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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 Jurnal Armada Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer JAMAIKA: Jurnal Abdi Masyarakat Abdi Jurnal Publikasi Journal of Artificial Intelligence and Digital Business Journal of Research and Publication Innovation Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Ilmiah Multidisiplin Nusantara Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Indonesian Journal of Innovation Multidisipliner Research PESHUM
Jupron Jupron
Universitas Pamulang
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Analisa Klasifikasi Penyakit Diabetes Dengan Algoritma Neural Network Sutrisno Sutrisno; Jupron
bit-Tech Vol. 6 No. 3 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i3.1161

Abstract

Metode yang populer dan efektif untuk mengidentifikasi dan klasifikasi diabetes adalah algoritma deep learning untuk klasifikasi dataset diabetes. Algoritma deep learning, terutama jaringan saraf tiruan juga dikenal sebagai neural networks telah terbukti sangat efektif dalam menangani tugas klasifikasi data medis, seperti diabetes. Dalam penelitian sebelumnya, algoritma neural network digunakan untuk mengklasifikasikan penyakit diabetes , tetapi nilai akurasinya masih di bawah 80,5%. Karena nilai akurasi masih kurang maksimal, penelitian ini bertujuan untuk meningkatkannya. Penggunaan metode pemrosesan yang lebih akurat, fine tuning hyperparameter, untuk memastikan data sudah normal pada setiap fitur yaitu dengan metode normalisasi standard, kemudian menambahkan hiden layer sebanyak 2 layer dengan harapan mempelajari klasifikas yang tidak bisa dipisahkan secara linier. Dalam penelitian ini, beberapa langkah pembaharuan dilakukan selain besaran hiden layer juga besaran test size. Pembaruan ini bertujuan untuk meningkatkan akurasi yang lebih besar, serta hasil yang lebih baik untuk presisi, recall dan F1. Artikel ini menggunakan data umum atau sekunder dari laman Kaggle. Penelitian ini dilakukan untuk meningkatkan pengetahuan tentang cara mencegah diabetes. Gejala penyakit ini termasuk kadar gula sewaktu lebih dari 200 mg/dl dan kadar gula puasa lebih dari 126.mg/dl, antara tahun 1998 dan 2014, Badan Kesehatan Dunia melaporkan peningkatan dramatis dalam jumlah kasus diabetes di seluruh dunia, dari 108 juta menjadi 422 juta.
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 Achmad Rizkyanto Ade Fikri Aditya Eko Nugroho Aditya Purna Adnan Kahfi Bara Affrizaa Primaliandra Agus Ammar Rianto Ahmad Khoerul Riski Aji Santoso Almas Zein Saputra Alpin Lubis Ariansa Dos Santos Bagas Mahendra Putra Bahrein, Muhammad Danis Wara Nugraha Dewi Andrayani Dhela Widiastuti Dipa Dyah Puspita Arum Dipo Arya Mukti Erland Radhitya Putra Priono Fadilah, Muhamad Sahrul Hafidz Faiz Fadillah Fajar Agung Nugroho Farid Mardan Aziz Fikri Chairul Rizki Fithrotun Nisa, Aida Galih Aji Prasetyo Hamzah Fachrudin M Harmon, Cendra Ibnu Haris Agam Iis Istiqomah Ika Radistia Valen Ilham Pramudia Ilham Syahgani Imaddudin Ghozali Imam Hanafi Indah Nur Seha Jihan Nabiylah Jonatan Situmorang Kasna Imanuel Khristofer Dalope Khudin, Fat Laode Made Rai Syaikah Lintan Zhuliani Lintar Yan Zuhara Lucky Yan Zuhara Lutfi Fadilah Putra Ma'ruf Nizar Fazari Malika Azzahra Munadi Martan Budi Rifaldi Maulida Azzahra Misbachuddin Mizanul Umam Moh Mahmud Yunus Mufid Ahmad Muhamad Jusub Muhamad Pahri Muhamad Syarif Afriansyah Muhammad Afdan Alghifari Muhammad Arya Mifza Muhammad Aufa Azmi Muhammad Azis Sularso Muhammad Bahrein Muhammad Bahrein Muhammad Bimo Krisyono Muhammad Deraya Kautsar Muhammad Fiqri Muhammad Hafidz Diaz Alghany Muhammad Ilham Muhammad Syafiq Aqil Mutiara Handayani Ujianti Nabiel Mochammad Lathif Mendur Nadya Salwa Putriyanti Najmi Cipta Nugraha Nover Efridho Nurul Akbar Obit Zunanda Okta Irawati Rahmadan, Dika Surya Rahman Harahap Refliani Marsela Rendy Wijaya Ria Ester Rilo Pambudi Rio Permana Mardianto Rio Saputro Rizky Raya Ananda Rizky Wahyu Saputra Santi Rahayu Santi Rahayu Saputra, Irfan Dias Sarman Sarman Sarman Sarman Sarman Satia Adi Irawan Shabrina Thufailah Shan Attar Syach SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Tegar Utomo Putra Violla Cantika Wahyu putra Ramadhan Wanto, Donna Oktar Endras Yodi Cahyo Yuda Permadi Yuda Permadi Yuda Permadi, Yuda Yusuf Hidayat Zaki Arfa Mustafa Zurnan Alfian