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All Journal IPTEK The Journal for Technology and Science JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal EECCIS Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIN (Jurnal Online Informatika) JIEET (Journal of Information Engineering and Educational Technology) Building of Informatics, Technology and Science Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Eduvest - Journal of Universal Studies Sewagati Jurnal Ilmiah Multidisiplin Indonesia
Shintami Chusnul Hidayati
Institut Teknologi Sepuluh Nopember
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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IMPROVED LIP-READING LANGUAGE USING GATED RECURRENT UNITS Nafa Zulfa; Nanik Suciati; Shintami Chusnul Hidayati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1080

Abstract

Lip-reading is one of the most challenging studies in computer vision. This is because lip-reading requires a large amount of training data, high computation time and power, and word length variation. Currently, the previous methods, such as Mel Frequency Cepstrum Coefficients (MFCC) with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with LSTM, still obtain low accuracy or long-time consumption because they use LSTM. In this study, we solve this problem using a novel approach with high accuracy and low time consumption. In particular, we propose to develop lip language reading by utilizing face detection, lip detection, filtering the amount of data to avoid overfitting due to data imbalance, image extraction based on CNN, voice extraction based on MFCC, and training model using LSTM and Gated Recurrent Units (GRU). Experiments on the Lip Reading Sentences dataset show that our proposed framework obtained higher accuracy when the input array dimension is deep and lower time consumption compared to the state-of-the-art.
Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU Ravi Vendra Rishika; Baskoro Adi Pratomo; Shintami Chusnul Hidayati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 23, No. 1, January 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i1.a1241

Abstract

Technological development and the growth of the internet today have a positive and revolutionary impact in various areas of human life, such as banking, health, science, and more. The presence of Open Data and Open API also facilitates the exchange of data and information between entities without the restrictions imposed by different regions and geographical areas. However, information openness not only has a positive impact but also makes data vulnerable to data theft, viruses, and various other types of cyber attacks. The large-scale data exchange that occurs across the network poses a challenge in detecting unusual activity and new cyber attacks. Therefore, the existence of an Intrusion Detection System (IDS) is urgently essential. The IDS helps system administrators detect cyber attacks and network anomalies, thus minimizing the risk of data leaks and intrusions. The research developed a new approach using time-based sequential clustered data sets in the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This IDS model was implemented using the CIC-IDS 2018 data set, which has more than 4 million data lines. The capabilities and uniqueness of the LSTM and GRU models are used to classify and determine various attacks in IDS based on sequential data sets ordered by time and clustered according to the destination ports and protocols, such as TCP and UDP. The model was evaluated using the accuracy, precision, recall, and F-1 scores matrix, and the results showed that the time-based sequential clustered models in LSTM and GRU have an accurities of up to 97.21%. This suggests that this new approach is good enough to be applied to the future IDS models.
Optimalisasi Manajemen Keuangan Kelompok Belajar dan Taman Kanak-Kanak melalui Modul Front-Office KinderFin di Wilayah Kota Surabaya Ahmadiyah, Adhatus Solichah; Sarno, Riyanarto; Hidayati, Shintami Chusnul; Sungkono, Kelly Rossa; Anggraini, Ratih Nur Esti
Sewagati Vol 9 No 6 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i6.4679

Abstract

Pengelolaan keuangan yang efisien dan transparan sangat penting untuk mendukung operasional lembaga pendidikan anak usia dini, termasuk kelompok bermain (KB) dan taman kanak-kanak (TK). Namun, banyak institusi tersebut masih mengandalkan pencatatan manual yang rawan kesalahan dan tidak transparan. Kegiatan pengabdian masyarakat ini bertujuan untuk mengembangkan dan mengimplementasikan modul Front-Office aplikasi KinderFin guna membantu digitalisasi manajemen keuangan di tujuh sekolah KB/TK di beberapa kecamatan di Kota Surabaya. Metode pelaksanaan mencakup analisis kebutuhan mitra, pengembangan aplikasi dengan pendekatan iteratif, pelatihan langsung, serta evaluasi berbasis survei umpan balik. Hasil menunjukkan bahwa fitur-fitur utama aplikasi seperti PPDB, pembayaran SPP, dan pencatatan pengeluaran sangat membantu administrasi sekolah. Mitra memberikan penilaian positif dengan skor rata-rata 4,0 hingga 4,8 dari skala 5,0. Hal ini menunjukkan peningkatan transparansi dan efisiensi dalam pengelolaan keuangan sekolah. Program ini memberikan kontribusi nyata dalam transformasi digital tata kelola keuangan sekolah PAUD dan membuka peluang pengembangan lanjutan untuk adopsi skala lebih luas.
Model Machine Learning Berbasis Perilaku Pembayaran Angsuran untuk Prediksi Gagal Bayar KPR Subsidi Jaya, Muhammad Triyanda Taruna; Hidayati, Shintami Chusnul
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 3 (2025): Volume 11 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i3.100857

