Garuda - Garba Rujukan Digital

FAJRI RAKHMAT UMBARA

Universitas Jenderal Achmad Yani, Cimahi, Indonesia

Author-ID : 10665015

Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Public Health Social Sciences

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Articles

Prediksi Penyakit Diabetes menggunakan Teknik Imputasi Missforest dan Klasifikasi LightGBM ALDOVA FERDIANSYAH; FAJRI RAKHMAT UMBARA; FATAN KASYIDI
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.221-234

AbstrakDiabetes adalah salah satu penyakit kronis dengan grafik prevalensinya meningkat secara global. Penyakit ini disebabkan oleh gangguan metabolisme tubuh yang memengaruhi kadar gula darah, dan jika tidak ditangani sejak dini dapat menimbulkan komplikasi serius seperti stroke, gagal ginjal, kebutaan, hingga kematian. Penelitian ini mengembangkan model prediksi risiko diabetes berbasis klasifikasi biner menggunakan algoritma LightGBM yang dikombinasikan dengan teknik imputasi Missforest untuk menangani data yang hilang. Dataset yang digunakan berasal dari Pima Indian, tersedia secara publik di Kaggle. Tahapan pre-processing mencakup imputasi data hilang, penanganan outlier dengan Isolution Forest, pembagian data menjadi 80:20. Evaluasi model menunjukkan hasil akurasi sebesar 91,84% dan ROC AUC 0.9614. BMI menjadi faktor paling berpengaruh dalam prediksi yang diikuti oleh DiabetesPedigreeFunction dan Glucose.Kata kunci: diabetes melitus, data mining, klasifikasi, LightGBM, missforestAbstractDiabetes mellitus is one of the most common chronic diseases, with a globally increasing prevalence. It is caused by metabolic disorders that affect blood glucose levels and, if not treated early, can lead to serious complications such as stroke, kidney failure, blindness, and even death. This research develops a diabetes risk prediction model based on binary classification using the LightGBM algorithm combined with the Missforest imputation technique to handle missing data. The dataset used is the publicly available Pima Indian dataset from Kaggle. The pre-processing stages include missing value imputation, outlier handling using Isolution Forest, an 80:20 data split. Model evaluation shows an accuracy of 91.84% and a ROC AUC 0.9614. BMI was found to be the most influential factor in the prediction, followed by DiabetesPedigreeFunction and Glucose.Keywords: diabetes mellitus, data mining, classification, LightGBM, missforest

Application of the XGBoost Method with CNN Feature Extraction for AI-Generated Video Detection Adhitya Bagus Pratikno; Fajri Rakhmat Umbara; Ridwan Ilyas
INFLUENCE: INTERNATIONAL JOURNAL OF SCIENCE REVIEW Vol. 8 No. 3 (2026): INFLUENCE: International Journal of Science Review
Publisher : Global Writing Academica Researching and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/influencejournal.v8i3.396

The rapid advancement of generative artificial intelligence has substantially improved the visual quality of synthetic video, making it increasingly difficult to distinguish from authentic footage and creating new challenges for digital content verification. This study develops and evaluates an AI-generated video detection system using a hybrid approach that integrates a Convolutional Neural Network (CNN) as a feature extractor with Extreme Gradient Boosting (XGBoost) as the classifier. The research uses a 70,000-video subset of the GenVidBench-143k benchmark, selected through stratified sampling for computational efficiency. Feature extraction is performed in parallel using ResNet50, which captures high-level semantic features, and VGG16, which captures local texture detail; the resulting 2,048- and 512-dimensional vectors are concatenated into a single 2,560-dimensional representation per frame. A temporal aggregation stage transforms frame-level features into video-level features using Average Pooling and Max Pooling, and a Cost-Sensitive Learning strategy is applied through the scale_pos_weight, sample_weight, and max_delta_step parameters of XGBoost to address the marked class imbalance between real and AI-generated videos. The experimental results show that the hybrid ResNet50-VGG16 architecture combined with Average Pooling aggregation and scale_pos_weight optimization yields the best performance, achieving 92.93% accuracy, with 0.89 precision and 0.85 recall for the real-video class, and 0.94 precision and 0.96 recall for the AI-video class. A robustness analysis further shows that the model performs strongly on low-resolution video (<720p), reaching 94.00% accuracy, and maintains 88.00% accuracy on high-resolution video (>=720p); the modest decline at higher resolution reflects the increasingly seamless visual quality of modern generative models. These findings indicate that the hybrid CNN-XGBoost approach is a promising and computationally efficient strategy for synthetic video detection, although broader training-data variation is still required to reduce detection errors on high-quality video in real-world scenarios.

Application of IndoBERT for Granular Emotion Detection in Public Sentiment toward the Free Nutritious Meals Program (MBG) Alifiya Seftika Putri; Fajri Rakhmat Umbara; Ridwan Ilyas
INFLUENCE: INTERNATIONAL JOURNAL OF SCIENCE REVIEW Vol. 8 No. 3 (2026): INFLUENCE: International Journal of Science Review
Publisher : Global Writing Academica Researching and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/influencejournal.v8i3.400

The Free Nutritious Meals Program (Program Makan Bergizi Gratis/MBG) is a government policy that has generated diverse public responses on social media. Previous studies have generally focused on sentiment analysis, which may not adequately capture the fine-grained emotional responses of the public, while the use of data augmentation and misclassification analysis remains limited. This study aims to classify public emotions toward the MBG Program using IndoBERT based on Robert Plutchik’s emotion theory, which consists of eight emotion categories: anger, happiness, sadness, trust, fear, disgust, interest, and surprise. Data were collected through web scraping from X (Twitter), TikTok, and Facebook and were subsequently annotated by the author and two expert annotators. The preprocessing steps included case folding, data cleaning, and normalization. Back-translation data augmentation was applied to minority classes in the training set to address class imbalance. The experiments compared baseline and augmented data scenarios using several learning-rate configurations. Model performance was evaluated using accuracy, precision, recall, and F1-score, along with misclassification analysis. The results show that Model M4, which employed data augmentation with a learning rate of 1 × 10⁻⁵, a batch size of 16, and 5 epochs, achieved an accuracy of 85.13% and a macro F1-score of 85.39%. Based on the 733 test records, anger was the dominant emotion (31.79%), followed by happiness (16.78%) and trust (15.96%). Anger was the dominant emotion on Facebook and TikTok, whereas trust was dominant on X (Twitter). The misclassification analysis revealed classification errors among several emotions with semantically similar characteristics. These findings demonstrate the applicability of IndoBERT combined with data augmentation for granular emotion classification of public responses to the MBG Program.

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