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ASEAN Journal of Systems Engineering
Published by Universitas Gadjah Mada
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Engineering
ASEAN Journal of Systems Engineering (AJSE) is published by Master Program of Systems Engineering, Faculty of Engineering, Universitas Gadjah Mada as a mean for publishing scientific works in form of research papers, literature study, or scientific review on published articles, about systems engineering especially in the field of energy, industry and environment. The journal is published twice a year (June and December), in both print and online versions.
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Articles 5 Documents
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Search results for , issue "Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering" : 5 Documents clear
ON-GRID AND OFF-GRID RENEWABLE ENERGY DESIGN AS SWRO (SEAWATER REVERSE OSMOSIS) POWER FOR FRESHWATER NEEDS IN GILI TRAWANGAN (STUDI CASE: GILI TRAWANGAN) Robbani, Farisan; Ridwan, Mohammad Kholid; Darmawan, Arif
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.77149

Abstract

Indonesia has 17,504 islands and one of them is Gili Trawangan. Gili Trawangan is one of the favorite tourist destinations in Indonesia which has an area of approximately 6 km2. In order to achieve the development of tourism islands, sufficient water and energy are needed in the area. The island's location in the middle of the sea makes it difficult to get freshwater and energy supplies.In this study, on-grid and off-grid simulations of renewable energy (solar, wind, and biomass) were carried out to determine the potential of integration energy as seawater desalination power as the need for freshwater in the area by calculating NPC (Net Present Cost), COE (Cost of Energy), operational costs, energy, and CO2 emissions. In the off-grid simulation, the lowest NPC and COE integration potentials were found at PV (Photovoltaic) capacities of 102 kW, 14 WT (Wind Turbine), 50 kW BG (Biogas Generator), and 50 kW DG (Diesel Generator) with a value of Rp 17,042,910,000 and Rp 1,587. The lowest operational costs and CO2 emissions are at the PV capacity of 209 kW, 8 WT, 50 kW BG, and 50 kW DG with a value of Rp 292,308,100 and 163 kg/year. In energy production, there is excess production and electrical energy at the PV capacity of 188 kW, 25 WT, 50 kW BG, and 50 kW DG with values of 2,301,533 kWh/year and 1,821,626 kWh/year. On-grid simulation results obtained the lowest NPC and NOE at 30 kW PV, 5 WT, and 50 kW BG with a value 2,961,782,000 and Rp. 200. Lowest operating costs, lowest CO2 emissions, largest electricity production, and best electricity trading at the PV capacity of 209 kW, 5 WT, and 50 BG with a value Rp 12,428,750, 84,000 kg/year, 882,455 kWh/year, 239,996 kWh, and 132,911 kWh.
SENSITIVITY ANALYSIS OF HYPERPARAMETER IN SOLAR ENERGY PREDICTION MODEL USING GRADIENT BOOSTING METHOD Ramadhan, Aska; Sopha, Bertha Maya; Ridwan, Mohammad Kholid
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.78322

Abstract

Solar energy prediction is one alternative to handling unpredicted conditions of weather and solar radiation intensity. It could be the most important factor in achieving stability in electricity generation using solar energy resources. In making predictions, the use of machine learning models has been carried out by various methods, and in this study, the method used for the algorithm model is gradient boosting. In the modeling process using gradient boosting, several hyperparameter settings are needed. Hyperparameters have an important role in producing stable predictive patterns and can avoid overfitting or underfitting conditions. In this study, the accuracy and speed of prediction of the machine learning model with the gradient boosting approach, namely XGBoost and LightGBM, were analyzed in relation to setting the hyperparameter learning rate and max depth of the model's prediction pattern. The dataset used spans 6 months at a data resolution rate of every 5 minutes and includes meteorological data at the location point of Energy Laboratory UKRIM Yogyakarta as well as the output value of PLTS power and temperature panels onsite. Setting the hyperparameter learning rate in the highest and lowest conditions generates accuracy values with a difference of 2% and about the same prediction speed. With nMAE values of 2.84% and 1.35% and nRMSE values of 6.11% and 3.68%, respectively, the higher learning rate results in lower error values for both models. The XGBoost model shown tendency for overfitting and slower prediction speeds with the highest max depth setting. The prediction speed is faster at the lowest max depth condition, but the XGBoost and LightGBM models both exhibit underfitting.
The Role of Renewable Energy in ReducingCarbon Emissions in Cirebon District Nurfalah, Agni; Setiawan, Bakti; Dewayanto, Nugroho
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.79545

