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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Buletin Sistem Informasi dan Teknologi Islam
Lutviana, Lutviana
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering

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Perancangan Sistem Informasi Akademik Sekolah Dasar Dengan Metode Waterfall Berbasis Website Lutviana, Lutviana; Arfianto, Irfan; Rohman, Taufik Fadhil; Sumantri, R. Bagus Bambang; Suryani, Riska
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 4, No 1 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i1.1550

Abstract

Sekolah menjadi sebuah sistem yang terdiri dari komponen-komponen yang saling berkaitan dan saling memengaruhi satu sama lain untuk mencapai suatu tujuan. Dalam pencapaian tujuannya sekolah banyak melakukan pengolahan data yang cukup kompleks dan dinamis. Pengolahan data akademik sekolah secara manual masih belum efektif karena masih banyak kesalahan-kesalahan yang terjadi. Hasil perancangan Sistem Informasi Akademik menjadi solusi untuk membantu pengolahan data menjadi lebih efektif. Metode yang digunakan dalam perancangan sistem ini adalah salah satu metode SDLC, yaitu metode waterfall yang terdiri dari beberapa tahapan yaitu analisis, desain, implementasi, pengujian, dan pemeliharaan. Dalam tahap desain dibuat diagram ERD dan DFD. Dan digunakan pengujian Black-Box untuk pengujian perancangan sistem ini.
CNN-based Classification of Bladder Tissue Lesions from Endoscopy Images Lutviana, Lutviana; Rian Ardianto; Purwono
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.17867

Abstract

Bladder cancer is one type of tumor that frequently occurs in the urinary system, and early diagnosis is essential to improve the prognosis and survival of patients. The study aims to develop a Convolutional Neural Network (CNN) model for bladder tissue lesion classification from endoscopic images. This study uses a dataset consisting of 1754 images, which are divided into four classes: High-Grade Cancer (HGC), Low-Grade Cancer (LGC), Non-Specific Tissue (NST), and Non-Tumorous Lesion (NTL). The proposed CNN model showed a validation accuracy of 96.29%, with high recall, precision, and F1-score in most classes. The results show that CNN-based automated methods can improve efficiency and accuracy in the early diagnosis of bladder cancer, reduce manual visual interpretation errors, and improve the quality of patient care. This study suggests increasing the training data, especially for the NTL class, and applying more complex model architecture to better results.
Pendekatan Transfer Learning dan SMOTE untuk Klasifikasi Kanker Kulit pada Imbalanced Dataset Lutviana, Lutviana; Purwono, Purwono; Imam Ahmad Ashari
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Skin cancer is one of the most commonly diagnosed cancers worldwide, with the incidence increasing every year. While early detection is a key factor in reducing skin cancer mortality, conventional methods such as biopsy have limitations in terms of cost and invasiveness. This research applies a deep learning based approach for skin cancer classification with Convolutional Neural Networks (CNN) model using transfer learning method. 3 CNN architectures namely MobileNetV2, EfficientNetB0, and DenseNet121 are used to evaluate the performance of the model in detecting skin cancer. One of the main challenges in this research is the imbalanced dataset, which can cause bias in classification. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to improve the representation of minority classes. The dataset used comes from Kaggle and consists of 2,357 images classified into 9 skin cancer categories. The results show that the transfer learning method combined with SMOTE can significantly improve the accuracy of the model, especially in detecting classes with a smaller number of samples. The evaluation was conducted using accuracy, precision, recall, and f1-score metrics. This research is expected to contribute to the development of an artificial intelligence-based skin cancer detection system that is more accurate, efficient, and can be used as a tool for medical personnel in early diagnosis of skin cancer.
Co-Authors Arfianto, Irfan Bambang Sumantri, R Bagus Imam Ahmad Ashari Purwono Purwono Purwono, Purwono Rian Ardianto Rohman, Taufik Fadhil Suryani, Riska