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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknik Elektro dan Komputer TRIAC JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Repositor INOVTEK Polbeng - Seri Informatika INJECT Interdisciplinary Journal of Communication
Christian Sri Kusuma Aditya
Universitas Muhammadiyah Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Implementasi Automatic Speech Recognition Bacaan Al-Qur’an Menggunakan Metode Wav2Vec 2.0 dan OpenAI-Whisper Danny Ferdiansyah; Christian Sri Kusuma Aditya
Jurnal Teknik Elektro dan Komputer TRIAC Vol 11, No 1 (2024): Mei 2024
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v11i1.24332

Abstract

Implementasi Pengenalan Ucapan Otomatis untuk memprediksi bacaan sering digunakan dalam kehidupan sehari-hari. Salah satu tujuan yang dilakukan penelitian ini adalah untuk mengurangi angka buta mengaji Al-Qur'an pada umat Islam dengan mengimplementasikan ASR sebagai prediksi huruf hijaiyah dan membaca dengan teks ayat-ayat suci Al-Qur'an sebagai target. Data diambil dari platform YouTube dengan suara-suara murottal dari Syeikh Mahmoud Al-Hussary. Ada banyak metode deep learning ASR yang dapat digunakan untuk memprediksi kata ( transcribing ), contohnya adalah Wav2vec 2.0 dan OpenAI-Whisper . Hasil dari metode Wav2vec 2.0 menunjukkan nilai Character Error Rate (CER) dalam memprediksi ayat suci Al-Qur'an dari jarak 0.226 (23%) ~ 0.677 (68%). Hasil dari metode OpenAI-Whisper menunjukkan performa yang lebih bagus daripada Wav2vec 2.0 dengan nilai Character Error Rate (CER) dari rentang 0.064 (6%) ~ 0.172 (17%). Hasil dari kedua metode yang telah diusulkan mengimplikasikan bahwa nilai error yang rendah menjadi metode yang terbaik dengan kesalahan yang minimal.
STUNTING CLASSIFICATION IN CHILDREN USING VIOLA-JONES AND MULTI-FEATURE FUSION WITH PRE-TRAINED MODELS Maylani Kusuma Wardhani; Garin Muhammad Akbar; Christian Sri Kusuma Aditya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7890

Abstract

Stunting remains a critical public health issue, particularly in developing countries, where early detection plays a vital role in prevention and intervention. Previous studies have generally relied on single-feature approaches, either using handcrafted descriptors or convolutional neural networks (CNNs) alone, which often fail to capture subtle craniofacial differences associated with stunting. This study proposes an image-based classification system for detecting stunting in children using facial analysis. The proposed method integrates Viola–Jones face detection with facial landmarks, Gray Level Co-occurrence Matrix (GLCM), Color Co-occurrence Matrix (CCM), and local descriptors such as SIFT–FAST/ORB, combined with deep features extracted from a pre-trained EfficientNet model. Feature fusion was performed by concatenating handcrafted and deep features before classification using a fully connected layer with Softmax activation. Experimental results demonstrated that the proposed fusion model achieved superior performance compared to single-feature baselines, reaching 98% accuracy, 0.98 precision, 0.97 recall, and an F1-score of 0.98. These findings indicate that the integration of geometric, texture, color, and deep semantic cues effectively enhances sensitivity toward the stunting class and improves model interpretability. The novelty of this study lies in the combination of classical computer vision and deep learning techniques for robust, interpretable, and clinically relevant stunting detection. This approach offers strong potential for developing digital health tools that enable early, non-invasive stunting screening in children.
Alzheimer's Disease Classification Using the Tabnet Model Enhanced by Hyperparameter Optimization M Bagus Triyadi; Christian Sri Kusuma Aditya
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/yb05hg15

