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All Journal Jurnal Ilmu Dasar BERKALA SAINSTEK JURNAL FISIKA Saintifika UNEJ e-Proceeding Panrita Abdi - Jurnal Pengabdian pada Masyarakat 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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Journal : journal of applied informatics and computing

 1 
A Hybrid Data Science Framework for Forecasting Bitcoin Prices using Traditional and AI Models Hiskiawan, Puguh; William, Jovan; Tio Jansel, Louis Feliepe
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10631

Abstract

Bitcoin, a highly volatile and decentralized digital asset, presents considerable challenges for accurate price forecasting. This study proposes an applied data science framework that compares traditional statistical approaches with modern Artificial Intelligence (AI)-based models to predict Bitcoin’s daily closing price. Using BTC-USD historical data from January 2020 to December 2024, we converted prices into Indonesian Rupiah (IDR) to increase local relevance. Our forecasting horizon is 30 days, based on a 60-day lookback window. We evaluate six models: Linear Regression, ARIMA, and Prophet as traditional techniques, alongside Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks as AI approaches. All models were trained using lag-based or sequence-based time series features and evaluated using MAE, RMSE, R², MAPE, and SMAPE. Results show that AI models, particularly LSTM and XGBoost, offer better performance in capturing short-term non-linear dynamics compared to traditional models. LSTM provides high accuracy, though with greater computational demand, while XGBoost strikes a balance between speed and precision. Prophet and ARIMA remain effective for quick and interpretable forecasts but struggle with abrupt trend shift common in cryptocurrency markets. In addition to performance metrics, we include a robustness analysis based on median absolute error and outlier detection to assess model stability under extreme variations. Visual analytics—including forecast curves, error distributions, and uncertainty bounds—help interpret and communicate model behavior. This comprehensive evaluation offers practical insights for investors, analysts, and fintech practitioners, and the pipeline can be extended to other volatile assets.
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.
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 Jean Christopher Allen Kezia Josephine Kristina, Jatim Kunsuswandono Suryo Bagus Sutojo 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 Virananda Iskandar William, Jovan Winson Winson Yemima Monica Geasela Yudhistira Adi Nugraha