jimik@stmiki.ac.id 081377790601 Online Submission Open
Vol. 5 No. 2 (2024) • Articles
Open Access

Optimisasi Pengecekan Anomali pada Proses Job: Analisis Waktu dan Data untuk Identifikasi Anomali yang Efisien

Michael Rio Aditya
Universitas Kristen Satya Wacana
Christine Dewi
Universitas Kristen Satya Wacana
Published: May 10, 2024 Pages: 1819-1832
Original Full-Text Article
Download published version for reading and archiving
Download PDF
Abstract

The use of services in various software program contexts has become vital. Anomalies, whether in small or large scale, can disrupt the function of a software and impact a company's business processes. Therefore, the process of anomaly identification is crucial to ensure optimal software performance. This research aims to optimize the anomaly identification process in software services at PT. XYZ Tbk. Currently, information about anomalies is obtained through manual checks by the system operator team, which involves checking each job one by one, consuming a lot of time and potentially leading to overlooked anomalies. This research presents a solution to address this issue by developing an optimization system for monitoring anomaly identification in job, enabling faster identification for maintaining system services stability.. The anomaly identification monitoring system is a web-based application built using the Prototyping methodology, PHP programming language, Yii2 framework, PostgreSQL database, WebSocket, and Control-M API. The result of this research is the developed anomaly identification monitoring system, capable of accelerate the identification process and accelerate information transfer using WebSocket.

Keywords:
Monitoring System Identification Software Service Anomalies WebSocket
Article Metrics & Downloads Graph
Monthly Download Trends:
Author Biographies
Michael Rio Aditya Universitas Kristen Satya Wacana

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.

Christine Dewi Universitas Kristen Satya Wacana

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.

How to Cite
Aditya, M. R., & Dewi, C. (2024). Optimisasi Pengecekan Anomali pada Proses Job: Analisis Waktu dan Data untuk Identifikasi Anomali yang Efisien. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(2), 1819-1832. https://doi.org/10.35870/jimik.v5i2.737
License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .

  • Share: You are free to copy, distribute, and transmit the work in any medium or format.
  • Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
  • Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Copyright & Retention: Authors retain copyright without restrictions and grant this journal the right of first publication under an open-access model. The journal retains non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

References
Total: 15 References
  1. Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., & Benini, L. (2019). A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems. Engineering Applications of Artificial Intelligence, 85, 634-644. DOI: https://doi.org/10.1016/j.engappai.2019.07.008.
  2. Ding, Q. (2012). BMC Control-M 7: A Journey from Traditional Batch Scheduling to Workload Automation. Packt Publishing Ltd.
  3. Gupta, Y., Dewan, H., & Leekha, A. (2020). Real-time monitoring using AJAX and WebSockets. Journal of Statistics and Management Systems, 23(1), 125-134. DOI: https://doi.org/10.1080/09720510.2020.1714154.
  4. Gutta, S., Shenoy, J., Kamath, S. P., Mithra, P., Baliga, B. S., Sarpangala, M., & Srinivasan, M. (2019). Light emitting diode (LED) phototherapy versus conventional phototherapy in neonatal hyperbilirubinemia: a single blinded randomized control trial from coastal India. BioMed Research International, 2019.
  5. Josephine, M., & Subramanian, D. K. S. (2009). Software error detection and correction (SEDC) using layer based approach in expert system. International Journal of Reviews in Computing, ISSN, 2076-3328.
  1. Kadir, A. (2009). Telaah Ajax Untuk Mengejar Ketertinggalan Aplikasi Web Terhadap Aplikasi Desktop. Jurnal Teknologi Ist Akprind, 2.
  2. Kothari, S. C., Bishop, L., Sauceda, J., & Daugherty, G. (2004). A pattern-based framework for software anomaly detection. Software Quality Journal, 12, 99-120.
  3. Maulana, R., & Alauddin Makassar, N. (2021). Implementasi Web Socket Pada Sistem Pelayanan Pasien Rawat Jalan Pada Puskesmas Kabupaten Gowa. J. INSTEK (Informatika Sains dan Teknol., vol. 6, no. 1, p. 130, 2021, doi: 10.24252/instek. v6i1. 20555.
  4. Ogundeyi, K. E., & Yinka-Banjo, C. (2019). WebSocket in real time application. Nigerian Journal of Technology, 38(4), 1010-1020.
  5. Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2021). Deep learning for anomaly detection: A review. ACM computing surveys (CSUR), 54(2), 1-38.
  6. Shi, C., Yuan, F., Li, Z., Zheng, Z., Yuan, C., Huang, Z., ... & Ding, Z. (2022). MSN@ IL-4 sustainingly mediates macrophagocyte M2 polarization and relieves osteoblast damage via NF-κB pathway-associated apoptosis. BioMed Research International, 2022.
  7. Shi, T., Zou, Z., & Ai, J. (2023). Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model. Applied Sciences, 13(20), 11349.
  8. Suryn, W. (2013). Software quality engineering: a practitioner's approach. John Wiley & Sons.
  9. Taha, A., & Hadi, A. S. (2019). Anomaly detection methods for categorical data: A review. ACM Computing Surveys (CSUR), 52(2), 1-35.
  10. Utomo, S. B., Hidayah, M., & Anjani, N. L. (2022). DETEKSI DINI IDENTIFIKASI INSIDEN PADA KEJADIAN ANOMALI PERANGKAT LUNAK DENGAN SISTEM PENDETEKSI ANOMALI PERANGKAT LUNAK STUDI KASUS DI ASTRA LIFE: indonesia. Technologic Politeknik Astra, 13(1). DOI: https://doi.org/10.52453/t.v13i1.390.
Similar Articles

Articles sharing related keywords and machine learning classifications:

Khanza Hospital Information System and Outpatient Service Efficiency at Bhayangkara Brimob Hospital
Vol. 7 No. 3 (2026) • DOI: 10.63447/jimik.v7i3.2027
Digitalisasi UMKM Bengkel melalui Sistem Booking Servis dan Monitoring secara Real-Time
Vol. 7 No. 3 (2026) • DOI: 10.63447/jimik.v7i3.2014
SISTEM INFORMASI SERVICE MOBIL DI PT. DUNIA BARUSA
Vol. 2 No. 1 (2021) • DOI: 10.35870/jimik.v2i1.45
Implementasi Sistem Informasi Pengukuran Kinerja Karyawan di PT Hetra Vexa Group dengan Pendekatan Key Performance Indikator
Vol. 7 No. 1 (2026) • DOI: 10.63447/jimik.v7i1.1640
Perancangan Sistem Perpustakaan Digital Berbasis Android dengan QR Code pada MTsN 2 Kota Bekasi
Vol. 7 No. 3 (2026) • DOI: 10.63447/jimik.v7i3.1980
Most read articles by the same author(s)

Other papers published by author(s) in this journal:

Analisis Sentimen Crawling Data dari Sosial Media X tentang Gaza Menggunakan Metode SVM dan Decision Tree
Nicola Van Robert Jhosefhin, Christine Dewi • Vol. 6 No. 1 (2025)
PENGEMBANGAN DASHBOARD TRIVY BERBASIS WEBSITE MENGGUNAKAN REACT JS DAN GOLANG
Aquenov Alexandro Kroons, Christine Dewi • Vol. 4 No. 3 (2023)
Sentimen Analisis Penggunaan Aplikasi Canva Menggunakan Support Vector Classification
Gracelya Oktaviani, Christine Dewi • Vol. 6 No. 1 (2025)