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder that leads to a gradual decline in cognitive function and remains challenging to diagnose at an early stage, as clinical symptoms often emerge after substantial brain damage has occurred. Therefore, accurate and efficient predictive models based on clinical data are essential to support early detection. Recent advances in deep learning for tabular data, particularly the TabNet model, enable adaptive feature selection through attention mechanisms while preserving interpretability. This study applies TabNet for Alzheimer’s disease classification using clinical tabular data and enhances its performance through hyperparameter optimization employing grid search, random search, and Bayesian optimization. Model evaluation was conducted using accuracy, area under the curve (AUC), confusion matrix analysis, and execution time. Experimental results show that random search achieved the highest classification accuracy of 90.53%, whereas Bayesian optimization obtained the highest AUC of 94.82%, indicating superior discriminative capability. These results demonstrate that integrating TabNet with appropriate hyperparameter optimization strategies provides a competitive, efficient, and interpretable approach for Alzheimer’s disease classification, supporting its potential application in data-driven clinical decision support systems.
PENGEMBANGAN MODEL KLASIFIKASI SPESIES FAUNA LANGKA INDONESIA DENGAN METODE BACKGROUND SUBTRACTION DAN DEEP LEARNING: DEVELOPMENT OF A CLASSIFICATION MODEL FOR RARE INDONESIAN FAUNA SPECIES USING BACKGROUND SUBTRACTION AND DEEP LEARNING Anindya Samantha Prayoga; Christian Sri Kusuma Aditya
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7652

Abstract

Wildlife monitoring systems based on camera traps require automated classification methods capable of processing thousands of images with high accuracy to support endangered species conservation. One of the main challenges in animal image classification is the complexity of vegetation backgrounds and dynamic lighting, which are often considered visual distractions for deep learning models. The use of background subtraction methods aims to separate fauna objects from their backgrounds to enhance the model's focus on key animal features. However, the effectiveness of background subtraction on the classification accuracy of Indonesian fauna species still needs further evaluation. This study develops a classification model by evaluating the impact of background subtraction using four segmentation methods (DeepLabV3+, PSPNet, YOLOv11n-seg, and Mask R-CNN) integrated with MobileNetV2 and ResNet-50 architectures. Experimental results on 10,800 images of 9 rare fauna species show that background subtraction significantly reduces accuracy, with a decrease of up to 33% for YOLOv11n-seg and 13-14% for Mask R-CNN. Conversely, models without background subtraction achieved the highest accuracy of 98%. These findings identify that the background in camera trap images is not noise, but vital contextual information that helps the model recognize species.
Students Final Academic Score Prediction Using Boosting Regression Algorithms Dignifo Nauval Muhammady; Haidar Aldy Eka Nugraha; Vinna Rahmayanti Setyaning Nastiti; Christian Sri Kusuma Aditya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28352

Abstract

Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.
Hybrid Video Transcription Summarization with a BERT-Based Clustering and BART Fathul Agit Darmawan; Muhammad Bima Mauludin; Christian Sri Kusuma Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7066

Abstract

The use of video as a medium for information and education is rapidly increasing across online platforms. However, long durations and unstructured delivery often hinder audiences from grasping the core message, presenting challenges for the development of automatic summarization methods for monologues, interviews, and podcasts. Extractive methods often yield less coherent summaries, while abstractive methods may overlook important details. To address this issue, this study proposes a hybrid approach combining extractive and abstractive techniques. In the extractive stage, sentences are represented using BERT embeddings and clustered using two methods, namely K-Means Clustering and Hierarchical Clustering (agglomerative). The abstractive stage then employs the BART model to generate summaries that are more coherent and informative. Experimental evaluations on 20 Human Metapneumovirus (HMPV) videos indicate the strongest performance on monologues, with ROUGE-1 of 57%, ROUGE-2 of 30%, and ROUGE-L of 32%. Although lower performance was observed for interviews and podcasts due to dynamic interactions and frequent speaker shifts, the hybrid approach consistently surpassed extractive-only and abstractive-only baselines. These results highlight the effectiveness of the hybrid approach and its potential for developing more adaptive video summarization in the future.
Comparison of VGG16 and VGG19 Models in the Classification of Down Syndrome in the European Region with Transfer Learning Excel Bima Evansyah; Christian Sri Kusuma Aditya
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/pz35e881

