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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Keperawatan Respati Yogyakarta
Lutviana Lutviana
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Computer Science & IT Economics, Econometrics & Finance Health Professions Nursing

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HUBUNGAN MEKANISME KOPING DENGAN SUBJECTIVE WELL-BEING PADA LANSIA DI PSTW YOGYAKARTA UNIT BUDI LUHUR Lutviana Lutviana; Edi Sampurno; Mulyanti Mulyanti
Jurnal Keperawatan Respati Yogyakarta Vol 4 No 2 (2017): MAY 2017
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jkry.v4i2.88

Abstract

Setiap individu akan mengalami proses menjadi tua dalam tahapan hidupnya dan akan menghadapi perubahan- perubahan yang erat kaitannya menjadi sumber stres serta dapat menimbulkan depresi yang mempengaruhi mekanisme koping dan subjective well-being. Tujuan dari penelitian ini yaitu untuk mengetahui hubungan mekanisme koping dengan subjective well-being pada Lansia di PSTW Yogyakarta Unit Budi Luhur. Jenis penelitian ini deskriptif kuantitatif menggunakan rancangan crosssectional, dengan jumlah sampel sebanyak 42 responden lansia di PSTW Yogyakarta Unit Budi Luhur yang diperoleh secara total sampling. Penelitian ini menggunakan uji statistik kendall tau. Hasil penelitian ini menunjukan responden yang menggunakan mekanisme koping maladaptif sebesar 61,9% dan 66,7%  memiliki tingkat subjective will-being sedang menunjukan nilaip value 0,076, yang artinya tidak ada hubungan yang signifikan antara mekanisme koping dengan subjective well- being pada lansia di PSTW Yogyakarta Unit Budi Luhur
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 | DOI: 10.46880/jmika.Vol9No2.pp323-331

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 Imam Ahmad Ashari, Imam Ahmad Mulyanti Mulyanti Purwono, Purwono Sampurno, Edi