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All Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Ilmiah Multidisiplin Indonesia
Shintami Chusnul Hidayati
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Medicine & Pharmacology Physics Public Health

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Evaluasi Capstone Project Desain Antarmuka Aplikasi untuk Guru MGMP TIK Surabaya Adhatus Solichah Ahmadiyah; Riyanarto Sarno; Shintami Chusnul Hidayati; Ratih Nur Esti Anggraini; Kelly Rosa Sungkono; Abdul Munif
ABDI: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 6 No 3 (2024): Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Labor Jurusan Sosiologi, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/abdi.v6i3.905

Abstract

Peningkatan keterampilan desain aplikasi bagi guru MGMP TIK Surabaya penting untuk ditingkatkan guna mendukung kualitas pendidikan yang lebih baik. Kegiatan pengabdian kepada masyarakat ini dilakukan oleh tim dosen dan mahasiswa laboratorium manajemen cerdas informasi teknik informatika ITS dengan tujuan memberikan rekomendasi dan masukan terkait permasalahan desain antarmuka aplikasi yang dibuat oleh guru peserta pelatihan. Metode yang digunakan adalah evaluasi heuristik dan klasifikasi permasalahan menggunakan severity level. Hasil dari kegiatan ini menunjukkan bahwa guru menjadi tahu apa permasalahan yang ada di desain antarmuka yang dibuat dan mengetahui aksi perbaikan. Hal ini juga memperjelas penyampaian materi dengan permasalahan dan solusi nyata, sehingga dapat meningkatkan kualitas pembelajaran dan pengajaran
Application of Hybrid CNN-LSTM Architecture with Optuna Optimization for Weather Image Captioning Sulaeman Salasa; Shintami Chusnul Hidayati; Muhamad Hilmil Muchtar Aditya Pradana
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 03 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), March 2026
Publisher : Sean Institute

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Abstract

Automating the description of weather phenomena through visual imagery is a crucial step in supporting efficient meteorological monitoring systems. This study aims to compare the performance of two Deep Learning architectures, ResNet101-LSTM and VGG16-LSTM, in generating automatic image captions for various weather conditions. The research methodology involves extracting visual features using Residual Learning and VGG-Net, which are subsequently processed by Long Short-Term Memory (LSTM) units for text generation. Hyperparameter optimization was conducted using the Optuna framework to ensure both models operate at their peak configurations. The results indicate that ResNet101-LSTM provides superior linguistic accuracy, achieving a BLEU-1 score of 0.7553, a BLEU-4 score of 0.4593, and a METEOR score of 0.7264. Qualitatively, this model is capable of identifying environmental details with higher precision compared to VGG16-LSTM. However, loss curve analysis reveals that VGG16-LSTM demonstrates better convergence stability (good fit), whereas ResNet101-LSTM shows signs of slight overfitting. This study concludes that while ResNet101-LSTM is superior in accuracy according to standard NLP evaluation metrics, additional regularization techniques are required to maintain its performance stability on validation data.
Co-Authors ABDUL MUNIF Adhatus Solichah Ahmadiyah Kelly Rosa Sungkono Muhamad Hilmil Muchtar Aditya Pradana Ratih Nur Esti Anggraini Riyanarto Sarno Sulaeman Salasa