Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Marine Engineering Innovation and Research Journal Research of Social Science, Economics, and Management
Nani Kurniati
Institut Teknologi Sepuluh Nopember
Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Hydrodynamic Model Simulation at the Port of Tanjung Rhu Belitung Sujantoko; Pramudya Adhi Pangestu; Dony Saputra; B. P. Putra Ekianto; Hasanudin Ekianto; Nani Kurniati; Rindi Kusumawardhani; Dody Hartanto
International Journal of Marine Engineering Innovation and Research Vol. 8 No. 2 (2023)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v8i2.5198

Abstract

Ports around Belitung Island have an essential role in supporting inter-island shipping activities, which positively impact economic growth. So that predictions to find out the condition of the waters need to be made early to anticipate disruption to the ship's shipping lanes. This study's prediction of these waters was carried out with a hydrodynamic model. Model accuracy (calibration) compares model results with survey data. Based on several iterations, the tidal model and current velocity are obtained with an accuracy rate of 5% and 7%, respectively. These results indicate the model's relatively good level of accuracy because it is below 10%. The model results also found that current velocity and wave height are always higher during high tide conditions than during low tide. In addition, along the Tanjung Rhu shipping channel, the current speed at point A1 experiences a significant difference during high tide conditions, namely 0.285 m/s at high tide, 0.102 m/s at low tide, 0.072 m/s at low tide, and 0.033 m/s s when heading for high tide, because of the narrowing of the water area due to the crush of two landmasses. In addition, areas far from shipping lanes (A1 and A2) have the lowest wave height compared to areas close to shipping lanes (A3). Because the surrounding land slightly covers the two points (A1 and A2), the wave height is smaller than point A3 in more open waters.
Optimization of Gas Turbine Operational Parameters Using Machine Learning Safwanul Hadi; Nani Kurniati
Journal Research of Social Science, Economics, and Management Vol. 5 No. 5 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i5.1264

Abstract

Cogeneration Gas turbines are the main equipment in the electrical grid system. To meet the load needs on the grid, power plants have several gas turbine units, with the same or different capacities. The load adjustment for each gas turbine unit is carried out by numerical calculation based on load needs, operating parameters, fuel consumption and steam production. In Fact, the recommended value of the numerical calculation is always above the turbine gas operation, resulting in inefficient fuel consumption. This research reformaltes a gas turbine dispatch problem into a data-driven optimization task. Researcher develop an Artificial Neural Network (ANN) on Multi Layer Preceptron (MLP) model using parameter data from 2024 with filters baseload-efficient condition. The Model produces unit capability rankings and validated within <2% error. Compared to dispatcher recommendations, average deviation ~7% with the model, enabling measurable fuel saving and increased steam production.
Implementation of Text Mining-Based Linear Programming in Maintenance Scheduling in Power Plant Fajri Septia Yazid; Nani Kurniati
Journal Research of Social Science, Economics, and Management Vol. 5 No. 5 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i5.1271

Abstract

Weekly maintenance scheduling is an important activity in maintaining the reliability of operating facilities, especially in the Power Plant area in the oil and gas industry sector. This study aims to optimize the maintenance planning and scheduling process by utilizing the text mining approach through Latent Dirichlet Allocation (LDA) modeling to manage and group maintenance data, and integrate it with the Linear Programming (LP) model as the basis for the preparation of an optimal Work Order (WO) Scheduler. The LDA model is used to categorize work based on Fixed Reference Activities (FRA), resulting in a more structured classification of maintenance activities. The output of this category is then an input for the LP model that compiles labor allocation, duration, and work priorities according to the available weekly time limits. Sensitivity analysis was carried out on the parameters of the number of labor, work priority, and length of time horizon with variations of ±10%, ±20%, and ±30%. The results show an increase in the WO completion rate, a reduction in the backlog, and a more accurate understanding of labor utilization. The model has proven to be sensitive to changes in workforce capacity so that human resource management is the dominant factor in the successful implementation of schedulers.
Co-Authors B. P. Putra Ekianto Dody Hartanto Dony Saputra Fajri Septia Yazid Hasanudin Ekianto Pramudya Adhi Pangestu Rindi Kusumawardhani Safwanul Hadi Sujantoko