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All Journal Jurnal Ilmu Dasar BERKALA SAINSTEK JURNAL FISIKA Saintifika UNEJ e-Proceeding CogITo Smart Journal Jurnal Pendidikan Fisika dan Keilmuan (JPFK) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Data Science: Journal of Computing and Applied Informatics JURTEKSI JURNAL GEOGRAFI Geografi dan Pengajarannya Jurnal Pengabdian dan Kewirausahaan NEAR: Jurnal Pengabdian kepada Masyarakat SAINTIFIKA Jurnal Pengabdian Masyarakat Nusantara
Puguh Hiskiawan
Universitas Bunda Mulia
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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MULTI VIEW FEATURE FUSION FOR INDUSTRIAL ANOMALY DETECTION USING 1D-CNN Daniel Fernando Nainggolan; Puguh Hiskiawan
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4501

Abstract

Abstract: Anomalous sound detection is essential for industrial predictive maintenance, as machine failures often originate from subtle acoustic changes during operation. However, high background noise and limitations of conventional Convolutional Neural Networks (CNN) reduce detection reliability. This study proposes a 1D-CNN-based anomaly detection framework with multi-view feature fusion and temporal segmentation to enhance detection performance. The approach combines MFCC, Log-Mel Spectrogram, and Chroma STFT features, while temporal segmentation divides audio signals into 5-second segments to better capture transient anomalies. Experiments on the MIMII dataset under varying Signal-to-Noise Ratio (SNR) conditions show that MFCC and Log-Mel fusion achieves the best performance, with 97.90% accuracy and ROC-AUC of 0.9789. The model maintains accuracy above 90% at −6 dB, demonstrating strong robustness in noisy industrial environments. Keywords: industrial anomaly detection; 1D-CNN; multi-view feature fusion; temporal segmentation; MIMII dataset. Abstrak: Deteksi anomali suara merupakan komponen penting dalam sistem pemeliharaan prediktif industri, karena kegagalan mesin sering diawali oleh perubahan akustik yang bersifat halus selama proses operasi. Namun, tingkat kebisingan yang tinggi serta keterbatasan arsitektur Convolutional Neural Network (CNN) konvensional dapat menurunkan keandalan deteksi. Penelitian ini bertujuan mengusulkan kerangka deteksi anomali berbasis 1D-CNN yang mengintegrasikan strategi fusi fitur multi-view dan segmentasi temporal untuk meningkatkan kinerja deteksi. Pendekatan yang digunakan menggabungkan fitur MFCC, Log-Mel Spectrogram dan Chroma STFT, sementara teknik temporal splitting membagi sinyal audio menjadi segmen berdurasi 5 detik untuk menangkap anomali yang bersifat sementara. Eksperimen menggunakan dataset MIMII pada berbagai kondisi Signal-to-Noise Ratio (SNR) menunjukkan bahwa kombinasi MFCC dan Log-Mel Spectrogram menghasilkan kinerja terbaik dengan akurasi 97,90% dan ROC-AUC sebesar 0,9789. Model juga mempertahankan akurasi di atas 90% pada kondisi kebisingan ekstrem (−6 dB) yang menunjukkan ketahanan yang baik dalam lingkungan industri yang bising. Kata kunci: deteksi anomali industri; 1D-CNN; fusi fitur multi-view; segmentasi temporal; dataset MIMII
An Applied Data Science Approach for Detecting Depression Symptoms in Indonesian Social Media Text Using Transformer Models Winson Winson; Puguh Hiskiawan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12644

Abstract

Depression is a mental health disorder that often remains undetected due to limited access to mental health services and persistent social stigma. Social media platforms provide an alternative source for identifying depressive symptoms through linguistic expressions shared by users in textual posts. This study proposes an applied data science approach for detecting depression symptoms in Indonesian social media text using Transformer-based models. The dataset was constructed by combining the DEPTWEET dataset with social media posts collected through keyword-based scraping guided by PHQ-9 indicators. The proposed framework consists of dataset construction, text preprocessing, Transformer-based modeling, and performance evaluation. Two pre-trained language models, IndoBERT and XLM-RoBERTa, were evaluated under two preprocessing configurations, namely normal preprocessing and light preprocessing. Experimental results show that preprocessing strategies significantly influence classification performance. Light preprocessing consistently improves contextual representation and leads to better results compared with normal preprocessing. XLM-RoBERTa combined with light preprocessing achieves the best overall performance with a test accuracy of 0.77 and an F1-score of 0.77. Additional robustness analysis and pairwise model agreement evaluation further indicate that both models maintain relatively stable predictions when processing noisy social media text. Findings from this study demonstrate the effectiveness of Transformer-based models for multi-class depression detection in Indonesian social media environments. The proposed framework provides insights into how applied data science techniques can support large-scale analysis of mental health signals in online platforms and contribute to the development of data-driven approaches for early detection of depression symptoms.
Applied Data Science Framework for Incremental and Interpretable Childhood Growth Risk Screening Puguh Hiskiawan; Theresia Puspa Wijayanti; Srava Chrisdes Antoro; Metta Gautama
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 2 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI) In Press
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10i2.24956

