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Arief Setyanto

Universitas Amikom Yogyakarta

Author-ID : 2484041

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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Federated Learning and Deep Reinforcement Learning Synergy: Opportunities for Multi-Cloud Serverless Deployment I Gusti Ngurah Wikranta Arsa Arsa; Arief Setyanto; Andi Sunyoto; Alva Hendi Muhammad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2694

The Development of distributed computing has enabled the use of multi-cloud and serverless computing, which are beneficial due to their flexibility, scalability, and cost efficiency. There are, of course, pertinent challenges associated with these computing paradigms, such as resource heterogeneity, cold-start latency, vendor lock-in, and privacy. Recent trends in Federated Learning (FL) and Deep Reinforcement Learning (DRL) hold promise in solving these issues. FL systems enable decentralised, privacy-preserving model training across heterogeneous systems, while DRL systems enable adaptive models for real-time decision-making to optimise system resources and improve performance. This Systematic Literature Review (SLR) covers the years 2020 to early 2026 and examines the intersection of FL and DRL in multi cloud serverless computing, following the PRISMA methodology. A primary analysis of 50 quality studies was undertaken to answer four privacy-related resource management questions. The results showed FL improves privacy and scalability using decentralised training. Consolidating the Federated DRL and Multi-Agent stacks enhances the system by achieving a better trade-off and optimization among latency, energy, and operational efficiency. However, a few gaps still exist, such as the absence of a more holistic framework, elusiveness in cross-system integration and collaboration, and a lack of concrete real-world applications. More work is needed to build a cohesive Federated Learning framework to improve sustainability and security in the multi-cloud, serverless systems of the future. This examination provides a solid foundation for the Development of innovative, privacy-preserving, and dynamic resource management in future cloud computing environments.

EMOGRAM-CNN: A Gram-Correlation Enhanced Multi-Kernel Convolutional Network for Text Emotion Recognition Marselina Endah Hiswati; Ema Utami; Kusrini Kusrini; Arief Setyanto
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3043

Deep neural architectures have demonstrated substantial capability for handling temporal and sequential data; however, most recurrent-based models, such as LSTM, BiLSTM, GRU, and BiGRU, remain computationally expensive and prone to overfitting. This study proposes and evaluates the EMOGRAM-CNN model, a convolutional neural architecture enhanced with Gram-matrix feature correlation, to improve feature representation in temporal classification tasks. Model performance was compared with conventional CNNs and recurrent architectures on a balanced six-class dataset comprising 17,967 samples. Experimental results show that EMOGRAM-CNN achieved the highest classification accuracy of 94.48%, outperforming CNN (94.00%), GRU (92.00%), BiGRU (91.00%), BiLSTM (91.00%), and LSTM (90.00%). The model converged faster, with smoother loss behavior and lower validation error, indicating superior stability and generalization. The Gram-based correlation layer effectively preserved second-order dependencies across feature maps, enabling the network to capture both local and global temporal relationships without recurrent connections. These findings confirm that EMOGRAM-CNN offers a robust, computationally efficient alternative to recurrent deep networks for sequence classification.

Evaluating Cross-Language Structural Generalization of the Unified Abstract Syntax Tree Mardi Utomo; Ema Utami; Kusrini; Arief Setyanto
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3191

Cross-language code analysis requires syntax-aware representations that can reduce language-specific syntactic variation while preserving comparable program structure. Although unified Abstract Syntax Tree (AST) representations have been proposed, empirical evidence on their representation-level structural behavior across datasets and programming languages remains limited. This paper evaluates the structural generalization of a Unified AST representation as a schema-level abstraction, not as a parser-free or full semantic equivalence mechanism. The Unified AST schema is constructed from the CodeXGLUE code-to-text dataset covering Python, PHP, Ruby, Java, JavaScript, and Go. Its generalization is then examined on function-level aligned benchmarks from CodeXGLUE code-to-code translation (Java-C#) and multilingual HumanEval (Java, JavaScript, Go, Python, C++, and Rust). Tree Edit Distance (TED) similarity is used as the primary structural metric, while cosine similarity, BLEU, compression ratio, and identifier precision-recall are treated as auxiliary indicators of lexical similarity, reconstruction fidelity, compactness, and identifier retention. The results show an average TED similarity of 0.77 on CodeXGLUE code-to-code translation and 0.60 on HumanEval. These findings indicate that the Unified AST can preserve cross-language structural patterns under aligned benchmark assumptions, although it does not prove behavioral equivalence and remains dependent on language-specific parsing during AST extraction

