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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 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

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Towards intelligent post-quantum security: a machine learning approach to FrodoKEM, Falcon, and SIKE Akrom, Muhamad; Setiadi, De Rosal Ignatius Moses
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12865

Abstract

The rapid advancement of quantum computing poses a substantial threat to classical cryptographic systems, accelerating the global shift toward post-quantum cryptography (PQC). Despite their theoretical robustness, practical deployment of PQC algorithms remains hindered by challenges such as computational overhead, side-channel vulnerabilities, and poor adaptability to dynamic environments. This study integrates machine learning (ML) techniques to enhance three representative PQC algorithms: FrodoKEM, Falcon, and Supersingular Isogeny Key Encapsulation (SIKE). ML is employed for four key purposes: performance optimization through Bayesian and evolutionary parameter tuning; real-time side-channel leakage detection using deep learning models; dynamic algorithm switching based on runtime conditions using reinforcement learning; and cryptographic forensics through anomaly detection on vulnerable implementations. Experimental results demonstrate up to 23.6% reduction in key generation time, over 96% accuracy in side-channel detection, and significant gains in adaptability and leakage resilience. ML models also identified predictive patterns of cryptographic fragility in the now-broken SIKE protocol. These findings confirm that machine learning augments performance and security and enables intelligent and adaptive cryptographic infrastructures for the post-quantum era.
Unmasking effects of feature selection and SMOTE-Tomek in tree-based random forest for scorch occurrence detection Dumebi Okpor, Margaret; Eluemnor Anazia, Kizito; Adigwe, Wilfred; Abugor Okpako, Ejaita; Moses Setiadi, De Rosal Ignatius; Adimabua Ojugo, Arnold; Omoruwou, Felix; Erhovwo Ako, Rita; Ochuko Geteloma, Victor; Valentine Ugbotu, Eferhire; Chukwudi Aghaunor, Tabitha; Enadona Oweimeito, Amanda
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8901

Abstract

Scorch occurrence during the production of flexible polyurethane foam has been a menace that consistently, jeopardize a foam’s integrity and resilience. It leads to foam suppression and compactness integrity failure due to scorch. There is always the increased likelihood of scorching, and makes crucial the utilization of methods that seek to avert it. Studies predict that the formation of foam constituent processes via optimization using machine learning have adequately trained models to effectively identify scorch occurrence during the profiling in the polyurethane foam production. Our study utilizes the random forest (RF) ensemble with feature selection (FS) and data balancing technique to identify production predictors. Study yields accuracy of 0.9998 with F1-score of 0.9819. Model yields 2-distinct cases for (non)-occurrence of scorch respectively, and the ensemble demonstrates that it can effectively and efficiently predict the occurrence of scorch in the production of flexible polyurethane foam manufacturing process.
DIGITAL SIGNATURE PADA CITRA MENGGUNAKAN RSA DAN VIGENERE CIPHER BERBASIS MD5 Handoko, Lekso Budi; Umam, Chaerul; Setiadi, De Rosal Ignatius Moses; Rachmawanto, Eko Hari
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v10i1.2212

Abstract

Salah satu teknik yang populer untuk mengamankan data dengan tingkat keamanan yang tinggi yaitu kriptografi. Berbagai penelitian telah dilakukan dengan menggabungkan kunci simteris dan kunci asimteris untuk mendapatkan keamanan ganda. Dalam makalah ini, tanda tangan digital diterapkan melalui Rivest Shamir Adleman (RSA) sebagai algoritma kunci asimteris yang akan digabung dengan algoritma kunci simteris Vigenere Cipher. RSA yang tahan terhadap serangan karena menggunakan proses eksponensial dan kuadrat besar dapat menutupi kelemahan Vigenere Cipher, sedangkan Vigenere Cipher dapat mencegah kemunculan huruf yang sama dalam cipher yang mempunyai pola tertentu. Vigenere cipher mudah diimplementasikan dan menggunakan operasi substitusi. Untuk mengkompresi nilai numerik yang dihasilkan secara acak, digunakan fungsi hash yaitu Message Digest 5 (MD5). percobaan dalam makalah ini telah memberikan kontribusi dalam peningkatan kualitas enkripsi dimana citra digital dioperasikan dengan MD5 yang kemudian hasilnya akan diubah menjadi RSA. Fungsi hash awal yaitu 32 karakter diubah menjadi 16 karakter yang akan menjadi inputan untuk proses RSA dan Vigenere Cipher. Pada citra berwarna yang digunakan sebagai media operasi, akan dilakukan pengecekan apakah citra tersebut sudah melalui proses digital signature
HELM PINTAR BERBASIS ARDUINO PRO MINI UNTUK MENDETEKSI KECELAKAAN Agustina, Feri; Syahputra, Zulfikar Adi; Moses Setiadi, De Rosal Ignatius
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 2 (2020): JURNAL SIMETRIS VOLUME 11 NO 2 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i2.5414

