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All Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Eduvest - Journal of Universal Studies Jurnal Ilmiah Multidisiplin Indonesia
Shintami Chusnul Hidayati
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Bank Customer Churn Prediction Using a Hybrid Ensemble Soft Voting Approach Based on Tabnet and XGBOOST Mohamad Syazimmi Hersyaputra; Shintami Chusnul Hidayati
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.52494

Abstract

In an increasingly competitive banking industry, the ability to predict potential customer churn is a strategic factor in maintaining business profitability and sustainability. Churn has a direct impact on a bank’s revenue and operational efficiency; therefore, a prediction model is needed that is not only accurate but also stable and adaptive to variations in customer data. This study proposes a hybrid ensemble soft voting approach based on TabNet and XGBoost to improve the performance and robustness of churn prediction. TabNet, with its sequential attention mechanism, can selectively identify important features, while XGBoost excels at handling nonlinear relationships and controlling overfitting through gradient boosting regularization. The two models are combined using a probability-based soft voting mechanism to produce more balanced and consistent predictions. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied so that the data distribution is more proportional and churn patterns can be better represented. The experimental results show that the proposed approach achieves optimal performance, with an accuracy of 96.74%, precision of 90.09%, recall of 89.53%, and an F1-score of 89.81%. These values indicate that the model is able to maintain a balance between accurate churn detection and the minimization of misclassification. This hybrid ensemble soft voting approach has proven to be superior to single models in terms of predictive stability and generalization capability, making it an effective framework to support data-driven customer retention strategies in the banking sector.
Co-Authors ABDUL MUNIF Adhatus Solichah Ahmadiyah Kelly Rosa Sungkono Mohamad Syazimmi Hersyaputra Muhamad Hilmil Muchtar Aditya Pradana Ratih Nur Esti Anggraini Riyanarto Sarno Sulaeman Salasa