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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Indonesian Journal of Applied and Industrial Sciences (ESA)
Ramadani Ramadani
Universitas Muhammadiyah Asahan
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Knowledge Graph Integration to Improve Reliability and Quality of Maintenance Data in the Oil and Gas Processing Industry Adnan Buyung Nasution; Ahir Yugo Nugroho; Deny Adhar; Risman Risman; Ramadani Ramadani
Indonesian Journal of Applied and Industrial Sciences (ESA) Vol. 5 No. 4 (2026): July 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/esa.v5i4.20

Abstract

Data quality and reliability are fundamental challenges in asset maintenance management in the oil and gas processing industry, where equipment data is scattered across dozens of heterogeneous operational tables, including SAP notifications, work orders, pipe inspections, corrosion monitoring results, and metering data. This data fragmentation makes it difficult to trace relationships between entities, identify anomalies, and perform consistent cross-validation. This study proposes a Knowledge Graph (KG) integration approach as a data representation layer that connects equipment entities as central nodes with child nodes from monitoring tables through typed relationships. Implementation is carried out through the development of a Graph Manager component based on Flask and D3.js that synchronizes data from a PostgreSQL relational database to a Neo4j graph database on a scheduled basis. Evaluation is carried out on three aspects of data reliability, namely referential consistency, attribute completeness, and timeliness of data updates, before and after the implementation of the KG. Test results in a production environment show significant improvements in equipment tag inconsistency detection, decreased equipment history search time, and increased semantic search accuracy when the KG is combined with embedding vector (pgvector)-based search. This study concludes that the graph approach provides a real contribution to data governance and operational reliability of maintenance systems.
Analisis Komparatif Algoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes Alfa Saleh; Ria Eka Sari; Ramadani Ramadani; Fujiati Fujiati; Ratna Lestari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23794

Abstract

Diabetes Mellitus is one of the most common chronic diseases, this disease is also a major concern in global public health issues. in this study, a Machine Learning approach was carried out to help predict diabetes in the community. Machine learning is very useful in analyzing health data because of its good ability to process large amounts of data. A comparative study with several machine learning classification algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes and Decision Tree (C4.5) has been conducted to determine which algorithm gives the best results in terms of predicting diabetes. Where, the features used in predicting diabetes include gender, age, history of hypertension, history of heart disease, history of smoking, BMI, level of HbA1c and blood glucose levels. From the results of this study, the accuracy rate of diabetes prediction for the K-Nearest Neighbors (KNN) algorithm is 92.5%, the Support Vector Machine (SVM) algorithm is 94.5%, the Naive Bayes algorithm is 90% and the last for the Decision Tree (C4.5) algorithm is 93.5%. So, from the test results of several machine learning classification algorithms it can be concluded that the Support Vector Machine (SVM) algorithm is the most optimal algorithm in terms of predicting diabetes. Keywords: Machine Learning, KNN, SVM, Naive Bayes, Decision Tree, Diabetes.
Co-Authors Adnan Buyung Nasution Ahir Yugo Nugroho Alfa Saleh Deny Adhar Fujiati, Fujiati Ratna Lestari Ria Eka Sari Risman Risman