Abstract

Helm merupakan salah satu atribut yang wajib digunakan saat berkendara dengan sepeda motor. Helm berfungsi untuk melindungi kepala dari benturan saat terjadi kecelakaan. Insiden kecelakaan kendaaran bermotor banyak didominasi oleh kendaraan roda dua, dimana pada kasus tertentu dapat dimungkinan korban tidak membawa surat identitas maupun bisa melewati area yang sangat sepi, sehingga sulit dilakukan pertolongan pertama dan identifikasi korban. Penilitian ini bertujuan untuk membuat terobosan baru yaitu menciptakan helm pintar. Helm ini ditambahkan perangkat pintar yang disematkan pada spoiler helm, tujuannya untuk mengirimkan pesan beserta titik lokasi tempat kecelakaan. Perangkat pintar yang disematkan pada spoiler helm dibangun berbasis Arduino pro mini yang dipadukan dengan perangkat GPS, sensor kemiringan untuk mendeteksi kecelakaan, dan modul SIM  800L untuk mengirim notifikasi berupa SMS. Perangkat pintar juga dilengkapi dengan saklar untuk mematikan dan menghidupkan sistem. Berdasarkan hail pengujian Helm pintar sudah dapat bekerja dengan baik, dengan pemicu terjadinya kemiringan sebesar 180° modul SIM 800L dapat mengirimkan pesan berupa titik koordinat yang valid dan dapat dibuka langsung menggunakan google maps. 
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua; Pribadi, Octara; Kartikadarma , Etika; Setyoko, Bimo Haryo; Widiono, Suyud; Robet, Robet; Aghaunor, Tabitha Chukwudi; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
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.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
KOMBINASI DCT DAN BEAUFORT CHIPER UNTUK PENINGKATAN KEAMANAN HAK CIPTA CITRA DIGITAL Setiadi, De Rosal Ignatius Moses; Jatmoko, Cahaya; Rachmawanto, Eko Hari; Sari, Christy Atika
JST (Jurnal Sains dan Teknologi) Vol. 7 No. 2 (2018)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v7i2.13795

Abstract

Informasi penting seperti hak cipta tentunya perlu diamankan, terlebih saat era digital saat ini yang semakin canggih. Pengamanan informasi dapat dilakukan dengan teknik kriptografi atau penyandian. Sedangkan untuk pengamanan hak cipta dapat dilakukan dengan teknik watermarking. Penelitian ini mengkombinasi teknik kriptografi dan watermarking. Sebelum watermark disisipkan watermark disandikan terlebih dahulu. Metode watermarking yang diusulkan adalah DCT dan metode kriptografi yang diusulkan adalah Beaufort cipher. DCT dipilih karena merupakan transformasi domain yang tahan terhadap macam-macam manipulasi, cukup ringan dalam kalkulasi dan menghasilkan watermarking yang impercept. Sedangkan Beaufort cipher merupakan algoritma yang sederhana tapi sangat aman untuk pengamanan data. Alat ukur yang digunakan  dalam eksperimen adalah SSIM, CC dan analisis histogram. Berdasarkan pengukuran terhadap hasil eksperimen dari metode yang diusulkan didapatkan hasil watermarking yang tahan terhadap serangan, impercept, dan aman.
Deteksi Tumor Otak Dengan Metode Convolutional Neural Network Dwi, Bernadetta Sri Endah; Setiadi, De Rosal Ignatius Moses
Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.971

Abstract

Tumor otak merupakan salah satu penyakit mematikan di dunia. Menurut data Global Cancer Observatory, kasus tumor otak di Indonesia pada tahun 2021 mencapai 5.964 kasus serta tingkat kematian berada pada posisi 12 dengan 5298 kasus. Diagnosa cepat dan lebih dini tentu akan mampu menekan tingkat kematian tumor otak. Penelitian ini mengusulkan metode Convolutional Neural Network (CNN) untuk deteksi otak berdasarkan pencitraan medis. Model CNN didesain secara khusus terdiri dari 14 layer. Berdasarkan hasil pengujian model CNN yang dihasilkan memiliki akurasi tinggi yaitu 99%. Selain itu berdasarkan hasil komparasi dengan dataset yang sama, model yang diusulkan 5% lebih unggul dari metode sebelumnya yang menggunakan pre-trained model MobileNetV2.
RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1529

Abstract

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.
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'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