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All Journal Jurnal Inotera JSiI (Jurnal Sistem Informasi) bit-Tech Infotech: Jurnal Informatika & Teknologi Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Janitra Informatika dan Sistem Informasi JAMAIKA: Jurnal Abdi Masyarakat Abdi Jurnal Publikasi Journal of Artificial Intelligence and Digital Business Journal of Research and Publication Innovation Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat PESHUM
Jupron Jupron
Universitas Pamulang
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Pengaruh ketidakseimbangan data terhadap kinerja Algoritma Naive Bayes dalam klasifikasi stroke Muhammad Azis Sularso; Jupron Jupron
INFOTECH : Jurnal Informatika & Teknologi Vol 6 No 2 (2025): INFOTECH: Jurnal Informatika & Teknologi
Publisher : LPPMPK - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/infotech.v6i2.2075

Abstract

Stroke merupakan salah satu penyebab utama kematian dan kecacatan di dunia, sehingga diperlukan metode klasifikasi yang andal untuk mendukung deteksi dini dan pengambilan keputusan medis. Penelitian ini bertujuan untuk mengevaluasi kinerja algoritma Naive Bayes dalam klasifikasi penyakit stroke serta menganalisis dampak ketidakseimbangan data terhadap performa model. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan eksperimen komputasional menggunakan algoritma Naive Bayes. Dataset yang digunakan merupakan healthcare stroke dataset yang tersedia secara publik, terdiri dari 5.110 instance dengan delapan atribut, yaitu gender, hypertension, heart_disease, ever_married, work_type, residence_type, smoking_status, dan stroke sebagai variabel target. Proses eksperimen dilakukan menggunakan WEKA Explorer dengan dua skema evaluasi, yaitu percentage split (66%) dan 10-fold cross-validation. Hasil penelitian menunjukkan bahwa model Naive Bayes menghasilkan akurasi keseluruhan yang tinggi, yaitu di atas 94%, pada kedua skema evaluasi. Namun, performa model dalam mendeteksi kasus stroke sangat rendah, yang ditunjukkan oleh nilai true positive rate (TPR) yang kecil pada kelas stroke. Masalah utama yang ditemukan adalah ketidakseimbangan kelas yang signifikan dalam dataset, di mana kasus stroke hanya mencakup sekitar 5% dari total data. Kondisi ini menyebabkan model menjadi bias terhadap kelas mayoritas (non-stroke) dan gagal mengenali sebagian besar kasus stroke yang sebenarnya. Sebagai rekomendasi, penelitian selanjutnya disarankan untuk menerapkan teknik penanganan data tidak seimbang, seperti resampling (misalnya SMOTE), penyesuaian bobot kelas, atau penggunaan algoritma klasifikasi alternatif yang lebih robust terhadap data imbalanced, agar sensitivitas deteksi kasus stroke dapat ditingkatkan.
Enhancing Brain Tumor Prediction Accuracy through Advanced Convolutional Neural Networks: A Methodological Approach Sutrisno, Sutrisno; Jupron, Jupron
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3784

Abstract

Timely and accurate diagnosis of brain tumors remains a significant challenge in neuro-oncology due to the heterogeneous nature of tumor characteristics and their substantial impact on patient prognosis and treatment outcomes. Conventional diagnostic methods, particularly manual interpretation of medical imaging, often exhibit limited sensitivity and specificity, leading to delayed diagnoses and suboptimal clinical decisions. To address these limitations, this study proposes a tailored Convolutional Neural Network (CNN) framework that leverages hierarchical feature extraction to capture subtle spatial patterns in brain MRI images, offering advantages over traditional machine learning approaches that rely on handcrafted features. This study aims to develop and validate the proposed model to improve the accuracy and efficiency of brain tumor prediction using annotated MRI data. The dataset was systematically preprocessed, augmented, and partitioned into training and testing subsets to ensure reliable evaluation. The proposed CNN architecture introduces a streamlined feature extraction–classification pipeline designed to balance computational efficiency with discriminative capability, making it suitable for limited medical datasets. Experimental results demonstrate that the model achieves an overall classification accuracy of 86.27%, with balanced sensitivity and specificity, representing a measurable improvement over conventional diagnostic workflows and baseline approaches reported in related studies. From a clinical perspective, the model supports early detection by reducing false-negative and false-positive rates, thereby enhancing diagnostic consistency and enabling more timely clinical intervention. These findings highlight the potential of CNN-based systems as fast, accurate, and non-invasive decision-support tools, supporting the integration of artificial intelligence into medical imaging and clinical diagnostic workflows.
Co-Authors Abdul Ade Fikri Adi Irawan, Satia Agus Ammar Rianto Bagas Mahendra Putra Bahrein, Muhammad Dhela Widiastuti Fadilah, Muhamad Sahrul Hafidz Fajar Agung Nugroho Fikri Chairul Rizki Fithrotun Nisa, Aida Harahap, Rahman Haris Agam, Ibnu Harmon, Cendra Iis Istiqomah Imam Hanafi Imanuel, Kasna Indah Nur Seha Jihan Nabiylah Jusub, Muhamad Khudin, Fat Laode Made Rai Syaikah Lutfi Fadilah Putra Malika Azzahra Munadi Maulida Azzahra Mufid Ahmad Muhamad Syarif Afriansyah Muhammad Afdan Alghifari Muhammad Azis Sularso Muhammad Bahrein Muhammad Bahrein Muhammad Bimo Krisyono Muhammad Deraya Kautsar Muhammad Hafidz Diaz Alghany Muhammad Ilham Nabiel Mochammad Lathif Mendur Nadya Salwa Putriyanti Nurul Akbar Okta Irawati Pambudi, Rilo Purna, Aditya Rahmadan, Dika Surya Ria Ester Rio Saputro Rizky Raya Ananda Rizky Wahyu Saputra Rizkyanto, Achmad Santi Rahayu Santi Rahayu Saputra, Irfan Dias Sarman Sarman Sarman Sarman Sarman Shan Attar Syach SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Tegar Utomo Putra Wanto, Donna Oktar Endras Yuda Permadi Yuda Permadi Yuda Permadi, Yuda Zunanda, Obit Zurnan Alfian