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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Applied Data Sciences Jurnal Teknisi
Muhammad Ardiansyah Sembiring
Universitas Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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PERFORMANCE OF ROBUST SUPPORT VECTOR MACHINE CLASSIFICATION MODEL ON BALANCED, IMBALANCED AND OUTLIERS DATASETS Muhammad Ardiansyah Sembiring; Herman Saputra; Riki Andri Yusda; Sutarman Sutarman; Erna Budhiarti Nababan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5272

Abstract

In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development
ANALISIS FAKTOR PREDIKSI DIAGNOSA TINGKAT SERANGAN JANTUNG MENGGUNAKAN METODE REGRESSION Muhammad Ardiansyah Sembiring
JURNAL TEKNISI Vol. 4 No. 1 (2024): February 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v4i1.1800

Abstract

Abstract: Heart disease, which is also known as cardiovascular, is a variety of conditions where there is narrowing or blockage of the blood vessels which can cause heart attacks, chest pain, stroke. Heart disease can occur in anyone of any age, gender, occupation, and lifestyle. In addition, heart disease cannot be cured. This condition requires careful treatment and monitoring throughout life. When this treatment fails, sufferers have to undergo surgical operations which are quite expensive and complicated. A report from WHO in September 2009 stated that this disease was the first cause of death to date. In 2004, an estimated 17.1 million people died from heart disease. This figure represents 29% of global causes of death, with details of 7.2 million people dying from heart disease and 5.7 million people dying from stroke. This can be prevented by reducing risk factors. The role of information technology can be realized by data retrieval techniques. research to shorten the time and selection of factors for early detection of heart attacks.Keywords: heart; regression; information Technology  Abstrak: Penyakit jantung atau heart disease , yang juga dikenal dengan istilah kardiovaskuler adalah berbagai kondisi dimana terjadi penyempitan atau penyumbataan pembuluh darah yang dapat menyebabkan serangan jantung ,nyeri dada,stroke, Penyakit jantung dapat terjadi oleh siapapun disegala usia , jenis kelamin, pekerjaan , dan gaya hidup. Selain itu penyakit jantung tidak dapat disembuhkan .Kondisi ini membutuhkan pengobataan dan pemantauan hati-hati sepanjang hidupnya. Ketika pengobataan ini gagal , penderita harus melakukan operasi bedah yang cukup mahal dan rumit . Laporan dari WHO September 2009, menyebutkan bahwa penyakit tersebut merupakan penyebab kematian pertama sampai saat ini. Pada 2004, diperkirakan 17,1 juta orang meninggal karena Penyakit Jntung . Angka ini merupakan 29% dari penyebab kematian global, dengan perincian 7,2 juta meninggal karena penyakit jantung dan 5,7 juta orang meninggal karena stroke.Hal inni dapat dicegah dengan menggurangi factor- factor resiko .Peran teknologi informasi dapat diwujudkan dengan teknik mencari data riseet untuk mempersingkat waktu dan pemilihan fakto-faktor pendeteksi dini serangan jantung .Kata kunci: jantung; regresi; teknologi informasi
A Fibonacci-Based Dynamic Neural Network Architecture for Imbalanced Classification of Higher Education Performance Muhammad Ardiansyah Sembiring; Syahril Efendi; Mohammad Andri Budiman; T.Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1459

Abstract

This study aims to address two problems in neural network design: the lack of a principled method for choosing hidden-layer neuron counts, and the reliability loss caused by severe class imbalance. To meet this objective, the study propose the Fibonacci Dynamic Neural Network, contributing, to our knowledge, the first architecture unifying number-theoretic hidden-layer sizing with imbalanced-learning correction, an unbiased selection property proven formally, and cross-domain validation. Conceptually, candidate hidden-layer widths are generated from the Fibonacci sequence: because consecutive terms grow proportionally toward the Golden Ratio, the resulting set spans a non-redundant, well-distributed range of capacities, unlike arbitrary sets from conventional methods. One candidate is drawn per layer via a discrete uniform distribution, giving every option equal selection probability, while class imbalance is corrected through synthetic minority oversampling applied only to training data. The architecture was evaluated experimentally on three benchmark datasets spanning higher education, credit card fraud, and charitable donation prediction, with imbalance ratios from about eighteen-to-one to nearly five hundred eighty-to-one, tested across five seeds against five baselines and an ablation study via the Wilcoxon signed-rank test. It achieved macro F1-scores of approximately 0.83, 0.94, and 0.81, improvements of sixteen, five, and sixteen percent over the strongest baseline, with the smallest training-test performance gap of any model tested; oversampling and Fibonacci spacing contributed most to these gains. These findings suggest number-theoretic structures offer a practical, reproducible basis for architecture design under class imbalance, useful for future work on automated range selection and for deploying reliable classifiers in imbalanced real-world settings.
Co-Authors Efendi, Syahril Erna Budhiarti Nababan Herman Saputra Mohammad Andri Budiman Riki Andri Yusda Sutarman Sutarman T.Henny Febriana Harumy