Optimizing Acoustic Fingerprinting for Synchronized Audio Binary Matching Andi Bahtiar Semma; Kusrini Kusrini; Arief Setyanto; Bruno da Silva; An Braeken
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2744

As interconnected devices proliferate, secure and efficient pairing methods are critical. Environmental acoustic signals offer a promising solution, but their effectiveness depends on robust audio features that perform well across varying conditions. This study investigates optimal audio features for fingerprinting, focusing on synchronized audio in time-frequency domains. Six diverse datasets were collected across controlled environments to simulate real-world scenarios. Thirteen audio features were extracted and analyzed for robustness across distances, devices, scenes, and sample lengths. Cosine similarity assessed consistency, while the Youden index determined thresholds. The Mel Spectrogram, particularly with 5-second samples, achieved an AUC of 0.8758 and a J-statistic of 0.7278. Augmenting it with Tonnetz and spectral bandwidth yielded the highest performance (AUC: 0.9346, J-statistic: 0.7955, Accuracy: 0.8320, recall: 0.9648), demonstrating the potential of combining robust base features with complementary acoustic characteristics for reliable device pairing.

A comparative study of mango fruit pest and disease recognition Kusrini Kusrini; Suputa Suputa; Arief Setyanto; I Made Artha Agastya; Herlambang Priantoro; Sofyan Pariyasto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.21783

Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.

Comparative Analysis of Machine Learning-Based Software Defect Prediction in Object-Oriented and Structured Paradigms Using Apache Camel and Redis Datasets Nasiri, Asro; Setyanto, Arief; Utami, Ema; Kusrini, Kusrini
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5315

Software Defect Prediction (SDP) is a crucial component of software engineering aimed at improving quality and testing efficiency. However, the majority of SDP research often overlooks the fundamental influence of the programming paradigm on the nature and causes of defects. This study presents a comparative analysis to identify the most influential software metrics for predicting defects across two distinct paradigms: Object-Oriented (OOP) and Structured. To ensure modern relevance and reproducibility, we constructed two new datasets from large-scale, open-source projects: Apache Camel (Java) for OOP and Redis (C) for Structured which exhibited realistic defect rates of 14.4% and 21.8%, respectively. The dataset creation process involved mining Git repositories for defect labeling and automated metric extraction using the CK and Lizard tools. Correlation analysis and baseline modeling using Random Forest revealed significant differences between the paradigms. In the OOP system, dominant defect predictors were related to the complexity of the class interface and features (e.g., uniqueWordsQty, totalMethodsQty, WMC, CBO). Conversely, defects in the structured system were strongly correlated with size and algorithmic complexity (e.g., file_tokens, file_loc, file_ccn_sum). Although the baseline models performed well (ROC–AUC = 0.82–0.87), the significant class imbalance resulted in low recall (44–50%). This motivates the need for more context aware approaches. These findings underscore that effective SDP strategies must be tailored to the underlying programming paradigm.

Temporal Gradient Oscillation with Accuracy Recovery Mechanism for Efficient BERT-Based Text Classification Indra Listiawan; Ema Utami; Kusrini Kusrini; Arief Setyanto
JET (Journal of Electrical Technology) Vol 11, No 2 (2026): : Edisi June
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i2.13472

Large Language Models (LLMs) such as BERT have demonstrated impressive performance across various NLP tasks, yet their high computational cost poses challenges for deployment in resource-constrained environments. This paper proposes a dynamic temporal token pruning approach based on gradient oscillation monitoring, where token importance is estimated from the temporal variability of gradient signals during training. Tokens exhibiting low gradient oscillation are selectively pruned to reduce effective input length. To mitigate potential performance degradation caused by aggressive pruning, an accuracy recovery mechanism based on lightweight re-finetuning is introduced. Experimental results on benchmark sentiment classification datasets, including IMDB and SST-2, demonstrate that the proposed method substantially reduces the number of input tokens while maintaining or recovering predictive performance. These results indicate that gradient oscillation provides a viable signal for token-level efficiency, achieving a favorable trade-off between input sparsity and model accuracy without modifying the underlying Transformer architecture.

