Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Digitus : Journal of Computer Science Applications
Widio Rijanto
Teknik Informatika Universitas Islam As-syafi'iyah
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Identifying Recurrent Urban Traffic Congestion Hotspots Using Spatiotemporal Analysis of Vehicle GPS Data in Balikpapan Widio Rijanto; Karno Diantoro
Digitus : Journal of Computer Science Applications Vol. 4 No. 2 (2026): April 2026
Publisher : PT Penerbit Ilmiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v4i2.2026

Abstract

This study develops a spatiotemporal framework for identifying recurrent urban traffic congestion hotspots in Balikpapan, Indonesia, addressing misclassification caused by speed thresholds and GPS density measures that overlook operational stops. The framework integrates direction-specific road segmentation, coverage-adjusted recurrence measurement, spatiotemporal clustering, and independent validation. GPS trajectories from 120 four-wheeled vehicles were collected across three arterial corridors over 212 days (August 2025–February 2026), yielding 86,089 traversals and 1,713,246 GPS points after deduplication and map matching. Congestion was identified using speed ratios below 0.50 of the 85th-percentile reference speed within 15-minute directed segment intervals containing at least five unique vehicles. ST-DBSCAN clustered 3,357 congestion episode cells into 419 daily spatiotemporal clusters across 30 directed segments. Jalan MT Haryono exhibited the highest congestion intensity, accounting for 1,664 episode cells and 24,960 congestion-minutes. Recurring congestion, defined as a congested-day proportion of at least 0.30 among sufficiently observed days, affected 49 segment–time-window combinations across all 30 directed segments, particularly during weekday afternoon (15:00–18:00) and morning (06:00–09:00) periods. Independent validation demonstrated high precision (0.97), moderate recall (0.53), and an F1-score of 0.69. October 2025 recorded the highest monthly congestion episode fraction (13.3%). These findings provide reproducible hotspot identification methods and evidence-based insights for traffic management in sensor-limited urban environments.
Static Analysis vs. Property-Based Testing for Escrow Smart Contracts: Fault Detection and False Positives Asril Adi Sunarto; Widio Rijanto
Digitus : Journal of Computer Science Applications Vol. 4 No. 1 (2026): January 2026
Publisher : PT Penerbit Ilmiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v4i1.2033

Abstract

Escrow smart contracts automate fund management on blockchain, where errors in authorization logic or state-transition rules can cause unauthorized payments or permanently locked funds. This study compared Slither, a static analysis framework, with Echidna, a property-based fuzzer, in detecting controlled faults injected into Solidity escrow contracts. A paired experiment was conducted on 24 base contracts across four escrow patterns and 144 fault variants across six fault categories, with Echidna executed under 30 independent random seeds per contract. Echidna achieved higher recall than Slither (61.3% vs. 31.9%), with a paired difference of 29.4 percentage points (95% CI: 21.7–36.9 pp). Slither produced substantially more false positives (29.2% vs. 1.8%) and lower precision (48.4% vs. 90.4%). Slither executed in approximately 5 seconds per run, whereas Echidna consistently reached the 300-second limit; however, Echidna required approximately 2.7 times the preparation effort. Slither outperformed Echidna only on reentrancy faults, while Echidna dominated on authorization, double payment, invalid refund, and state-transition categories. Combining both tools raised union recall to approximately 71.6%. These findings suggest complementary deployment of both tools in secure contract development pipelines, leveraging Echidna’s precision for business-logic faults and Slither’s speed for reentrancy detection.
Co-Authors Asril Adi Sunarto Karno Diantoro