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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 BERNAS: Jurnal Pengabdian Kepada 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
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Journal : journal of applied informatics and computing

 1 
Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases Afridiansyah, Rahmanda; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8677

Abstract

The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.
Model Pembelajaran Mesin untuk Deteksi Penipuan Kartu Kredit yang Dioptimalkan Menggunakan SMOTE-Tomek dan Rekayasa Fitur Wibowo, Mochammad Abdurrochman Ari; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8732

Abstract

In today's digital economy, credit cards are essential and both credit card usage and theft have increased significantly in recent years. Credit card fraud can be categorized using machine learning models using data from suspicious transaction history. However, credit data is often imbalanced. Therefore, machine learning models are biased towards the majority class resulting in poor performance on publicly accessible Kaggle credit card classification datasets. We balance the class distribution in the dataset using a hybrid synthetic minority oversampling strategy to address this difficulty. The findings show that the random forest machine learning model combined with oversampling techniques combined with feature engineering and cross-validation yields optimal results of more than 99% for all assessment measures. It performs better compared to three other models, namely decision tree, gradient boosting, and XGBoost. It can be concluded that the use of feature engineering, cross-validation, and oversampling are useful approaches to handle imbalanced credit card data and ultimately help in preventing credit card transaction fraud.
Layered Image Encryption Method Based on Combination of Logistic Map, Henon Map, and Sine Map to Enhance Digital Image Security Amir Musthofa; Moses Setiadi, De Rosal Ignatius
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9569

Abstract

In today's digital era, ensuring the confidentiality of image data is crucial due to the widespread use of images in fields such as medical imaging, military communication, and multimedia applications. This study proposes a layered image encryption method by integrating three chaotic systems: Logistic Map, Henon Map, and Sine Map. Each layer in the encryption process applies a different chaotic map to sequentially perform pixel permutation, XOR-based substitution, and modulus-based substitution. Key generation is carried out by producing pseudo-random number sequences derived from the iterations of each chaotic map: the Logistic Map (using specific initial and control parameters), the Henon Map (with two initial condition variables), and the Sine Map (based on a sine function), all of which are highly sensitive to initial conditions and control parameters. These sequences are then used as keys in each encryption stage. The proposed method strengthens the principles of confusion and diffusion, thereby enhancing the security and randomness of the encrypted images. Evaluation was conducted using metrics such as histogram analysis, entropy, chi-square, correlation coefficient, PSNR, and BER. The experimental results demonstrate that the method produces encrypted images with strong statistical characteristics and high resilience against common cryptographic attacks. Thus, this approach makes a significant contribution to the development of secure and efficient image encryption techniques based on chaos theory.
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.
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 Abugor Okpako, Ejaita Aceng Sambas Achmad Nuruddin Safriandono Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Rofiqul Muslikh Ahmad Salafuddin Ajib Susanto Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Arnold Adimabua Ojugo Arya Kusuma Ayu Pertiwi Bimo Haryo Setyoko Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chris Chukwufunaya Odiakaose Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Daniel Nomolas Wicaksono Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Eferhire Valentine Ugbotu Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ella Budi Wijayanti Eluemnor Anazia, Kizito Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Etika Kartikadarma Fachrul Mustofa Farah Zakiyah Rahmanti Farooq, Omar Ferda Ernawan Fidelis Obukohwo Aghware Firnando, Fadel Muhamad Fita Sheila Gomiasti Fittria Shofrotun Ni&#039;mah Florentina Esti Nilawati Florentina Esti Nilawati Frances Uche Emordi Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Hanny Haryanto Harish Trio Adityawan Harun Al Azies Henry Christian Herowati, Wise Heru Agus Santoso Hong-Seng Gan Hussain Md Mehedul Islam Hussain Md Mehedul Islam 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 Imanuel Harkespan Indra Gamayanto Irnanda, Muhammad Diva Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo Kusuma, Edi Jaya L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Macellino Setyaji Sunarjo Mamet Adil Araaf Margaret Dumebi Okpor Maureen Ifeanyi Akazue Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen Mohamad Afendee Mohamed Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muh Galuh Surya Putra Kusuma Muhamad Akrom Muhamad Akrom Muhamada, Keny Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Nantalira Niar Wijaya Nartriani, Yulian Dwi Nizar Rafi Pratama Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omar Farroq Omoruwou, Felix Patrick Ogholuwarami Ejeh Pradana, Akbar Ganang Prajanto Wahyu Adi Pratama, Ananta Surya Purnamasari, Devi Pushan Kumar Dutta Rahadian Kristiyanto Rachman Ramadhan, Pramudia Reuben Akporube Abere Ricardus Anggi Pramunendar Rita Erhovwo Ako Robet Robet Rume Elizabeth Yoro Ruri Suko Basuki Sahu, Aditya Kumar Sandy Nugroho Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Satrio Bagus Imanulloh Setiawan, Marcell Adi Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Suyud Widiono Suyud Widiono Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo T. Sutojo Tabitha Chukwudi Aghaunor Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Umam, Taufiqul Valentine Ugbotu, Eferhire Victor Ochuko Geteloma Warto Wellia Shinta Sari Wellia Shinta Sari Wibowo, Mochammad Abdurrochman Ari Wise Herowati Yusianto Rindra Zuama, Leygian Reyhan