OPTIMIZATION OF SOFTWARE DEFECT PREDICTION USING CNN AND ADABOOST: ANALYSIS AND EVALUATION Muhammad Abdul Basit; Arief Setyanto; Tonny Hidayat
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6405

This study focuses on enhancing software defect prediction (SDP) by integrating Convolutional Neural Networks (CNN) with the AdaBoost algorithm. The PROMISE dataset was employed in this research, and data balancing was achieved using the SMOTE Tomek technique. With the help of AdaBoost, we were able to increase the prediction accuracy after building a complex CNN model to extract features from the da-taset. The AdaBoost model's hyperparameters were fine-tuned using GridSearch to find the best values for enhanced model performance. For the studies, we used StandardScaler to normalize the data after splitting it into training and testing groups with an 80:20 ratio. The ex-perimental results show that compared to the baseline method, SDP's accuracy is significantly improved when CNN, AdaBoost, and GridSearch hyperparameter tweaking are used together. Accuracy, pre-cision, recall, F1 score, MCC, and AUC were some of the measures used to assess the model's performance.

Klasifikasi Radio Siaran FM Berdasarkan Data IQ Menggunakan Convolutional Neural Networks Agus Sukarno; Arief Setyanto; Asro Nasiri
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.1978

Pengawasan spektrum siaran FM secara real-time memerlukan teknik canggih, pendekatan berbasis klasifikasi sinyal telah terbukti meningkatkan ketepatan deteksi dibandingkan metode manual. Penelitian ini mengembangkan dan membandingkan tiga arsitektur deep learning - CNN 5-Layers, CNN-BiLSTM, dan CNN-Transformer - untuk mengklasifikasikan pengguna siaran radio FM berdasarkan data IQ. Data sinyal dikumpulkan dari 16 pemancar FM menggunakan SDR dan diolah menjadi 80.000 sampel seimbang. Model-model ini dievaluasi berdasarkan akurasi klasifikasi dan waktu inferensi. Hasil eksperimen menunjukkan bahwa CNN-BiLSTM memberikan akurasi tertinggi sebesar 98,96% namun dengan waktu inferensi relatif lama sekitar 62 detik. Sementara itu, CNN 5-Layers memiliki waktu klasifikasi tercepat sekitar 10 detik dengan akurasi tinggi sebesar 98,18%, dan CNN-Transformer paling lambat sekitar 120 detik dengan akurasi sebesar 97,72%. Mengingat waktu klasifikasi per batch harus lebih pendek daripada laju pengambilan data sekitar 2 ms per sampel, hanya CNN 5-Layers yang memenuhi persyaratan pemantauan spektrum secara real-time.

