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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Krea-TIF: Jurnal Teknik Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Responsif : Riset Sains dan Informatika Jurnal Pengabdian Masyarakat Inovatif Jurnal Pengabdian Masyarakat Inovatif
Gibtha Fitri Laxmi
Universitas Ibn Khaldun Bogor
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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KLASIFIKASI PENYAKIT KULIT WAJAH MENGGUNAKAN METODE TRANSFER LEARNING DENGAN ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK Raihan Harits Fadhillah; Foni Agus Setiawan; Gibtha Fitri Laxmi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 2 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i2.7722

Abstract

Penyakit kulit wajah merupakan masalah kesehatan yang umum dan dapat secara signifikan memengaruhi kualitas hidup penderitanya. Data menunjukkan bahwa jumlah kasus penyakit kulit yang mening-kat memerlukan perhatian lebih dari masyarakat dan tenaga medis. Penelitian ini bertujuan untuk mengetahui model klasifikasi penyakit kulit wajah dengan tingkat akurasi terbaik. Model yang digunakan adalah metode transfer learning dengan arsitektur Convolutional Neu-ral Network (CNN), yaitu VGG16, ResNet152V2, dan DenseNet201. Dataset yang digunakan dalam penelitian ini terdiri dari 895 citra dengan lima jenis penyakit kulit wajah: Blackhead, Eksim, Jerawat, Rosacea, dan Tinea Fasialis. Hasil penelitian menunjukkan bahwa model DenseNet201 mencapai performa terbaik dengan akurasi sebe-sar 88%, serta nilai precision, recall, dan F1-Score masing-masing sebesar 0,87, 0,88, dan 0,87. Meskipun model ini menunjukkan hasil yang memuaskan, tantangan masih ada dalam membedakan kelas penyakit yang memiliki kemiripan visual. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem deteksi penyakit kulit wajah yang lebih akurat, sehingga meningkatkan kesadaran dan penanganan penyakit kulit di masyarakat.
Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections Setiawansyah Setiawansyah; Sitna Hajar Hadad; Ahmad Ari Aldino; Pritasari Palupiningsih; Gibtha Fitri Laxmi; Dyah Ayu Megawaty
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7713

Abstract

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.
Co-Authors Abidin, Riswan Agustian H, Sahid Ahmad Ari Aldino Aljauhari, Muhyiddien Rabbani Anbi, Moningka Naca Apid Robini Eka Prawira Br. Ginting, Novita Budi Susetyo Catur Hermanto Darmawan, Supri Zaki Dewi Primasari Dyah Ayu Megawaty Erwin Hermawan Farha Fitrahul Janah Fety Fatimah Firly Djulyansyah, Moechammad Fitrah Satrya Fajar Kusumah Foni Agus Setiawan Freza Riana Hadjimartsu, Sahid Agustian Hendriawan Nur, Wawan Iksal Yanuarsyah Jejen Jaenudin Kamali, Alviyan Badro Kudang Boro Seminar Lievia Anjhelina Maharani Muhammad Zikri Novita Br Ginting Nurul Kamilah Palupiningsih, Pritasari Panca Jarot Santoso Puspa Eosina Putra Nugraha, Raka Raihan Harits Fadhillah RR. Ella Evrita Hestiandari Saepulloh, Irwan Sahid Agustian Hudjimartsu Setiawan, Foni Agus Setiawansyah Setiawansyah Sitna Hajar Hadad Wulandari, Berlina Yandra Arkeman Yuggo Afrianto