Indonesia sebagai platform media sosial yang digemari masyarakat, yang menunjukkan Indonesia negara pengguna Twitter terbanyak kelima di dunia pada Januari 2022. Seiring berkembangnya tren saat ini, memaksa pemerintah Indonesia untuk melakukan berbagai cara lebih banyak berinteraksi dengan masyarakat, salah satunya dengan penggunaan Twitter. Memanfaatkan media social Twitter, pemerintah akan mendapatkan berbagai informasi yang sedang kontroversial di kalangan masyarakat. Seperti dilaksanakannya kegiatan vaksinasi oleh pemerintah Indonesia, banyak masyarakat menilai kontroversial dalam kegiatan vaksinasi ini dan banyak kalangan di masyarakat yang memberikan berbagai pendapatnya di media sosial. Melalui Twitter salah satunya, masyarakat berbagi postingan opini masing-masing tentang vaksinasi ini. Tahapan analisis pada pengujian ini akan membandingkan dua algoritma klasifikasi yakni Naive Bayes Classifier (NBC) dengan Decision Tree dengan menggunakan perhitungan Confusion Matrix untuk mengukur kinerja pada kedua algoritma tersebut. Hasil tes pengujian akhir disajikan dalam bentuk nilai akurasi, presisi dan recall. Perbandingan kedua algoritma klasifikasi Naïve Bayes dengan Decision Tree hasil yang diperoleh memiliki perbedaan tingkat akurasi yang berbeda. Algoritma Naïve Bayes Classifier mendapat hasil akurasi 93.96 % dan nilai presisi 91% dengan nilai recall 98%, sedangkan pada algoritma Decision Tree menghasilkan nilai akurasi sebesar 88.64 %, dengan nilai presisi 91% dan nilai recall 94%. Hasil ini didasarkan pada perhitungan Confusion Matrix. Kemudian bisa disimpulkan jika algoritma NBC penelitian ini lebih akurat dibanding algoritma Decision Tree.

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Section: Pendahuluanunclassified

Perbandingan Algoritma NBC dan Decision Tree pada Sentimen Analisis Mengenai Vaksinasi Covid-19 Di Indonesia

Agtira¹,

Handayani

Masruriyah

2023

remik

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“…The tenors are the loan repayment period agreed upon by the customer with the lender whose indicator is the number of months (period) of credit (Nurhapsari & Apriani, 2021;Sarimuddin et al, 2020;Utami & Dewi, 2021;Wahyuni & Maulidia, 2020).…”

Section: Tenorsmentioning

confidence: 99%

“…The loan is the number of loans received by customers, of which payments will be made in stages in the following months, the indicator of which is the rupiah value of the principal loan/mortgage value (Nurhapsari & Apriani, 2021;Sarimuddin et al, 2020;Utami & Dewi, 2021;Wahyuni & Maulidia, 2020).…”

Section: Loanmentioning

confidence: 99%

The Effect of Tenors, Installments, Loans on Arrears, and Moderating Variables

Ardiansyah

2023

j.ofaccount.var.scopes

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The research objective is to determine the effect of tenors, installments, and loans on arrears and moderating variables. This type of quantitative research. Secondary data type. The research population for customer loan data for 2017-2022 and customer loans maturing in 2022-2027 are 527 customers as a sample. The analytical method uses path analysis with the help of SmartPLS 3. The results of the study: (1) Tenors have a negative and significant effect on Arrears; (2) Tenors have a negative and significant effect on Installment; (3) Loans have a positive and significant effect on Tenors; (4) Loans have a positive and significant effect on Arrears; (5) Loan has a positive and significant effect on Installment; (6) Installment has a negative and significant effect on Arrears; (7) Loans are not significant to Arrears moderated by Installment; and (8) Tenors have a negative and significant effect on Arrears moderated by Installment. The research findings provide empirical evidence that the determinants of customer arrears that can have an impact on arrears are tenors, loans, and installments. The implication of the research results is the discovery of factors that need to be considered by the company in providing loans to customers by carefully considering the alignment between loans to tenors, then tenors to installments so that the determination is not burdensome to customers so that arrears are smaller and can increase company profitability. Recommendations for further research: (1) Conduct data analysis on consumptive and productive loans; (2) Develop variables: Remaining loan principal, Arrears of fines, and Interest arrears.

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“…Algoritma Tree biasa dipakai untuk pengenalan pola statistik. (Sarimuddin et al, 2020). Decision Tree terbuat dari tiga simpul yaitu leaf, lalu terdiri juga dari simpul root yang merupakan titik awal dari suatu decision tree, dan yang terakhir adalah simpul perantara yang berhubungan dengan suatu pengujian.…”

Section: Pendahuluanunclassified

Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

Puspita

Widodo

2021

JIUP

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BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.

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Klasifikasi Data Aging Tunggakan Nasabah Menggunakan Metode Decision Tree Pada ULaMM Unit Kolaka

Cited by 4 publications

References 1 publication

Perbandingan Algoritma NBC dan Decision Tree pada Sentimen Analisis Mengenai Vaksinasi Covid-19 Di Indonesia

Perbandingan Algoritma NBC dan Decision Tree pada Sentimen Analisis Mengenai Vaksinasi Covid-19 Di Indonesia

The Effect of Tenors, Installments, Loans on Arrears, and Moderating Variables

Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

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