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All Journal PROSIDING SEMINAR NASIONAL Autotech: Jurnal Pendidikan Teknik Otomotif Universitas Muhammadiyah Purworejo E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Elkom: Jurnal Elektronika dan Komputer PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MANAJEMEN Jurnal Universal Technic Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Jurnal Publikasi Ilmu Manajemen Jurnal Penelitian Rumpun Ilmu Teknik Jurnal Sains dan Ilmu Terapan Jurnal Sistem Informasi dan Ilmu Komputer Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Elektronika dan Teknik Informatika Terapan Journal of Technology Informatics and Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi International Journal of Computer Technology and Science International Journal of Information Engineering and Science International Journal of Electrical Engineering, Mathematics and Computer Science Jurnal Bengawan Solo: Pusat Kajian Penelitian dan Pengembangan Daerah Kota Surakarta Systematic Literature Review Journal Journal of New Trends in Sciences Router : Jurnal Teknik Informatika dan Terapan Journal of Engineering, Electrical and Informatics Global Science: Journal of Information Technology and Computer Science Jurnal Sistem Informasi dan Ilmu Komputer
Danang Danang
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Evaluating Trust Aware Machine Learning Models for Secure Data Sharing in Distributed Internet of Things and Edge Computing Infrastructures Eko Siswanto; Danang Danang; Sunarmi Sunarmi
International Journal of Computer Technology and Science Vol. 1 No. 1 (2024): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i1.359

Abstract

The rapid growth of Internet of Things (IoT) and edge computing technologies has introduced new security challenges due to the distributed, heterogeneous, and dynamic nature of these environments. Conventional static security mechanisms, such as rulebased authentication and fixed trust models, are often inadequate for addressing evolving threats and abnormal behaviors in largescale IoT systems. To overcome these limitations, this study proposes a machine learningbased trust evaluation framework for enhancing security in distributed IoT environments. The proposed approach dynamically assesses the trustworthiness of IoT nodes by analyzing behavioral and interactionbased features collected at the edge layer. Machine learning models are trained to classify nodes into trusted and malicious categories and continuously update trust values in response to changing network conditions. Based on the predicted trust levels, adaptive security decisions are enforced to allow or restrict node participation in data sharing and computation processes. A quantitative experimental evaluation is conducted using simulated distributed IoT scenarios that include both normal and malicious behaviors. The performance of the proposed framework is evaluated using standard metrics such as accuracy, precision, recall, F1score, and detection effectiveness, and is compared against conventional static trust and rulebased security mechanisms. The results demonstrate that the proposed machine learningbased trust evaluation approach achieves significantly higher detection accuracy and robustness while maintaining low computational overhead. Overall, the findings confirm that integrating machine learning into trust management provides an effective and scalable solution for securing distributed IoT systems under dynamic and adversarial conditions.
Perancangan Tempat Sampah Pintar Berbasis Arduino Uno Fa`iq Khotibul Umam; Nuris Dwi Setiawan; Danang Danang; Mufadhol Mufadhol
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 1 (2024): Februari : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i1.2728

Abstract

The lack of awareness from Resto visitors in the process of disposing of garbage in its place makes the environment around the Resto polluted at least the awareness of Resto visitors to dispose of garbage in its place is still low. The purpose of this research is to create an automation system for the trash can. The problem that arises for Resto S2 is the lack of effectiveness, especially in the tissue waste section, one of the solutions that can be done for these problems, namely by designing a device in the form of a smart trash can that can open and close automatically, so that Resto visitors do not need to make direct contact with the trash can. Researchers aim to realize the design of the tool, as for the method carried out in this study is to implement the design of a smart trash can in the form of a box that has an input in the form of an ultrasonic sensor, and an output in the form of a servo motor. The results of input and output testing show that the ultrasonic sensor can detect movement in front of the trash can and the servo motor can move the trash can cover, so it can be concluded that the smart trash can work system as a whole can function properly in accordance with the design that has been made.
Explainable Clinical Risk Prediction from EHR Tabular Data using Monotonic Constraints and Calibrated Probabilities Danang Danang; Toni Wijanarko Adi Putra
Jurnal Riset Rumpun Seni, Desain dan Media Vol. 2 No. 1 (2023): April : Jurnal Riset Rumpun Seni, Desain dan Media
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrsendem.v2i1.9197

Abstract

Tabular-based clinical risk prediction models are extensively applied in medical decision support systems; however, two major challenges often reduce their reliability: predictions that contradict basic clinical logic and poorly calibrated probability outputs that weaken threshold-based decision making. This study investigates explainable binary risk prediction using the processed Cleveland subset of the UCI Heart Disease dataset as a public clinical benchmark. A lightweight and CPU-efficient pipeline is proposed by employing an XGBoost classifier integrated with monotonic constraints on clinically relevant features, followed by probability calibration through post-hoc methods, including Platt scaling, temperature scaling, and isotonic regression on a separate validation set. Model performance is assessed in terms of discrimination capability using AUROC, AUPRC, F1-score, sensitivity, and specificity, while probability reliability is evaluated using ECE and Brier score metrics. A monotonicity audit is also conducted through counterfactual feature sweeps to measure violation rates. In addition, the model is applied for risk stratification into low-, medium-, and high-risk categories with corresponding event-rate reporting. The findings demonstrate that isotonic regression improves probability reliability without degrading discrimination performance. Furthermore, the monotonicity audit reveals no observed violations for constrained features. Overall, the integration of monotonic constraints and probability calibration produces more decision-ready risk estimates for threshold-based clinical decision support while maintaining transparency through SHAP-based analysis.
Co-Authors Agus Priyadi Agustinus Budi Santoso Ahmad Ashifuddin Aqham Ahmad Zainudin Aldo Raditya Pangestu Andik Prakasa Hadi Arie Atwa Magriyanti Budi Santoso Dani Sasmoko Daniel Rudjiono Dendy Kurniawan Dewi Retno Putri Eka Satria Wibawa Ekky Fredyan Eko Siswanto Eko Siswanto Eni Muhadi Fa`iq Khotibul Umam Febri Adi Prasetya Febryantahanuji Febryantahanuji Greget Widhiati Hanif Assabib Rosyid Hendri Rasminto Iman Saufik Suasana Indra Ava Dianta Joseph Teguh Santoso Kenny Setiawan Khoirur Rozikin Kusumajaya, Robby Andika Laily Eka Andrianni Lilis Suryani Mat Toyib Maya Utami Dewi Muhamad Khoirul Umam Muhammad Ainun Najib Muhammad Fauzi Alfikri Mukhammad Ibnu Khalim Neilin Nikhlis NUR FAUZIAH Nuris Dwi Setiawan PADJAR SETYO BUDI Priyadi Priyadi Priyo Wibowo Puteri Anindya Maulan Raden Mohamad Herdian Bhakti Rashad Huseynaga Asgarov Rizky Ni'ma Febriani Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto, Edy Siti Kholifah Sulartopo Sulartopo Sunarmi Suprapti Suprapti Suwardi Suwardi Tanveer Shah Teguh Setiadi Toni Wijanarko Adi Putra Uswatul Chasanah Widya Aryani Wijaya Heti Susanti Zaenal Mustofa Zellius Ragiliawan