Co-Authors (Menunda Publikasi) Abdillah, M A Agastya, I Made Artha Agung Budi Prastyo Agung Nugroho Agung, Kris Agus Sukarno Agus Tumulyadi Agustina Rahmawati Ahmad Afief Amrullah Ahmad Afief Amrullah Ahmad Naufal Labiib Nabhaan Ahmad Tantoni Ainul Yaqin Al Maky, Nuril Huda Aliyah, Nada Rahma Alva Hendi Muhammad Amanda Rifan Fathoni Amir Fatah Sofyan Amiruddin Khairul Huda Ammara, Laya Amrullah, Ahmad Afief An Braeken Anam, M. Choirul Anang Anang Andi Bahtiar Semma Andi Kriswantono Andi Sunyoto Andik Isdianto Anggit Dwi Hartanto Anggit Hartanto annisa gatri zakinah Annisa Gatri Zakinah Anthon Andrimida, Anthon Anton, Tri Arbiansyah, Moh Junit Arief Maehendrayuga Ariefandi, Muhammad Fikri Asadi, M. Arif Askar, Muhammad Ichfan Asmirijal, Amrey Syahnur Asro Nasiri Asro Nasiri Asro Nasiri Astika Wulansari Astuti , Septiana Sri Atmaja, Albertus Aldo Danar Atminenggar, Alinda Najma Aulia Lanudia Fathah Béjar, Rodrigo Martínez Berlania Mahardika Putri Bruno da Silva Constantin Menteng Daduk Setyohadi Darmawan Ockto Sutjipto Dedi Tri Hermanto Desy Bawan, Sarah Bunda Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya, Dewa Gede DHANI ARIATMANTO Dhea, Luthfia Ayu Dhiana Puspitawati Diah, M. Dian Rusvinasari Dinar Mustofa Dwi Satrio Anurogo Eko Pramono Eko Pramono Eko Pramono Ema Utami Emha Taufiq Luthfi F Purwanto Fadjeri, Akhmad Fathah, Aulia Lanudia Fazlul Rahman Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fiqih Akbari Gatut Bintoro Gibran, Ibrahim El Gibran, Khalil Ginting, Meliani Ananda Br. Gunawan Wicahyono Hadin La Ariandi Hadiyah, Lisa Nur Hafidz Sanjaya, Hafidz Hamdallah, Dika Puja Hamdikatama, Bimantyoso Hamka Suyuti Hamzah Hamzah Hanif Al Fatta Hanif Al Fattah Hanifa Ramadhani Hari Susanto Harlyan, Ledhyane Eka Henderi . Hendi Muhammad, Alva Heri Sismoro Herlambang Priantoro Hidayat, Aji Said Wahyudi Hidayat, Kardilah Rohmat Hizbul Izzi I Gusti Ngurah Wikranta Arsa Arsa I Made Adi Purwantara I Made Artha Agastya Ilham Mubarog Imam Syafii Imam Syafii Imam Thoib Indra Listiawan Irianies Cahya Gozali Irwan Jatmiko Ishaq, Syafrial Yanuar Jimmy H Moedjahedy José Ramón Martínez Salio Kamila, Firda Nikmatul Karaman, Jamilah Kartikasari, Wahida Khairan marzuki Khasanah, Nabiila Rizqi Kholida Zia Abidin Komang Aryasa Kris Agung Kudrati, Amelinda Vivian Kumara Ari Yuana Kumoro, Danang Tejo Kusnawi Kusnawi Kusrini López, Alba Puelles M. Diah M. Rudyanto Arief M. RUDYANTO ARIEF Mardi Utomo Mardya Hayati Marsela, Kristina Marselina Endah Hiswati Martiani, Evi Martínez-Béjar, Rodrigo MEI PARWANTO KURNIAWAN Miftahul Madani Mohamad Syafri Lamato Morita Puspita Sari Muchamad Zainul Muhamad Maksum Hidayat Muhammad Abdul Basit Muhammad Arif Asadi, Muhammad Arif Muhammad Arif Rahman Muhammad Azmi Muhammad Ghozaly Salim Muhammad Javier Irsyad Muhammad Reza Muhammad Reza Riansyah Muhammad Yusuf Munandar, Arief Muqorobin Muqorobin Nabhaan, Ahmad Naufal Labiib Nabilla, Azma Salma Nadea Cipta Laksmita Nasiri, Asro Naufal Hilda Bahtiar nfn Sarip Nggego, Dedy Abdianto Ni Nyoman Utami Januhari, Ni Nyoman Nico Rahman Caesar Nila Feby Puspitasari, Nila Feby Nina Kurnia Hikmawati Nisrina, Aliyya Nizery, Sefhanissa Puspa Retno Nuddin Harahab Nugroho, Agung Nur Khamidah oktiyas muzaky Luthfi, oktiyas muzaky Pahlawan, Muammar Reza Pangestu, Wanda Suryani Pattisahusiwa, Annisa Shafira P. Prayoghi, M. Lukman Publikasi), (Menunda Putra, Muhammad Naufal Eka Putri, Berlania Mahardika Rachmanto, Rakandhiya Daanii Rafif Zul Fahmi Rahmad Arif Setiawan Rahman, Aulia Tegar Rahmat Taufik R.L Bau Rakandhiya Daanii Rachmanto Ramdhani, Mohamad Dhicy Rarasrum Dyah Kasitowati Ratno Kustiawan Ria Andriani Ripto Sudiyarno Rismayani Rismayani Roni Sasongko Rudyanto Arief Sadikin, Moh. Fal Samuel, Pratama Diffi San Sudirman Saputra, Tedy Eko Sarip, nfn Seniwati, Erni Septiansyah, Moch. Rafli Shahruri, Rifandi Annas Simone Martin Marotta Siswo Utomo, Mardi Siti Alvi Sholikhatin Siti Halimah Soejono, Ajie Wibowo Sofyan Pariyasto Sriyati Sriyati Stephan Adriansyah Hulukati Suardi, Heri Sucianingsih, Ni Komang Diah Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Suhardi Aras Sukoco Sunardi Sunardi Supriyadi Supriyadi Supriyadi Supriyadi Suputa Suputa Suwanto Raharjo Suyadi Suyadi Suyuti, Hamka Syarief, Salsabila Nazmie Putri TONNY HIDAYAT Totok Wahyu Caturiyanto Tri Djoko Lelono Tumulyadi, Agus Tyas, Herlin Widi Aning Utama, Andria Ansri Veithzal Rivai Zainal Wahyu Nugroho Widhiarta, Widhiarta Wijaya, Sony Yasmin, Delviega Aisyah Yeni Kartika Sari, Yeni Kartika Yorarizka, Putri Devi Yuliana Yuliana Yumna, Orryza Nayla Zul Hisyam

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