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All Journal TEKNIK INFORMATIKA Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS KOMPUTEK Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Generation Journal Jurnal Teknika JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Berdaya Mandiri Jurnal Teknik Informatika (JUTIF) Joutica : Journal of Informatic Unisla INFORMATIKA Jurnal Ilmiah Edutic : Pendidikan dan Informatika Journal of Artificial Intelligence and Digital Business Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Miftahus Sholihin
Universitas Islam Lamongan
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Pengembangan Aplikasi Web R Shiny untuk Visualisasi Interaktif Data Klinis Diabetes Sholihin, Miftahus; Sari, Putri Dina; Mahuda, Isnaini; Rahman, Arief; Asmaraningtyas, Kinanthi Trah; Pertiwi, Dinda Dwi Anugrah; Delano, M. Fabian Reinhard
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3807

Abstract

Peningkatan jumlah kasus diabetes dari tahun ke tahun menunjukkan perlunya pendekatan analitik yang lebih komprehensif untuk memahami berbagai faktor klinis yang berkontribusi terhadap timbulnya penyakit ini. Diabetes merupakan gangguan metabolik yang melibatkan sejumlah indikator yang saling berkaitan, seperti kadar glukosa, insulin, tekanan darah, ketebalan kulit, indeks massa tubuh, dan kecenderungan genetik. Kompleksitas hubungan antar variabel tersebut menuntut adanya alat analisis yang mampu menyajikan informasi secara lebih interaktif dan mudah dipahami. Penelitian ini mengembangkan aplikasi web berbasis R Shiny yang dirancang untuk menyajikan visualisasi data klinis diabetes secara dinamis. Aplikasi ini bertujuan untuk membantu pengguna memahami pola dan tren dalam data diabetes, sehingga mendukung pengambilan keputusan yang lebih baik dalam manajemen kesehatan. Selain itu, aplikasi ini juga memfasilitasi pengguna dalam mengeksplorasi pola distribusi dan hubungan antarfaktor melalui beragam jenis visualisasi, termasuk radar chart, heatmap, histogram, scatter plot, dan boxplot. Seluruh fitur dikembangkan untuk mendukung analisis eksploratif yang fleksibel, di mana pengguna dapat memilih sendiri variabel X dan Y guna menilai keterkaitan antar indikator klinis. Data yang digunakan bersumber dari Kaggle dan mencakup informasi diagnostik pasien perempuan keturunan Pima Indian berusia minimal 21 tahun. Hasil eksplorasi visual menunjukkan bahwa glukosa memiliki pengaruh paling kuat terhadap status diabetes, sementara BMI dan insulin juga muncul sebagai indikator penting lainnya. Aplikasi ini berkontribusi sebagai media eksplorasi data yang responsif, mudah diakses, serta potensial digunakan dalam penelitian, pembelajaran, maupun pengambilan keputusan awal terkait kesehatan masyarakat.
Implementation of ResNet50 Based on Transfer Learning for Sugarcane Leaf Disease Detection Ulum, M. Miftah Fatkhul; Sholihin, Miftahus; Mustain, Mustain
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31873

Abstract

Sugarcane (Saccharum officinarum) is a vital commodity in Indonesia’s sugar industry and is highly susceptible to leaf diseases such as Mosaic, RedRot, Rust, and Yellow, which significantly reduce yield quality and quantity. This study proposes an automatic disease classification system using the ResNet50 architecture with a transfer learning approach, offering a more systematic evaluation compared to previous studies that typically tested only a single configuration or focused on other crops. The dataset consists of 3,250 RGB images across five classes after preprocessing and augmentation to address class imbalance. Eight model configurations were evaluated by combining epoch values (20, 40) and learning rates (0.0001, 0.001, 0.01, 0.1). The best performance was achieved by the configuration with 20 epochs and a learning rate of 0.0001, producing an accuracy and F1-score of 97%. The model was further deployed into a Flask-based web application to demonstrate practical usability. However, this study is limited by the use of a single controlled dataset, so model performance may vary under real-field conditions such as different lighting, camera angles, and leaf damage severity. Future research should include field data evaluation to strengthen model generalization.
Co-Authors Abdul Kholiq Abdul Kholiq Ahmad Fauzi Hendratmoko Alfarisi, Muhammad Nur Fikri AlMuhibbi, Muhammad Rayendra Anam, M. Khairul Ansori, Yulian Arief Rahman Arief Rahman Arshad, Mohamad Syafwan Asmaraningtyas, Kinanthi Trah Asshiddieqie, Rafi Ramadhan Atia Sonda Aulia Ikhsan Azizah, Luluk Nur Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Bagus Nur Bakti Aji Bagus Nur Bakti Aji bin MD. Fudzee, Mohd Farhan Cindy Suryanti Darnis, Febriyanti Delano, M. Fabian Reinhard Dinar Mahdalena Leksana 1 Erna Hayati Erna Hayati, Erna Erry Anggraini ERRY ANGGRAINI Farizki, Achmad Nurasel FATHARANI, ATIKA Fatkhul U, M. Miftah Febriyanti Darnis Firdaus, Muhammad Alvin Fudzee, Mohd Farhan Md Gusman, Taufik Hamid, Rahayu A Ismail, Mohd Norasri Izz, Aiz Ahmad Fa’iz Dliya’ul KIKI SEPTARIA Lilik Anifah M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN Mahuda, Isnaini Masruroh MASRUROH Megawati Indriani Mohd Farhan MD Fudzee, Mohd Farhan Mufrody, Moh Adam Mustain Mustain Nafiiyah, Nur Nur Nafi'iyah Nur Nafi’iyah Nurroziqin, M Chabib Nurul Aswa Omar Nurul Ftria ApriLliani Pertiwi, Dinda Dwi Anugrah Prastowo, Diko Pratiwi, Putri Septiani Indah Prisma Nanda Prsatama, Febrian Abie Rahayu A Hamid Retno Wardhani Rofika Arista Sari, Putri Dina Setia Budi, Agus Sika Azkia, Czidni Siti Mujilahwati Sulaiman, Akhmad Nurali Surojuddin, Eko Titin Nurbella Udiansyah, Naufal Arrafi Ulum, M. Miftah Fatkhul Umam, Moch. Zuhrul Vanesta Ikhsana Putri Maulana Wati, Efi Neo Yulian Ansori Zirby, Qonit Zumrotus Shalekhah