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Wilda Imama Sabilla
Fakultas Vokasi Universitas Airlangga
Computer Science & IT

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Prediksi Ketepatan Waktu Lulus Mahasiswa dengan k-Nearest Neighbor dan Naïve Bayes Classifier Wilda Imama Sabilla; Tesa Eranti Putri
Jurnal Komputer Terapan  Vol. 3 No. 2 (2017): Jurnal Komputer Terapan November 2017
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.758 KB)

Abstract

Salah satu aspek pengukuran kualitas dalam evaluasi keberhasilan penyelenggaraan pendidikan tinggi adalah ketepatan lulus mahasiswa. Jumlah prosentase mahasiswa yang lulus tepat waktu menjadi indikator keberhasilan pelaksanaan proses belajar mengajar di suatu program studi. Penelitian ini menawarkan penggunaan metode penggalian data untuk memprediksi waktu lulus mahasiswa menggunakan dua metode yaitu k-Nearest Neighbour dan Naïve Bayes Classifier. Hasil dari penelitian ini berupa sistem yang dapat memprediksi ketepatan waktu lulus. Uji coba dilakukan dengan menggunakan data lulusan mahasiswa D3 Sistem Informasi Universitas Airlangga. Hasil uji coba menunjukkan bahwa metode k-Nearest Neighbor menghasilkan akurasi lebih tinggi dibandingkan dengan Naïve Bayes Classifier. Akurasi tertinggi diperoleh dengan menggunakan metode k-Nearest Neighbor yaitu sebesar 98.7%. Oleh karena itu dapat disimpulkan bahwa sistem yang dibangun pada penelitian ini mampu memprediksi ketepatan waktu lulus dengan akurasi cukup tinggi.
Implementasi SMOTE dan Under Sampling pada Imbalanced Dataset untuk Prediksi Kebangkrutan Perusahaan Wilda Imama Sabilla; Candra Bella Vista
Jurnal Komputer Terapan  Vol. 7 No. 2 (2021): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.027 KB) | DOI: 10.35143/jkt.v7i2.5027

Abstract

Company bankruptcy becomes a serious problem because it can cause economic damage and other social consequences. It’s very important to predict bankruptcy as early as possible because prediction can be useful for evaluation and planning to avoid bankruptcy. Bankruptcy prediction is one of the imbalanced classification problems because the data with the bankrupt class is far less than the non-bankrupt class. This study aims to produce a good classification model for predicting bankruptcy. Resampling used a combination of SMOTE and under sampling, is applied to the training data to produce more optimal classification model. The classification method used for prediction is multilayer perceptron and complement naïve bayes. Predictive performance was calculated using recall, ROC AUC, and PR AUC. Based on the test, using SMOTE and under sampling is quite significant in improving the classification model on the multilayer perceptron. Resampling in complement naïve bayes also increased. recall and PR AUC scores The best recall obtained was 95.45% with the complement naïve bayes method. The highest ROC AUC with resampling was also obtained using complement naïve bayes of 87.80%. Therefore, it’s concluded that bankruptcy prediction using resampling with SMOTE and under sampling, can produce good performance for detecting bankruptcy.
Co-Authors Candra Bella Vista Tesa Eranti Putri