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All Journal Telematika Sinkron : Jurnal dan Penelitian Teknik Informatika JSiI (Jurnal Sistem Informasi) Jurnal Algoritma
Ades Tikaningsih
Universitas Amikom Purwokerto
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Penerapan Algoritma XGBoost dengan SMOTE untuk Klasifikasi Kanker Payudara pada Dataset Wisconsin Adrianus Anggoro; Imam Tahyudin; Ades Tikaningsih
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3423

Abstract

Penelitian ini bertujuan untuk mengembangkan model deteksi kanker payudara menggunakan algoritma Extreme Gradient Boosting (XGBoost) pada Breast Cancer Wisconsin Diagnostic Dataset. Dataset terdiri dari 569 sampel dengan 30 fitur medis yang merepresentasikan karakteristik morfologi tumor, dengan dua kelas target yaitu benign (jinak) dan malignant (ganas). Tahapan penelitian meliputi pembersihan data, imputasi nilai hilang, normalisasi fitur, serta penerapan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk menangani ketidakseimbangan kelas. Model XGBoost dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 94,55%, dengan nilai recall kelas malignant sebesar 95,24%, yang mengindikasikan kemampuan tinggi dalam mendeteksi kanker ganas. Confusion matrix menunjukkan hanya 2 kasus false negative, menandakan sensitivitas model yang sangat baik terhadap kelas minoritas. Dibandingkan dengan model tanpa SMOTE, penerapan SMOTE terbukti meningkatkan recall pada kelas malignant secara signifikan. Hasil penelitian ini menunjukkan bahwa algoritma XGBoost dengan penanganan imbalance class efektif digunakan sebagai sistem pendukung keputusan dalam diagnosis kanker payudara dan berpotensi membantu deteksi dini secara lebih akurat.
Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes Puji Lestari; Imam Tahyudin; Ades Tikaningsih; Ade Nurhopipah
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2817

Abstract

Stroke is a critical health issue in Indonesia, contributing to high mortality rates. At Banyumas District Hospital, stroke is the fourth most common condition, presenting significant challenges in both clinical care and financial management. The purpose of this study is to enhance the quality of services and optimize treatment costs for stroke patients by developing a predictive model using the XGBoost algorithm. This study employs the XGBoost algorithm to develop predictive models, which are then implemented within a web-based machine learning application using the Paython Flask framework. The models predict patient mortality and hospitalization duration. The results indicate that the XGBoost algorithm predicts patient mortality with 86% accuracy and hospitalization duration with 82% accuracy. The developed application significantly enhances care quality and resource management at Banyumas District Hospital by providing accurate predictions to support healthcare decision-making. The use of this application can significantly improve the management of stroke patient care at Banyumas District Hospital, thus maximizing service quality and optimizing treatment costs. By integrating accurate predictive modeling into healthcare decision-making processes, the application facilitates more effective allocation of resources and timely medical interventions, ultimately contributing to better patient outcomes.
Klasifikasi Edibilitas Jamur Secara Otomatis Menggunakan  Algoritma Random Forest Berbasis Morfologi Muhammad Reza Pahlevi; Imam Tahyudin; Ades Tikaningsih
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10868

Abstract

Jamur merupakan organisme yang memiliki keragaman morfologi yang tinggi, namun beberapa jenis di antaranya bersifat beracun dan membahayakan jika dikonsumsi. Kesamaan ciri fisik antara jamur yang dapat dimakan dan yang beracun sering kali menyulitkan proses identifikasi secara manual. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis berbasis algoritma Random Forest untuk membedakan jamur edible dan poisonous, memanfaatkan seluruh 21 atribut (18 kategorikal, 3 numerik) dari dataset komprehensif Kaggle (61.069 entri). Metodologi penelitian mengikuti alur CRISP-DM yang dimodifikasi, dimulai dari pengumpulan data hingga implementasi. Tahap pra-pemrosesan data krusial dilakukan secara ekstensif, meliputi penanganan duplikasi data dan imputasi missing value (menggunakan median dan modus). Selanjutnya, transformasi label kelas (edible=0, poisonous=1) dan One-Hot Encoding diterapkan pada fitur kategorikal untuk representasi numerik yang tepat. Fitur numerik seperti cap-diameter dan stem-height dinormalisasi menggunakan Standard Scaling untuk menyeimbangkan kontribusi. Data kemudian dibagi 80:20 untuk pelatihan dan pengujian. Model Random Forest dikembangkan dengan parameter optimal (n_estimators=200, max_depth=15, class_weight="balanced") untuk efisiensi dan robustabilitas terhadap ketidakseimbangan kelas. Hasil evaluasi menunjukkan performa sangat baik dengan akurasi keseluruhan 99,34%, serta nilai precision, recall, dan f1-score yang seimbang pada 0.99 untuk kedua kelas. Analisis feature importance mengidentifikasi stem-width, stem-height, dan cap-diameter sebagai atribut paling berpengaruh. Learning curve menunjukkan stabilitas model tanpa overfitting. Implementasi pada sampel jamur baru juga mengkonfirmasi kemampuan prediksi yang konsisten, menjadikan model ini layak sebagai sistem pendukung keputusan otomatis dalam deteksi jamur beracun.
Two-Stage Framework Using IndoBERT for Sentiment Analysis of Tokopedia Reviews under Extreme Class Imbalance Ades Tikaningsih; Imam Tahyudin; Berlilana
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16187

Abstract

The rapid growth of the Indonesian e-commerce industry has generated a large volume of customer reviews for sentiment analysis, but the data distribution often suffers from extreme class imbalance. The review dataset exhibits a 97.6% dominance of the positive class, causing the single-stage transformer model to produce high accuracy that does not fully represent classification capability. The baseline model achieves a macro-averaged F1-score of 0.599, with a neutral-class recall of 26.3%. Approaches based on loss function adjustment, such as class-balanced loss, focal loss, weighted cross-entropy, and decision-threshold adjustment, are unable to fundamentally address this issue, yielding only limited performance improvements. This study proposes a two-stage classification approach that decomposes the multi-class classification task into two sequential binary classification stages using a BERT-based Indonesian-language transformer model (IndoBERT). The first stage separates the positive class from the non-positive class, while the second stage distinguishes between the neutral and negative classes in a more balanced decision space. The proposed approach achieves a macro-averaged F1-score of 0.761, representing a 16.2% improvement over the baseline and outperforming all loss-function-based methods. These findings suggest that, under conditions of extreme class imbalance, simplifying the decision space through gradual task decomposition is more effective than intervention at the loss-function level. Furthermore, error propagation analysis and qualitative evaluations demonstrate that this approach improves sensitivity to minority classes, although challenges remain in cases involving ambiguous expressions.
Co-Authors Adrianus Anggoro Berlilana Imam Tahyudin Imam Tahyudin Imam Tahyudin Muhammad Reza Pahlevi Nurhopipah, Ade Puji Lestari