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All Journal Jurnal Teori dan Aplikasi Fisika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Nurfa Risha
Universitas Pendidikan Ganesha
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Evaluation of the performance of the Smote, Smote Enn, and Borderline Smote resampling methods based on the number of outlier data with Z Score arisgunadi gunadi; Dewi Oktofa Rahmawati; Nurfa Risha
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p05

Abstract

Handling class imbalances in datasets is a significant challenge in the classification process. Disruption occurs if the minority class has a crucial role in decision-making. Oversampling is one of the solutions that is widely used to overcome this problem. This study compares the performance of three popular oversampling methods, namely SMOTE (Synthetic Minority Oversampling Technique), SMOTE-ENN (SMOTE with Edited Nearest Neighbor), and Borderline-SMOTE, based on the number of outlier data produced. Outlier data is measured using a Z-score-based statistical approach. The research was conducted by applying the three oversampling methods on several datasets. Evaluation is carried out by counting the number of outlier data after the resample process, as well as by evaluating their impact on the performance of the classification model using metrics such as accuracy, precision, recall, and F1-score. The research results show that there is no significant difference in the number of outlier data in SMOTE, ENN SMOTE, or borderline SMOTE. In the diabetes.csv dataset, it was found that the percentage of outlier data in the initial condition and the condition after resampling with SMOTE, resampling with SMOTE ENN, and borderline SMOTE were 7.4%, 6.8%, 6.7%, and 63%, respectively. For the predict_ honor.csv dataset, the data are 7.1%, 7.3%, 7.6%, and 7%. For the winequality.csv dataset, the data are 8%, 7.8%, 6.8%, and 5.8%. Meanwhile, smoking.csv data found 7.1%, 7.3%, 7.6%, and 7.0%. However, if we look at each feature in each dataset, more varied conditions are found regarding the performance of the three algorithms, which is related to the number of outlier data produced. In terms of differences, no significant differences were found in the number of outlier data produced. The second finding is related to the performance of the decision tree classification model. It can be stated that the influence of feature correlation is more important than perfect data balance in the dataset.
Analisa Kandungan Mineral Pasir Hitam di Pantai Bali Barat Dewi Oktofa Rachmawati; Gede Aris Gunadi; Nurfa Risha
Jurnal Teori dan Aplikasi Fisika Vol. 13 No. 02 (2025): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v13i02.479

Abstract

Pasir hitam pantai Baluk Rening dan Candi Kusuma berasal dari pengikisan batuan vulkanik Gunung Merbuk, Gunung Batukaru, dan Pegunungan Seraya oleh gelombang laut Samudra Hindia. Kandungan mineral pada pasir hitam pantai ini memiliki karakteristik yang berbeda dari pasir hitam pantai lainnya. Faktor asal batuan, proses perombakan, media transportasi dan tempat pengendapan mempengaruhinya. Oleh karena itu analisa keterdapatan mineral pada pasir hitam kedua pantai ini perlu dikaji. Penelitian ini bertujuan menganalisa kandungan mineral pasir hitam di pantai Bali Barat yaitu pantai Baluk Rening dan Candi Kusuma. Kandungan mineral pasir hitam diuji dengan metode non destruktif menggunakan instrumen XRF. Sifat fisikanya ditentukan dengan prinsip rasio massa dan volume. Data dianalisis secara deskriptif kuantitatif dinyatakan dalam persentase berat (wt%). Hasil penelitian menunjukkan pasir hitam pada kedua pantai tersebut mengandung mineral hematite, rutil, quartz, corondum, zinkit, quicklime, shcherbinaite, eskolaite, dan manganosit. Jumlah mineral hematit mencapai 80,80% dengan elemen Fe sebanyak 84,50% pada pasir hitam pantai Baluk Rening dan 80,88% pada pasir hitam pantai Candi Kusuma dengan elemen Fe sebanyak 84,61%wt. Selain itu terdapat senyawa yang bukan mineral yaitu kalium oksida, P2O5, rhenium oksida, europium oksida. Ditemukan logam berat bismut, logam transisi berat renium dan elemen tanah jarang europium. Kerapatan mineral magnetik pada pasir hitam pantai Baluk Rening dan Candi Kusuma adalah 2018.90 km/m3 dan 1827.10 kg/m3.   Kata kunci: mineral magnetik, pasir hitam, x-ray flouresensi.
Co-Authors arisgunadi gunadi Dewi Oktofa Rachmawati Dewi Oktofa Rahmawati Gede Aris Gunadi