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All Journal Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Inovasi Penelitian
Laila Qadrini
Universitas Sulawesi Barat, Majene
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Decision Tree dan Adaboost pada Klasifikasi Penerima Program Bantuan Sosial Laila Qadrini; Andi Seppewali; Asra Aina
Jurnal Inovasi Penelitian Vol 2 No 7: Desember 2021
Publisher : Sekolah Tinggi Pariwisata Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47492/jip.v2i7.1046

Abstract

Kemiskinan adalah masalah sosial yang masih belum terselesaikan di negara berkembang khususnya di indonesia. Kemiskinan telah hadir dalam realitas kehidupan manusia dengan bentuk dan kondisi yang sangat memprihatinkan, Karena kemiskinan memang tidak bisa dihilangkan begitu saja. Dengan adanya permasalahan terhadap Negara berkembang terutama kemiskinan. Maka pemerintah membuat kebijakan-kebijakan atau program-program untuk memberantas masalah tersebut. Diantaranya adalah Bantuan langsung tunai atau biasa disebut BLT. Bantuan Langsung Tunai (BLT) dapat dipahami sebagai pemberian sejumlah uang (dana tunai) kepada masyarakat miskin setelah pemerintah memutuskan untuk menaikkan harga BBM dengan jalan mengurangi subsidi namun selisih dari subsidi itu diberikan kepada masyarakat miskin. Melihat dari program pemerintah tersebut, upaya pemberantasan kemiskinan di negara Indonesia ini cukup menarik simpati masyarakat. Hal ini menjadi salah satu objek yang menarik untuk diteliti dan dikaji lebih lanjut. Untuk menentukan klasifikasi tingkat penduduk miskin terdapat banyak metode yang dapat digunakan. Salah satunya yang digunakan pada penelitian ini yaitu Decision Tree dan Adaboost.
Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017 Laila Qadrini; Hikmah Hikmah; Megasari Megasari
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2154

Abstract

Bidikmisi is tuition assistance from the government for high school graduates (SMA) or equivalent who have good academic potential but have economic limitations. Different from scholarships that focus on providing awards or financial support to those who excel. The achievement requirements for Bidikmisi are aimed at ensuring that Bidikmisi recipients are selected from those who truly have the potential and willingness to complete higher education. Given that the recipients of this bidikmisi must really be the right person, in this study a classification of the recipients of the 2017 bidikmisi in East Java will be carried out, in this study there is data that is not balanced the "Accepted" class is more than the "Not accepted" class. If the data is not balanced, almost all classification algorithms will produce much higher accuracy for the majority class than for the minority class. Researchers will handle class imbalances. The resampling technique used in research related to the prediction of bidikmisi recipients includes resampling techniques, namely Oversampling, Undersampling and SMOTE using two classification methods, namely SVM and Random Forest. The Oversampling technique was chosen because it does not reduce the amount of data but adds to the dataset that is lacking in the minority class. The Oversampling algorithm used is Synthetic Minority Over-sampling Technique (SMOTE), this algorithm was chosen from several resampling algorithms because SMOTE produces good accuracy and is effective in dealing with unbalanced classes because it reduces overfitting.
Undersampling dan K-Fold Random Forest Untuk Klasifikasi Kelas Tidak Seimbang Laila Qadrini
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3141

Abstract

Classification in Data Mining is a process of modelling that explains and differentiates data classes intending to estimate the class of an object whose class is unknown. Classification can be applied in various aspects so over time quite a lot of classification algorithms have been developed, but some problems are often encountered in classification, namely the problem of data imbalance. An imbalanced class is a condition where there are several data where the number of classes is not balanced or there is a significant difference in each number of classes. Most classification datasets do not have the same number of classes. However, the class imbalance is not a problem when the comparison between classes is not much different. Class imbalance can cause problems if left untreated because the resulting model predictions will tend to the majority group so that the contribution of the minority class to the model is small. One of the algorithms that are often used to handle unbalanced classes is the resampling algorithm. The purpose of this research is to apply the Resampling Undersampling Random Forest and Random Forest K-Fold Undersampling Algorithms to the Breast Cancer Diagnostic dataset from UCI Machine Learning. Undersampling was chosen because it produces better accuracy than oversampling. Recall accuracy for the K-Fold 10 Random Forest Algorithm is 83% and for Recall Undersampling Random Forest is 65%.
The Utilization of Resampling Techniques and the Random Forest Method in Data Classification Ciciana Ciciana; Rahmawati Rahmawati; Laila Qadrini
TIN: Terapan Informatika Nusantara Vol 4 No 4 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i4.4342

Abstract

In data classification, there are various methods that can be employed, one of which is the random forest method. This method proves effective in handling non-linear data, exhibiting robustness against extreme data points and disturbances, and providing ease of use that results in high-quality classification outcomes. Data imbalance, where one class has more or fewer instances than the others, is a common issue. In situations of data imbalance, most classification models tend to favor the majority class, which can lead to overfitting and unsatisfactory classification results. To address this issue, resampling techniques can be applied. One such resampling technique is SMOTE, specifically an oversampling method that augments the minority class by generating synthetic data points. This research aims to evaluate the accuracy of data classification using the random forest method and assess the impact of resampling and random forest on classification. The data used in this study includes simulated breast cancer data and real-world patient data from LBW Puskesmas Banggae I Kabupaten Majene. The analysis results indicate an accuracy rate of 94.74%, a sensitivity of 93.33%, and an F1-Score of 95.89% for breast cancer data. Meanwhile, the accuracy for LBW data reached 73.75%, with a sensitivity of 77.63%, and an F1-Score of 84.89%.
Co-Authors Andi Seppewali Asra Aina Ciciana Ciciana Hikmah Hikmah Megasari Megasari Rahmawati Rahmawati