Article Per Year (5 Year)
p-Index From 2021 - 2026
6.275
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Teknologi dan Sistem Komputer SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Fuzzy Systems and Control (JFSC) Journal of Information System and Application Development Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
De Rosal Ignatius Moses Setiadi
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

Published : 114 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 114 Documents
Search

 8   9   10   11   12 
Comparative No-Reference Evaluation of Classical Image Sharpening Techniques under Varying Degradation Conditions Santoso, Siane; Setiadi, De Rosal Ignatius Moses; Pramunendar, Ricardus Anggi
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11430

Abstract

This research conducts a comparative evaluation of four image sharpening methods: Unsharp Masking, Laplacian of Gaussian, High-Boost Filtering, and Adaptive High-Boost Filtering. These methods are tested on low-contrast, blurred, normal, and high-contrast images. The assessment uses No Reference Image Quality Assessment metrics, specifically BRISQUE and NIQE, along with intensity histogram analysis and visual inspection. Results show that High-Boost Filtering improves global contrast, reducing BRISQUE scores to 26.28 for low-contrast images and 27.56 for high-contrast images, although it can cause halo artifacts. Unsharp Masking performs best on blurred images, lowering BRISQUE to 26.65, but it is more sensitive to noise. The Laplacian of Gaussian yields relatively low NIQE scores, such as 3.04 in low-contrast and 3.10 in high-contrast images; however, its output often appears coarse in texture. Adaptive High-Boost Filtering performs best on normal images, achieving a BRISQUE score of 11.89, but shows limited improvement in other cases. Notably, alignment between NIQE scores and perceptual evaluation is only observed in high-contrast images. These results confirm that no single technique is universally optimal, emphasizing the importance of selecting sharpening methods based on specific image degradation characteristics. Additionally, this observation highlights that BRISQUE more reliably reflects perceived image quality, whereas NIQE occasionally diverges from subjective judgments.
Deteksi Stres Berbasis Teks pada Dreaddit Menggunakan Fine Tuning DeBERTa-v3 Ramadhan, Pramudia; Setiadi, De Rosal Ignatius Moses
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9318

Abstract

Mental health has become an important issue in the digital era, as psychological expressions are increasingly reflected through social media posts such as Reddit. This study uses the publicly available Dreaddit dataset containing Reddit user texts labeled with stress categories. The objective is to compare two text-based stress detection approaches: fine-tuning the transformer model DeBERTa-v3 and the classical TF-IDF LinearSVC method. Both approaches are implemented as binary classification systems to automatically distinguish stress and non-stress texts. The research workflow includes data preprocessing, tokenization, model training, validation, and evaluation using Accuracy, Precision, Recall, F1-score, and AUROC metrics. DeBERTa-v3 is fine-tuned using contextual representations with a self-attention mechanism, while TF-IDF LinearSVC relies on statistical n-gram weighting. Experimental results show that DeBERTa-v3 achieves superior performance with an Accuracy of 0.830, Precision of 0.802, Recall of 0.889, F1-score of 0.843, and AUROC of 0.918. Meanwhile, TF-IDF LinearSVC obtains an Accuracy of 0.732, Precision of 0.722, Recall of 0.783, F1-score of 0.751, and AUROC of 0.817. The experiments were conducted with consistent training configurations, data splits, and evaluation procedures to ensure a fair comparison. The confusion matrix analysis indicates that DeBERTa-v3 produces fewer false positives and false negatives, demonstrating stronger capability in recognizing implicit stress expressions. These findings highlight the advantages of transformer-based models in capturing emotional and semantic context and indicate the potential for real-time deployment in social-media-based mental health monitoring systems.
Enhancing Aspect-Based Sentiment Analysis via Hugging Face Fine-Tuned IndoBERT Aprilah, Thania; Setiadi, De Rosal Ignatius Moses; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11409