Abstract

Childhood growth monitoring plays an important role in the early identification of children who may experience developmental risks related to nutrition and health conditions. Conventional screening methods typically rely on anthropometric measurements that may not always be consistently obtained in community-based health environments. This study proposes an applied data science framework for incremental and interpretable screening of childhood growth risk using pose-derived body structure features combined with demographic and anthropometric attributes. Pose landmarks are extracted using the MediaPipe framework and integrated with variables including age, gender, and body weight to construct predictive models. Several machine learning algorithms are evaluated, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and a Soft Voting Ensemble. Experimental evaluation using five-fold stratified cross-validation demonstrates that Logistic Regression achieves the highest predictive performance with a mean ROC-AUC of 0.901. Ablation analysis further indicates that incorporating pose-derived landmarks significantly improves classification performance compared with using demographic attributes alone. Interpretability analysis based on odds ratios highlights the contribution of pose features and demographic variables to prediction outcomes.
Benchmarking Pseudo-Mask Generation Methods for ResNet34-U-Net Acne Segmentation Nikita Amelia Valencia; Puguh Hiskiawan
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13568

Abstract

Acne lesion segmentation is essential for automated dermatological image analysis. However, developing accurate deep learning segmentation models requires pixel-level annotations, which are costly and time-consuming to obtain. To address this limitation, pseudo-mask generation methods can be utilized to produce surrogate labels for training segmentation networks without manual annotation. This study presents a comparative evaluation of five pseudo-mask generation methods for acne lesion segmentation, namely Contour-Based, Superpixel (SLIC), K-Means, Weakly Supervised Semantic Segmentation (WSSS) based on Otsu Thresholding and Morphological Operations, and Pseudo-Mask-Based Generation. The generated pseudo masks were employed as supervisory labels to train a ResNet34-U-Net segmentation model under identical experimental settings. To improve the robustness of the training process, data augmentation was applied exclusively to the training dataset. Segmentation performance was quantitatively evaluated using Dice Score, Intersection over Union (IoU), Precision, Recall and Validation Loss, and qualitatively assessed through visual comparisons of the generated pseudo masks and predicted segmentation results. The experimental results demonstrate that pseudo-mask quality has a substantial impact on segmentation performance. Among the evaluated methods, Superpixel (SLIC) achieved the highest performance with a Dice Score of 0.898, an IoU of 0.815, a Precision of 0.889, a Recall of 0.908, and the lowest validation loss of 0.268 indicating superior lesion boundary preservation and region consistency. WSSS (Otsu + Morphology) also produced competitive results, whereas Pseudo-Mask-Based and Contour-Based methods yielded comparatively lower performance. These findings demonstrate that high-quality pseudo masks can provide effective supervision for ResNet34-U-Net training, offering an annotation-efficient approach for acne lesion segmentation and providing practical insights into selecting suitable pseudo-mask generation methods for dermatological image analysis.
Co-Authors A.A. Ketut Agung Cahyawan W Adeline Yolanda C. Cristie Agustian Sukarno, Ferry Agustinus Fritz Wijaya Agustinus, Khemal Artoto Arkundato Budidarma, Ni Gusti A. A. Evangelina Chandra, Earlene Aprilie Christina, Livia Daniel Fernando Nainggolan Evasaria Magdalena Sipayung Everlin, Shierly Fitri Rizqi Azizah Geasela, Yemima Geasela, Yemima Monica Himmah Khasanah Illavi Praseti Pebrian Kezia Josephine Kristina, Jatim Merryana Lestari Metta Gautama Nikita Amelia Valencia Nurul Piyantari Puspita, Maria Entina Rano Rano, Rano Reni Agustiani, Reni Ruliyanti Ruliyanti Srava Chrisdes Antoro Sri Hartanto Supriyadi Supriyadi Supriyadi Supriyadi Teguh Santoso Theresia Puspa Wijayanti Tio Jansel, Louis Feliepe Tipani Tio, Cynthia Vicky, Vicky William, Jovan Winson Winson Yemima Monica Geasela Yudhistira Adi Nugraha