Abstract

Down syndrome detection by utilizing facial images as the main data has been widely developed through deep learning approaches, especially Convulutional Neural Network (CNN). However, most studies only classify the disorder without paying attention to regional factors. This has limited the effectiveness of the model in the classification of Down syndrome, especially in populations in European regions that have different morphological characteristics. This study examines the performance of two pretrained CNN models, namely VGG16 and VGG19, in classifying facial images of children from Europe who are divided into 2 categories of Down Syndrome and Healthy. The dataset used in the study consists of 1,543 images from the Down syndrome class 671 images and the Healthy class 872 images. It was then expanded to 1570 images to balance the data between both Down syndrome and Healthy classes. The evaluation results of this research by applying augmentation show that the VGG16 model has superior performance compared to VGG19, with accuracy reaching 94%. Meanwhile, the VGG19 model obtained an accuracy of 90%. This difference shows that the VGG16 model has a more stable performance in detecting both categories with a better balance between precision and recall. This research is limited to European children's image data and still does not exist for ethnic teenagers or the elderly. This provides a basis for the development of facial image-based early detection systems, particularly for clinical applications or early screening in areas with similar populations.
BITCOIN PRICE PREDICTION WITH TECHNICAL INDICATORS: A HYBRID TRANSFORMER-RIDGE REGRESSION APPROACH Dio Richard Prastiyo; Muhammad Zaky Darajat; Christian Sri Kusuma Aditya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.7789

Abstract

Bitcoin has emerged as the dominant cryptocurrency, exhibiting rapid adoption alongside extreme price volatility that complicates investment strategies, risk management, and regulatory decision-making. While prior hybrid studies have predominantly combined multiple deep learning components such as CNN–LSTM or Transformer–GRU architectures, the integration of a deep neural architecture with a regularized linear model remains underexplored in Bitcoin price forecasting. To address this gap, this study proposes a hybrid framework combining a Transformer neural network with Ridge Regression, wherein the Transformer captures nonlinear temporal dependencies while Ridge Regression introduces L2 regularization to mitigate overfitting and enhance interpretability—an integration explicitly motivated by the bias–variance trade-off. The model is trained on technical indicators including MACD, Bollinger Bands, and RSI, and an ensemble weighting parameter α is systematically optimized via grid search. Empirical evaluation demonstrates that the hybrid model consistently outperforms standalone baselines, achieving an MAE of 1,251.572, RMSE of 1,623.004, R² of 0.991, and MAPE of 1.701%, with performance differences confirmed statistically via the Diebold–Mariano test. Economic validation reveals that the hybrid model is the only strategy to demonstrate statistically significant directional accuracy, although absolute trading returns remain below passive benchmarks under trending market conditions—a dissociation consistent with established findings in financial forecasting research. These results indicate that the model's primary contribution lies in forecast reliability and directional signal quality rather than return maximization under simple trading rules. Sensitivity to macroeconomic shocks and computational demands remain limitations for real-time deployment, suggesting directions for future research.
Co-Authors Abdul Hadiy Dyo Fatra Adam Novrisal Ahmad Akbar Maulana Ahmad Rizky Habibullah Anindya Samantha Prayoga Chandranegara, Didih Rizki Danny Ferdiansyah Dati Nafa Alfiana Dignifo Nauval Muhammady Dio Richard Prastiyo Dzur Rifqi Aziz Evi Dwi Wahyuni Excel Bima Evansyah Fatahillah Arsyad Fathul Agit Darmawan Fatimah Defina Setiti Alhamdani Garin Muhammad Akbar Gian Bagus Sadewa Gita Indah Marthasari Haidar Aldy Eka Nugraha Hamdan Nafiatur Rosyida Hardianto Wibowo Hasbi Atsqalani Hilman Abi Sarwan Heryawan Indra Puja Laksana M Bagus Triyadi Maylani Kusuma Wardhani Moch Fuad Nasvian Muhammad Abizar Muhammad Bima Mauludin Muhammad Ferry Septian Ihzanor Syahputra Muhammad Robby Fathuda Muhammad Zaky Darajat Nisrina Arintia Maghfiroh Nur Hayatin Nuryasin, Ilyas Qori Raditya Damayanti Raffi Ainul Afif Rakha Pradana Susilo Putra Roni Hadi Wijaya S, Vinna Rahmayanti Sabrila, Trifebi Shina Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Vera Suryani Wicaksono, Galih Wasis Wildan Suharso Yufis Azhar