Abstract

Aspect-Based Sentiment Analysis (ABSA) on hotel reviews faces significant challenges regarding semantic complexity and severe class imbalance, particularly in low-resource languages like Indonesian. This study evaluates the effectiveness of fine-tuning IndoBERT, a pre-trained Transformer model, to address these issues by benchmarking it against classical statistical methods (TF-IDF) and static embeddings (Sentence-BERT). Utilizing the HoASA dataset, the experiment implements a Random Oversampling strategy at the text level to mitigate data sparsity in minority classes. Empirical results demonstrate that the fine-tuned IndoBERT significantly outperforms baselines on the majority of aspects, achieving a global accuracy of 97% and macro F1-score of 0.92. Granular per-aspect analysis reveals that the model’s self-attention mechanism captures linguistic context robustly in tangible aspects (e.g., wifi, service), yet faces persistent challenges in highly ambiguous aspects such as smell (bau) and general. Statistical significance tests (Paired t-test and Wilcoxon) confirm that the performance gains over baselines are statistically significant (p < 0.05) and not due to random chance. The study concludes that leveraging contextual representations from IndoBERT, combined with data balancing strategies, offers a superior and statistically robust solution for handling linguistic variations and class bias in the Indonesian hospitality domain.
Bi-LSTM with Explainable AI for Session Duration-Based Customer Lifetime Value Proxy on Multi-Category E-Commerce Platforms Nartriani, Yulian Dwi; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11578

Abstract

The rapid growth of multi-category e-commerce platforms has increased the importance of behavioral data for predicting Customer Lifetime Value (CLV). However, monetary-based CLV estimation is often infeasible due to incomplete or unavailable transaction records. This study adopts session duration as a short-term behavioral proxy for CLV and proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) model enhanced with a Temporal Attention mechanism to improve predictive accuracy. The publicly available REES46 dataset, consisting of 1,6 million events and 276.000 unique sessions, is used with preprocessing steps including label encoding, temporal feature construction, and outlier-aware sampling to address the highly right-skewed distribution of session durations. Four baseline models Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and conventional Long Short-Term Memory (LSTM) are implemented for comparative evaluation. The baseline LSTM achieves MAE = 0,0080 and RMSE = 0,0322. The proposed Bi-LSTM v3 model, equipped with Temporal Attention and structured sampling, demonstrates substantial performance improvement, achieving MAE = 0,0043 (≈368 seconds) and RMSE = 0,0172 (≈1466 seconds), representing an accuracy gain of approximately 45–50% over the baseline. Explainability analysis using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) confirms that the time_diff feature is the dominant contributor at both global and local levels, aligning with the behavior of the attention mechanism. Additionally, the integration of Explainable Artificial Intelligence (XAI) provides transparent insights into model decision patterns. These findings show that combining Bi-LSTM, Temporal Attention, and XAI yields an accurate and interpretable framework for session duration prediction, supporting the use of session duration as a feasible CLV proxy in multi-category e-commerce environments.
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abere, Reuben Akporube Abugor Okpako, Ejaita Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Adityawan, Harish Trio Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Salafuddin Ajib Susanto Akazue, Maureen Ifeanyi Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Akrom, Muhamad Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Araaf, Mamet Adil Arya Kusuma Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dutta, Pushan Kumar Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Eluemnor Anazia, Kizito Emordi, Frances Uche Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Farah Zakiyah Rahmanti Farooq, Omar Farroq, Omar Ferda Ernawan Firnando, Fadel Muhamad Fittria Shofrotun Ni&#039;mah Florentina Esti Nilawati Florentina Esti Nilawati Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Gomiasti, Fita Sheila Hanny Haryanto Henry Christian Herowati, Wise Heru Agus Santoso Ibnu Gemaputra Ramadhan Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibor, Ayei Egu Ihya Ulumuddin, Dimas Irawan Imanuel Harkespan Imanulloh, Satrio Bagus Indra Gamayanto Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo, Jutono Kartikadarma , Etika Kusuma, Edi Jaya Kusuma, Muh Galuh Surya Putra L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen, Minh T. Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muhamada, Keny Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Mustofa, Fachrul Nartriani, Yulian Dwi Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Nugroho, Sandy Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omoruwou, Felix Pradana, Akbar Ganang Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Nizar Rafi Purnamasari, Devi Rachman, Rahadian Kristiyanto Ramadhan, Pramudia Ricardus Anggi Pramunendar Robet Robet Ruri Suko Basuki Sahu, Aditya Kumar Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Setiawan, Marcell Adi Setyoko, Bimo Haryo Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Sunarjo, Macellino Setyaji Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Valentine Ugbotu, Eferhire Warto - Wellia Shinta Sari Wellia Shinta Sari Wibowo, Mochammad Abdurrochman Ari Wijaya, Nantalira Niar Wijayanti, Ella Budi Yoro, Rume Elizabeth Yusianto Rindra Zuama, Leygian Reyhan