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All Journal JURNAL SISTEM INFORMASI BISNIS Elkom: Jurnal Elektronika dan Komputer Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika Scientific Journal of Informatics Jurnal Informatika Upgris Prosiding 2nd Seminar Nasional IPTEK Terapan (SENIT) 2017 Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Komunikasi INFORMATIKA Jurnal Pengabdian Mandiri Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Manajemen Informatika & Teknologi Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Community: Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu SWAGATI: Journal of Community Service Saturnus: Jurnal Teknologi dan Sistem Informasi International Journal of Information Technology and Business Systematic Literature Review Journal Journal of Engineering, Electrical and Informatics Global Science: Journal of Information Technology and Computer Science Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Toni Wijanarko Adi Putra
Universitas Sains Dan Teknologi Komputer
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA BASE64 PADA SISTEM LOGIN MENGGUNAKAN JSON WEB TOKEN (JWT) UNTUK AUTENTIKASI WEB Ulil Asyhar; Budi Hartono; Toni Wijanarko Adi Putra
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 17 No. 1 (2026): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v17i1.1311

Abstract

Authentication security is a crucial aspect of web application development, particularly in login systems that function as the primary gateway to an application or web service. The authentication process plays a vital role in ensuring that only authorized users can access the system, thereby preventing data breaches, information misuse, and potential system damage. Traditional login methods that store passwords in plain text or rely solely on session IDs are vulnerable to theft and session hijacking. This research proposes the implementation of JSON Web Tokens(JWT) with Base64 encoding, integrated through Web Service technology, to enhance login system security. JWT is a JSON-based token standard used for secure information exchange between clients and servers. The token consists of three parts—header, payload, signature—each encoded using Base64URL. Base64 is an encoding method that converts binary data into ASCII text, making it safe for transmission over HTTP protocols. In this implementation, the bcrypt algorithm is utilized for password hashing, ensuring that the original password is never stored directly in the database. The Web Service acts as an intermediary for communication between the client and server without the need to store session data on the server. During login, the system verifies credentials, generates a JWT token, and sends it to the client for subsequent authenticated requests. Testing results demonstrate improved security, as tokens can be verified without maintaining server-side sessions, while Base64 encoding ensures safe data transmission. This implementation is expected to serve as a practical solution and reference for future advancements in web application security.
Utilizing Explainable AI for Interpreting Machine Learning Model Results in Ceria Credit Scoring Roni Eka Setiawan; Toni Wijanarko Adi Putra; Budi Hartono
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2769

Abstract

This study aims to improve the transparency of machine learning models in credit scoring using various Explainable Artificial Intelligence (XAI) methods. The methods used include SHAP, BRCG, ALE, Anchor, and ProtoDash to explain the prediction results of machine learning models, namely logistic regression, XGBoost, and random forest. This study applies a quantitative approach with a comparative method, where Ceria loan application data from Bank Rakyat Indonesia (BRI) is analyzed using a machine learning model, then evaluated using the Explanation Consistency Framework (ECF). The results show that the XAI method can improve understanding of model decisions, with SHAP and ALE effective for global explanations, while Anchor and ProtoDash provide in-depth insights at the individual level. Evaluation using ECF shows that the post-hoc method has high consistency, although Anchor has limitations in the aspect of axiom identity. In conclusion, the XAI method can help improve trust and transparency in credit scoring at BRI.Keywords: Explainable Artificial Intelligence; Credit Scoring; Machine Learning; Model Interpretability; Explanation Consistency Framework AbstrakPenelitian ini bertujuan untuk meningkatkan transparansi model pembelajaran mesin dalam penilaian kredit menggunakan berbagai metode Explainable Artificial Intelligence (XAI). Metode yang digunakan antara lain SHAP, BRCG, ALE, Anchor, dan ProtoDash untuk menjelaskan hasil prediksi model pembelajaran mesin yaitu regresi logistik, XGBoost, dan random forest. Penelitian ini menggunakan pendekatan kuantitatif dengan metode komparatif, dimana data pengajuan pinjaman Ceria dari Bank Rakyat Indonesia (BRI) dianalisis menggunakan model machine learning, kemudian dievaluasi menggunakan Explanation Consistency Framework (ECF). Hasilnya menunjukkan bahwa metode XAI dapat meningkatkan pemahaman keputusan model, dengan SHAP dan ALE efektif untuk penjelasan global, sementara Anchor dan ProtoDash memberikan wawasan mendalam pada tingkat individu. Evaluasi menggunakan ECF menunjukkan bahwa metode post-hoc memiliki konsistensi yang tinggi, meskipun Anchor memiliki keterbatasan pada aspek identitas aksioma. Kesimpulannya, metode XAI dapat membantu meningkatkan kepercayaan dan transparansi dalam credit scoring di BRI.Kata Kunci: Explainable Artificial Intelligence; Credit Scoring; Machine Learning; Model Interpretability; Explanation Consistency Framework
Explainable End-to-End Autonomous Driving Using Vision-Based Deep Learning in Safety-Critical Scenarios Dani Sasmoko; Lawrence Adi Supriyono; Toni Wijanarko Adi Putra
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.185

Abstract

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications
Manajemen Webinar Terintegrasi Berbasis Web dan Pengembangan Sistem Perpustakaan Digital untuk Divisi Pelatihan SDM Rizal Dewo Susanto; Toni Wijanarko Adi Putra; Eko Siswanto
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Vol. 5 No. 2 (2026): Mei : Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupsim.v5i2.6992