Abstract

Penelitian ini menyajikan rancangan dan pembuktian pemanfaatan model machine learning untuk prediksi gagal bayar sesuai definisi Otoritas Jasa Keuangan pada produk KPRS (Kredit Pemilikan Rumah Subsidi) berbasis perilaku pembayaran angsuran pada segmen MBR (Masyarakat Berpenghasilan Rendah). Berbeda dengan sebagian besar penelitian terdahulu yang berfokus pada application scoring saat pengajuan atau pencairan kredit dengan data statis nasabah seperti kemampuan finansial, riwayat peminjaman, informasi pekerjaan, agunan dan data statis lainnya, studi ini menargetkan kredit yang sudah berjalan (on-book) dengan memanfaatkan jejak historis pembayaran angsuran sebagai sumber utama sinyal risiko. Dataset berasal dari salah satu bank penyalur KPRS. Dengan teknik rekayasa fitur, data pembayaran angsuran diubah menjadi fitur tabular yang merangkum perilaku pembayaran (misalnya konsistensi nominal, kelancaran waktu bayar dan pola keterlambatan) yang kemudian dipelajari oleh beberapa metode machine learning, antara lain Multilayer Perceptron, Random Forest, XGBoost dan Logistic Regression. Data mencakup 8.116 akun dan 409.130 catatan transaksi dengan evaluasi menggunakan train set periode 2017–2022 (6.585 akun) dan test set 2023–2024 (1.217 akun). Model terbaik dicapai oleh MLP dengan performa AUC ≈ 0,997 pada test set dengan F1 Score maksimum pada threshold 0,3013 memberikan precision 0,7907, recall 0,9444 dan F1 0,8608. Hasil ini menunjukkan bahwa untuk pinjaman KPRS yang sudah berjalan, pola perilaku pembayaran angsuran semata—tanpa perlu menambahkan informasi mengenai kondisi usaha, kondisi finansial, agunan, maupun karakteristik lain nasabah—dapat dimanfaatkan untuk membangun model machine learning yang mampu memprediksi risiko gagal bayar secara akurat dan dapat memberikan early warning pada portofolio KPRS, sehingga tindakan pencegahan seperti intervensi, reminder atau kunjungan lapangan diharapkan dapat dilakukan secara lebih terarah dan efisien.
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 Abdillah, Rifqi ABDUL MUNIF ABDUL MUNIF Adhatus Solichah Ahmadiyah Adhatus Solichah Ahmadiyah, Adhatus Solichah Ahmadiyah, Adhatus Solichah Baskoro Adi Pratomo Darlis Herumurti Dava Aulia Ekohariadi Irawan Rahardja, Agustinus Aldi Jaya, Muhammad Triyanda Taruna Kelly Rosa Sungkono Kelly Rossa Sungkono Klemm, Sebastian Mohamad Syazimmi Hersyaputra Muhamad Hilmil Muchtar Aditya Pradana Muhammad Rivai Nafa Zulfa Nanik Suciati Navinda Meutia Nurul Fajrin Ariyani Pratama, Moch Deny Rachman, Rudy Raharja, Putu Bagus Gede Prasetyo Ratih Nur Esti Anggraini Ratih Nur Esti Anggraini, Ratih Nur Esti Ravi Vendra Rishika Riyanarto Sarno Rosyid, Alfian Nur Saikhu, Ahmad Sjahrunnisa, Anita Sulaeman Salasa Wahyuwidayat, Raditia Wardhiana, I Nyoman Gde Artadana Mahaputra Yeni Anistyasari