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p-Index From 2021 - 2026
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All Journal Telematika TEKNOSIA
Wahyu Dwi Prasetio
Universitas Bengkulu
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Engineering Transportation

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IMPLEMENTASI FUZZY TAHANI DALAM PENENTUAN PEMBELIAN SMARTPHONE Wahyu Dwi Prasetio; Astuti Zahrroh; Abdul Khudri Barkah Rizki; Achmad Rilwanul Izzati; Danny Brantadikara; Widhia Oktoeberza KZ
Teknosia Vol. 16 No. 2 (2022): Desember 2022
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/teknosia.v16i2.25990

Abstract

In 2021, 65.87% of the Indonesian population had a mobile phone. The smartphone market in Indonesia is expected to increase by 8% from the previous year to 44 million units in 2022. Users must consider various factors such as price, specifications, and others before deciding to buy a smartphone. In an effort to develop a smartphone selection support system, research uses the Fuzzy Tahani method. The Fuzzy inference system model Tahani will process the input provided by the user and then return the processed result as an output list of smartphone recommendations to the user sorted by fire strength value. As a final result, the fuzzy logic Tahani model can be used to provide accurate smartphone selection recommendations by considering various criteria proportionally.
CNN-Based for Skin Cancer Classification with Dull Razor Filtering and SMOTE Widhia Oktoeberza KZ; Wahyu Dwi Prasetio; Adam Idham Ramadhan; Adde Nanda C. Putra; Firsti Eliora; Afdhal Kurniawan Mainil; Agus Susanto
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.v18i1.3048

Abstract

Skin cancer is usually diagnosed by dermatologists through biopsy, which can be a time-consuming process due to limited resources. Early detection of skin cancer can increase the survival rate to over 99%, but if it's detected late, the rate drops to around 14%. This finding highlights the need for a rapid and accurate computing system for early cancer detection, which can prevent severe consequences. The purpose of this study is to classify skin cancer images into benign and malignant classes based on their nature. To facilitate the classification of CNN-based skin cancer, this research employs dull razor filtering. Additionally, the SMOTE method handles the unbalanced dataset. The classification results indicate that the proposed approach has an accuracy of 88.54%, a precision of 88%, and a sensitivity of 88%. These findings suggest that CNN-based methods can aid dermatologists in the diagnosis of skin cancer.
Co-Authors Abdul Khudri Barkah Rizki Achmad Rilwanul Izzati Adam Idham Ramadhan Adde Nanda C. Putra Afdhal Kurniawan Mainil Agus Susanto Astuti Zahrroh Danny Brantadikara Firsti Eliora Widhia Oktoeberza KZ Widhia Oktoeberza KZ