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IJAIN (International Journal of Advances in Intelligent Informatics)
Scopus
ISSN 2442-6571 · Universitas Ahmad Dahlan · 355 artikel di indeks
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Artikel Jurnal
Synergistic preprocessing approaches for improved time series analysis
This paper systematically evaluates the performance of an LSTM baseline model, along with four smoothing augmentation methods (Kalman, Laplace, Moving Average, Savitzky-Golay), under different normalization strategies (Min-Max and Z-Score) for multivariate time-series forecasting. Experiments were…
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Artikel Jurnal
A cascaded classification approach using transfer learning and feature engineering for improved breast cancer classification
The primary objective of this study is to design a cascaded classification framework that integrates deep-learning representations with handcrafted and clinical features to enhance the reliability and accuracy of breast cancer detection in mammographic screening. A multi-source mammography dataset…
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Artikel Jurnal
Fixed sherwood duel optimization for time series imputation
Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased…
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Artikel Jurnal
Towards a high-accuracy framework for quranic reciter recognition using deep learning and a large-scale benchmark dataset
Speaker recognition aims to identify who is speaking from their voice and is widely used in security, personalization, and archival search. A related, culturally significant task is recognizing Qur’ān reciters from their recitations. The Quran is the central religious text of Islam and is recited…
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Artikel Jurnal
Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement
Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel…
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Artikel Jurnal
A comprehensive comparative analysis of chicken meat classification techniques through machine learning models
This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction…
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Artikel Jurnal
TUD-BISINDO: A new dataset and its recognition system using YOLO
This study addresses the urgent need for digital inclusivity by developing a high-precision, real-time recognition system for Bahasa Isyarat Indonesia (BISINDO). The main new idea in this study is the creation of the Telkom University Database (TUD)-BISINDO, which is a strong and varied collection…
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Artikel Jurnal
Underwater image enhancement with fuzzy histogram equalization and adaptive color correction
Marine exploration continues to increase as new technologies, such as computer vision implemented in underwater vehicles and robots, develop. Identifying underwater objects is challenging due to environmental conditions, including poor lighting and color absorption in the viewed image. Underwater…
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Artikel Jurnal
PIFC-CLD: Poison image traceback via feature clustering and euclidean norm distance for clean-label attacks in deep neural networks
Clean-label poisoning attacks pose a stealthy and potent threat to deep neural networks (DNNs), particularly when models rely on publicly available or outsourced training data. Among these attacks, the Bullseye Polytope method is highly transferable and can evade state-of-the-art defenses such as…
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Artikel Jurnal
Collaborative filtering-based group recommender system using sparse autoencoder
The development of technology makes the distribution of information easier and faster, but leads to information overload. A recommender system is one tool to overcome information overload, while the collaborative filtering (CF) paradigm is a widely used approach in recommender systems. The…
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Artikel Jurnal
Comparative analysis of xception and svm for brain tumor classification on mri images
Brain tumor classification from magnetic resonance imaging (MRI) plays a critical role in supporting radiologists during diagnosis and treatment planning. However, many existing automated approaches employ limited preprocessing, single-stage transfer learning, or evaluation on a single dataset…
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Artikel Jurnal
Single-input and multi-input local binary pattern classification
Identification and classification of species are crucial for maintaining genetic diversity and supporting sustainable agricultural practices. The Toraja Buffalo, a unique type of buffalo in Indonesia, holds high cultural and economic value. Accurate classification of this species is essential to…
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Artikel Jurnal
Fine-tuned hyperparameter optimization for phishing website detection: insights into efficiency and performance
The escalation of digital threats has made phishing-site identification a critical aspect of online protection. This study investigates how systematic hyperparameter adjustment through grid search influences both predictive precision and computational efficiency in phishing detection. Nine…
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Artikel Jurnal
Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50
Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This study…
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Artikel Jurnal
Augmented haar cascade classifier for real-time ball detection in humanoid robots under dynamic environments
This study proposes an Augmented Haar Cascade Classifier (AHCC) to enhance real-time ball detection for humanoid robots operating in dynamic environments. The method integrates Convex Hull mapping, HSV-based segmentation, and Hough Circle validation to overcome challenges such as fluctuating…
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Artikel Jurnal
Predict customer churn in the banking sector: a machine learning approach with imbalanced data handling techniques
Customer value analysis is a critical component in formulating effective marketing and customer relationship management (CRM) strategies, especially in sectors where client movement and strong competition are prevalent A key element of this process lies in enhancing customer retention rates, as…
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Artikel Jurnal
Seq2Seq encoder–decoder with adaptive metaheuristic for scheduling optimization
The Job Shop Scheduling Problem (JSSP) is a combinatorial optimization problem that is NP-hard and highly complex, particularly in modern manufacturing environments associated with industry. Conventional metaheuristic methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are…
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Artikel Jurnal
Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability
Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and…
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Artikel Jurnal
Soft voting technique with explainable artificial intelligence (XAI) for predicting depression, anxiety and stress
Mental health severity assessment is often hindered by limited access to professional services and the time required for clinical evaluation. This study proposes an interpretable soft voting method to classify the severity levels of depression, anxiety, and stress using DASS-42 questionnaire data…
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Artikel Jurnal
Enhancing clinical safety in automated breast ultrasound segmentation through a sensitivity-prioritized detection and statistical fail-safe mechanism
Accurate breast ultrasound (BUS) lesion segmentation is critical for early diagnosis but is challenged by image artifacts and the reliance of foundation models on manual prompting. Existing automated frameworks often lack robust fail-safe mechanisms, leading to missed diagnoses. To address this…