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Muhammad Wali
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muhammadwali@amikindonesia.ac.id
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+6285277777449
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ijsecs@lembagakita.org
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Jl. Teuku Nyak Arief No. 7b 23112, Kota Banda Aceh, Banda Aceh, Provinsi Aceh
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 423 Documents
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Comparative Performance Analysis of Integrated Monitoring Engine for Electric Energy Transaction Data Gateway Infrastructure to Accelerate SLA Incident Resolution Pipit Suryandani; Yuli Kurnia Ningsih; R. Deiny Mardian
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7151

Abstract

PLN Icon Plus operates the Energy Transaction Data Gateway as the sole intermediary between banking partners and the national P2PST core server — an architecture where monitoring failure carries direct consequences for millions of daily transactions. Prior to this study, the monitoring ecosystem operated across three isolated platforms: Huawei iMaster NCE-Fabric for network telemetry, Zabbix for server resource metrics, and Elastic Stack (ELK) for application log management, with no automated correlation between them. This study developed an integrated monitoring system on the Grafana platform that unifies these heterogeneous data sources into a Single Pane of Glass dashboard. The architecture employs NTP-calibrated timestamp alignment and data normalization to ensure cross-platform event correlation accuracy at sub-100 millisecond precision. A unified alerting system was deployed via Telegram Bot API using multi-condition severity thresholding, requiring confirmed cross-layer correlation before notification dispatch to prevent alert fatigue. Comparative performance validation against the pre-implementation siloed condition — based on 69 documented production incidents from January to March 2026 — confirmed a 63.6% reduction in overall Mean Time to Repair (MTTR) and a 79.2% reduction in network incident MTTR specifically. SLA availability improved from 99.71% to 99.94%, surpassing the 99.9% contractual target. The primary contribution is a cross-layer data correlation model that measurably compresses the fault identification phase within national energy transaction infrastructure, validated through both statistical analysis and a structured questionnaire survey across 56 respondents.
Integrating Systematic Literature Review and Longitudinal Analysis for Employee Satisfaction Evaluation: A Hospitality Industry Case Study I Made Sudana; Endah Sudarmilah; Yusuf Sulistyo Nugroho
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7154

Abstract

The success of service quality in organizations is heavily influenced by employee satisfaction. This research proposes an integrated framework combining a Systematic Literature Review (SLR) with a longitudinal analysis of Employee Satisfaction Survey (ESS) data at Zest Parang Raja Solo to evaluate determinants of job satisfaction over the 2022–2025 period. Following the PRISMA 2020 protocol across 35 selected studies, seven key variables were identified, with leadership emerging as the most dominant factor (74.29%) in global literature. Empirical validation through longitudinal analysis of a qualified total population (N=70 respondents over four years, adhering to strict organizational tenure SOPs) reveals a degrading satisfaction trend, decreasing from 91.99% in 2022 to 85.66% in 2025. This decline was primarily driven by a significant divergence in compensation and workplace facilities, with the "Intention to Stay" indicator dropping to a critical 68.8% in the final year. Drawing on Herzberg's Two-Factor Theory, the empirical evidence underscores a psychological threshold: motivator factors, such as high-performing leadership (scoring 96.3%), cannot fully mitigate the decline in satisfaction if hygiene factors, specifically salary competitiveness, are not adequately addressed. The contribution of this study lies in providing a theoretical validation framework that enhances evidence-based HR analytics for strategic decision-making in the hospitality industry.
Web Attack Detection for SQLi and XSS Using Ensemble Learning Based on Character-Level N-Gram Features Yaya Suharya; Mohammad Bayu Anggara
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7193

Abstract

SQL Injection (SQLi) and Cross-Site Scripting (XSS) remain severe threats to web application security, particularly as attackers employ increasingly sophisticated obfuscation techniques to bypass conventional detection systems. This research constructs a machine learning framework using ensemble learning — specifically combining Random Forest and XGBoost — integrated with character-level n-gram feature extraction. The methodology involved rigorous data curation of a large-scale dataset, refining 156,636 raw samples into 151,783 unique entries to ensure high-quality training data. By extracting 10,000 character-level n-gram features, the model captures the intricate structural patterns of complex and obfuscated payloads. Experimental results show consistent and measurable performance: the proposed ensemble model achieved an overall accuracy of 99.67%. Stability was confirmed through a 5-fold cross-validation process, yielding a mean accuracy of 99.64% and a standard deviation of 0.0003. These findings are reinforced by ROC AUC scores of 1.0000 for XSS and 0.9999 for SQLi, indicating near-perfect discriminative capability. The combination of character-level representation and ensemble learning produces a precise and resilient solution for safeguarding modern web environments against dynamic and evolving cyber threats.

Page 43 of 43 | Total Record : 423

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