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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Jurnal Sains dan Informatika Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Electronics and Communications Systems Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Andi Farmadi
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models Agustina, Winda; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Saragih, Triando Hamonangan; Farmadi, Andi; Budiman, Irwan; Parenreng, Jumadi Mabe; Alkaff, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5098

Abstract

Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.
Prediction of Life Expectancy of Lung Cancer Patients After Thoracic Surgery Using Decision Tree Algorithm and Adaptive Synthetic Sampling Erdi, Muhammad; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Farmadi, Andi; Saragih, Triando Hamonangan; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4724

Abstract

This research focuses on predicting the life expectancy of lung cancer patients after undergoing thoracic surgery, using a decision tree classification algorithm (C4.5) combined with adaptive synthetic sampling to handle data imbalance. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. By applying ADASYN, the data distribution becomes more even, thus improving the performance of the C4.5 model. The results showed that combining these methods increased the prediction accuracy from 67% to 87%. In addition, the precision, recall, and f1-score for minority classes have significantly improved, which were previously difficult to identify by the model. Thus, combining the C4.5 algorithm and the ADASYN technique proved effective in dealing with the challenge of data imbalance and resulted in better prediction in the case of lung cancer. This study is expected to contribute to the field of medical classification and serve as a reference for further research on similar cases.
Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm Syahputra, Muhammad Reza; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Sutaji, Deni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4723

Abstract

Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.
Deep CNN for Wetland Mapping from Satellite Imagery Ramadhan, As`'ary; Herteno, Rudy; Farmadi, Andi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5280

Abstract

Wetland loss endangers the ecosystem through loss of biodiversity, carbon sequestration and flood regulation potential. A precise determination of wetlands status is necessary to safeguard for their conservation and ensure sustainable management. Implementation This study aims to assess the performance of deep CNNs in wetland detection using high-resolution Google Earth image data in South Kalimantan province, Indonesia. The work adopts the Chopped Picture Method (CPM) and the use of sliding windows for data augmentation to improve the diversity of the dataset and reduce the computational cost. Two CNN models, VGG-16Net, and LeNet-5, were trained using a dataset comprising 220 satellite images, which we converted into 89,100 patches of 56×56 pixels. Performance was compared using accuracy, precision, recall, and F1-score. Experimental results show good levels of accuracy for the two architectures, but LeNet-5 provided more stable results between test locations, having a F1-score closer to 100% and spending less computational time (≈10s per epoch) than VGG-16Net (≈40s per epoch). These results validate that CPM significantly increases the variety of training data, making it possible for a CNN to correctly identify the vague and irregular shapes of wetlands with high accuracy. In addition to advancing environmental conservation strategies, the study highlights the contribution of informatics to large-scale, automated environmental monitoring, particularly in supporting wetland conservation, sustainable land-use planning, and climate adaptation efforts.
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
Co-Authors Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Achmad Rizal Adi, Puput Dani Prasetyo Ahdyani, Annisa Salsabila Ahmad Bahroini Ahmad Faris Asy’arie Ahmad Juhdi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Akhmad Yusuf Ando Hamonangan Saragih Ardiansyah Sukma Wijaya Arif, Nuuruddin Hamid Arifin Hidayat Aris Pratama Azizah, Siti Roziana Bachtiar, Adam Mukharil Bahriddin Abapihi Bedy Purnama Deni Sutaji Dita Amara Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Efendi Mohtar Erdi, Muhammad Evi Nadya Prisilla Faisal Murtadho Fathul Hadi Fatma Indriani Fayyadh, Muhammad Naufaldi Fitria Agustina fitria Friska Abadi Ghinaya, Helma Gita Malinda Hertono, Rudy Heru Candra Kartika Heru Kartika Chandra I Gusti Ngurah Antaryama Ichsan Ridwan Irwan Budiman Irwan Budiman Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Keswani, Ryan Rhiveldi Khairunnisa Khairunnisa Lisnawati M. Apriannur Miftahul Muhaemen Muhammad Alkaff Muhammad Halim Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rusli Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi muliadi muliadi Musyaffa, Muhammad Hafizh Mutiara Ayu Banjarsari Nafis Satul Khasanah Ngo, Luu Duc Noryasminda Nugraha, Muhammad Amir Nugrahadi, Dodon Nurcahyati, Ica Nurlatifah Amini P., Chandrasekaran Patrick Ringkuangan Pirjatullah Pirjatullah Pirjatullah Priyatama, Muhammad Abdhi RACHMAT HIDAYAT Radityo Adi Nugroho Raidra Zeniananto Ramadhan, As`'ary Rifki Izdihar Oktvian Abas Pullah Rifki Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rusdiani, Husna Sabur, Baharuddin Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sa’diah, Halimatus Setyo Wahyu Saputro Shalehah Srihardjanti, Rurien Suci Permata Sari Syahputra, Muhammad Reza Tajali, Ahmad Ulya, Azizatul Umar Ali Ahmad Wijaya Kusuma, Arizha Winda Agustina YILDIZ, Oktay