Abstract

The rapid growth of digital technology has transformed how organizations manage human resource training. PT Bisnis Digital Ekonomi (BDE), through its HR Master Division, conducted 16 webinar series with 2,851 participants between July 2025 and February 2026; however, all administrative processes were managed manually across disconnected platforms including Zoom, Google Drive, and spreadsheets, resulting in fragmented knowledge assets and operational inefficiency. This study aims to design and develop an integrated web-based webinar management and Digital Library system for the HR Master Division using the Waterfall method. Data were collected through direct observation and semi-structured interviews with two division staff members. The system was built using PHP Laravel with MVC architecture and MySQL database, supporting four user roles: Super Admin, HR Master Admin, Trainer, and Employee. Black Box Testing across 24 test scenarios yielded a 100% success rate with no critical bugs detected. The system successfully consolidates webinar scheduling, participant registration, digital attendance, material archiving, and report export into a single platform accessible via hrmasterid.com, eliminating cross-platform fragmentation. This study contributes a replicable integrated training information system model for corporate HR divisions facing similar knowledge management challenges.
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.
Cost-Sensitive Fraud Detection with Reliability Calibration: A Practical Pipeline with XGBoost and Focal-Proxy Reweighting Danang, Danang; Toni Wijanarko Adi Putra
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.13-24

Abstract

Fraud detection on payment transactions is an extremely imbalanced, high-stakes classification task in which deployment decisions depend not only on ranking quality but also on reliable probability estimates. We study credit card fraud detection on a standard real-transaction benchmark (284,807 transactions; 492 frauds) and target two deployment requirements: cost-sensitive thresholding under asymmetric error costs and reliability calibration so model outputs can be interpreted as stable risk scores. We benchmark logistic regression and XGBoost and propose a focal-proxy reweighting scheme for boosted trees via iterative weight updates inspired by focal loss. Probabilities are calibrated on validation using Platt scaling, temperature scaling, and isotonic-style monotone calibration; the best calibrator is selected by minimum validation Brier score. For decision-making, we choose the operating threshold that minimizes expected cost, Cost(t) = 10 · FN(t) + 1 · FP(t), on validation, then evaluate on a held-out test set. On the benchmark split (train 199,364; validation 42,721; test 42,722), the calibrated XGBoost baseline achieves AUROC 0.973, AUPRC 0.812, fraud-class F1 0.767, and expected cost 154 with very low calibration error (ECE = 1.1 × 10⁻⁴). Overall, calibration reduces ECE and improves or maintains the Brier score, while cost-aware thresholding makes the FN/FP trade-off explicit via decision curves. 
Privacy Protection and Trust in the Digital Era: A Systematic Review of Data Breach Impacts on SDG Progress Toni Wijanarko Adi Putra; Danang, Danang
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.812025.24-34

Abstract

Objective – In the digital transformation era, the integrity of personal data has become essential for maintaining trust and ensuring the sustainability of digital services. This paper aims to systematically review how data privacy violations affect public trust and progress toward Sustainable Development Goals (SDGs), especially SDG 9 (infrastructure and innovation) and SDG 16 (strong institutions and justice). Methodology—This study adopts the Systematic Literature Review (SLR) approach based on Kitchenham’s framework. Relevant articles from 2021–2025 were retrieved from Scopus, IEEE, Springer, and ScienceDirect using a predefined search string aligned with PICOC. A total of 19,504 records were screened, and 36 high-quality studies were selected after applying inclusion/exclusion criteria and quality assessment tools (e.g., CASP, AMSTAR). Findings—The review reveals that sectors such as education, healthcare, and smart cities are increasingly adopting data protection technologies, including encryption, federated learning, differential privacy, and blockchain. However, many still face regulatory, infrastructural, and human literacy gaps. Breaches in personal data significantly reduce public trust, impair the exercise of digital rights, and pose ethical and operational risks for achieving SDGs. Limitations – The study is limited by the timeframe (2021–2025) and focuses primarily on peer-reviewed literature. Practical insights from developing countries may be underrepresented due to database indexing limitations. Contribution – This review contributes a cross-sectoral synthesis of technological and regulatory practices for data protection, identifies key challenges, and outlines a strategic roadmap for policymakers and technologists to integrate ethical data governance for sustainable digital futures.
Co-Authors Achmad Solechan Agung Surono Ahmad Ashifuddin Aqham Aldho Dwi Kurniawan Alex Ristanto Pratama Arditya Himawan Arsito Ari Kuncoro Aslina Baharum budi hartono Budi Hartono Budi Hartono Budi Hartono Chandani, Chicie Chicie Chandani Danang Danang Danang, Danang Dani Sasmoko Dani Sasmoko Dendy Kurniawan Devi Astri Nawangnugraeni Dimas Rahardian Dwi Prasetyaningsih Edy Siswanto Eko Siswanto Eko Siswanto Endrahadi Rahadian Endro Farhan Afif Ilmi Febryantahanuji Febryantahanuji Fitro Nur Hakim Fitro Nur Hakim Fujiama Diapoldo Silalahi Fujiama Diapoldo Silalahi Hakim , Fitro Nur Haryo Kusumo Haryo Kusumo Iman Saufik Suasana Isaac Vital Jarot Dian Susatyono Kusumo, Haryo Kusworo Adi Lawrence Adi Supriyono M. Zakki Abdillah Migunani Migunani Mohamad Irkam Nuris Dwi Setiawan R Rizal Isnanto Rizal Dewo Susanto Roni Eka Setiawan Rusito Siswanto Siswanto Siswanto Siswanto Siswanto, Edy Solikhin Solikhin Sulartopo Sulartopo Ulil Asyhar Unang Achlison Victor Gayuh Utomo Wibowo, Mars Caroline Yehezkiel Yanu Putranto Yuli Fitrianto Zaenal Mustofa