Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network

Suniantara, I Ketut Putu; Suwardika, Gede; Soraya, Siti

doi:10.30812/varian.v3i2.651

Cited by 3 publications

(3 citation statements)

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“…Tata cara ini berfokus pada gimana menghasilkan sesuatu algoritma yang memiliki sesuatu pola sebagai data dalam proses learning. [7].…”

Section: Supervised Learningunclassified

Analisis Pendukung Keputusan Penerima Bantuan PKH Kecamatan Malo Berbasis Web Menggunakan Metode Naïve Bayes

Jauharuddin

2023

Al-Mantiq

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PKH merupakan program pengentasan kemiskinan yang melibatkan beberapa sektor yang memerlukan kontribusi dan komitmen instansi/instansi terkait antara lain: Kementerian Kesehatan, Kementerian Pendidikan dan Kebudayaan, Kementerian Agama, BPS, dan PT Pos Indonesia serta keuangan perbankan. lembaga dalam menyalurkan bantuan kepada peserta. PKH. Pelaksanaan Program Keluarga Harapan (PKH) sampai saat ini diharapkan dapat menjadi solusi dalam upaya memutus mata rantai kemiskinan bagi RTSM dimana kepesertaan PKH tidak hanya dapat memberikan manfaat bagi masyarakat penerima PKH, tetapi juga perubahan gaya hidup dan perilaku terkait terhadap peningkatan pendidikan dan kesehatan. dapat memberikan dampak yang luas bagi masyarakat di wilayah pelaksanaan program PKH. Untuk itu, orang yang wajib dan berhak mengambil pembayaran adalah orang yang namanya tercantum pada Kartu PKH. Calon penerima yang terpilih harus menandatangani perjanjian bahwa selama menerima bantuan, mereka akan: 7-15 tahun dan anak-anak berusia 16-18 tahun tetapi belum menyelesaikan pendidikan dasar wajib belajar 9 tahun; Membawa anak usia 0-6 tahun ke fasilitas kesehatan sesuai prosedur PKH kesehatan anak; dan Bagi ibu hamil wajib memeriksakan kesehatan diri dan janinnya ke fasilitas kesehatan sesuai dengan prosedur kesehatan PKH ibu hamil. Sistem ini mampu menganalisis masyarakat yang layak mendapatkan program keluarga harapan berdasarkan kriteria ibu hamil, anak usia dini, lansia, anak sekolah, hasil uji kelayakan Software Testing Questionnaire Sistem Pendukung Keputusan untuk Penerima Bantuan Program Keluarga Harapan oleh 13 responden menunjukkan kelayakan aspek Uji Komunikasi sebesar 86%, pada Aspek Uji Graphical User Interface - Aksesibilitas sebesar 87,01%, pada Aspek Uji Graphical User Interface - Responsiveness sebesar 85,25%, Untuk Aspek Uji Graphical User Interface - Efficiency sebesar 87,18 %, dan untuk Aspek Uji Gruaphical User Interface - Comprehensibility sebesar 85%.

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“…Tata cara ini berfokus pada gimana menghasilkan sesuatu algoritma yang memiliki sesuatu pola sebagai data dalam proses learning. [7].…”

Section: Supervised Learningunclassified

Analisis Pendukung Keputusan Penerima Bantuan PKH Kecamatan Malo Berbasis Web Menggunakan Metode Naïve Bayes

Jauharuddin

2023

Al-Mantiq

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“…The accuracy of a classifier can be stated as the ratio of accurately predicting the class (positive and negative class) in the dataset [13]. Accuracy measures for binary problem classification can be described in terms of four terms: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) [14]. These terms can be arranged in a 2 × 2 matrix called confusion matrix as tabulated in Table 2.…”

Section: Performance Of Classification Modelmentioning

confidence: 99%

Credit Card Fraud Detection Using Linear Discriminant Analysis (Lda), Random Forest, and Binary Logistic Regression

Ahsan

Susanto²,

Virania³

et al. 2022

BAREKENG: J. Math. & App.

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The growth of electronic payment usage makes the monetary tension of credit-card deception is changing into major defiance for finance and technology companies. Therefore, pressuring them to continuously advance their fraud detection system is crucial. In this research, we describe fraud detection as a classification issue by comparing three methods. The method used is Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. The dataset used is a dataset containing transactions made by credit cards. The challenge in this analysis is that the dataset is highly unbalanced, so SMOTE must perform better on the data. The dataset contains only continuous features that are transformed into Principal Component Scores (PCs). The results show that the binary regression algorithm, the Random Forest algorithm, and the Linear Discriminant Analysis with variables that have SMOTE have AUC values greater than using the original variables. The largest AUC value was obtained by binary logistic regression with 90:10 separation data and Random Forest Algorithm with 60:40 separation data.

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“…The main aim of this research is to identify factors that influence students in achieving graduation, whether on time or late. As was done in the research (Suniantara, Suwardika, & Soraya, 2020) that research on student graduation rates can be carried out using neural network methods. This research will be divided into two main classification categories: on-time graduation and late graduation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method

Ariani,

Masrizal,

Muti’ah

2024

SinkrOn

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This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.

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Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network

Cited by 3 publications

References 0 publications

Analisis Pendukung Keputusan Penerima Bantuan PKH Kecamatan Malo Berbasis Web Menggunakan Metode Naïve Bayes

Analisis Pendukung Keputusan Penerima Bantuan PKH Kecamatan Malo Berbasis Web Menggunakan Metode Naïve Bayes

Credit Card Fraud Detection Using Linear Discriminant Analysis (Lda), Random Forest, and Binary Logistic Regression

Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method

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