Abstract

The adverse effects of the use of fossil energy such as global warming make people slowly decrease the use of fossil energy including in the electric power generation sector. Emissions from burning fossil fuels such as carbon dioxide are one of the components that make up and dominate the composition of greenhouse gases. It is this greenhouse gas that prevents the sun's heat from being reflected back into the atmosphere, so that it accumulates over many years which causes the temperature of the earth's surface to increase.               Slowly the use of fossil energy is stopped and replaced with new renewable energy which produces less greenhouse gases. In meeting the demand for electricity supply, Cirebon Regency is still relying on Steam Coal Power Plants to supply electricity demand. According to projection, Cirebon's total electricity demand will be 109.860,28 GWh by 2050. If Cirebon still continues to use Steam Coal Power Plants until 2050 it's estimated that it will emit 100.489.721,74 tons of carbon dioxide emissions.               Cirebon Regency has alternative energy potential from municipal solid waste (MSW) that can be utilized. Every day, every person in Cirebon Regency can produce 0,541 kg of waste. From the observations, the electrical energy that can be generated and utilized from waste until 2050 is 8.360,45 GWh, of which 2.462,34 GWh is generated from organic waste and 5.898,12 GWh is generated from combustible waste.                   The electrical energy that can be generated from city waste is sufficient for 7,61% of the electricity needs of Cirebon Regency. With an energy mix scheme between PLTU and waste-based power plants, total carbon dioxide emissions can be reduced by 3,05%.
TECHNO-ECONOMIC ANALYSIS OF THE POTENTIAL FOR HYBRID POWER PLANT IN GUNUNG SEWU GEOPARK, PACITAN REGENCY Prima, Putri; Setiawan, Ahmad Agus; Dewayanto, Nugroho
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.79689

Abstract

Gunung Sewu has been designated as a Global Geopark Area by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2015. In the preparation of the Gunung Sewu Masterplan as a UNESCO Global Geopark (UGGp), the provision of environmentally friendly and sustainable electricity is one of the strategies to utilize the renewable energy. The application of Hybrid Power Plants (HPP) that utilize wind and solar energy in the industrial and tourism sectors can have a positive impact on regional income where economic growth is one of the strategic issues of Pacitan Regency.The average wind speed in Pacitan is 3.41 m/s and the average solar radiation reaches 5.69 kWh/m2/day so that it can be said that the potential for solar and wind energy in the Gunung Sewu Pacitan  Geopark is reliable and feasible to be used as an environmentally friendly energy source. The simulation of HPP that will be implemented at Buyutan Beach illustrates that the system could produce an output of 7,519 kWh/yr. This result is able to meet the annual demand of 4,107 kWh/yr and even exceed the annual requirement.The economic analysis of the simulated HPP system produces a negative Net Present Value (NPV). This could be due to the large value of Operational Expenditure (OpEx) and Capital Expenditure (CapEx) and the low purchase price (National Electricity Company’s feed in tariff).  However, this research also proposes a scenario so that the HPP system can still be applied by dividing the cost components contained in the budget plan into two parts. In the proposal obtained cash inflows of Rp. 8,640,834.80/year and a positive or feasible NPV.
NATIONAL PLATFORM OF LIFE CYCLE INVENTORY DATABASE IN INDONESIA Ariyanto, Novy; Sasongko, Nugroho Adi; Eka Putri, Virny Zasyana; Subagyo, Hendro; Siswanto, Siswanto; Wardani, Maya Larasati Donna; Laili, Nurus Sahari; Pratiwi, Annisa Indah; Yanuar, Ahmad Ismed; Septiani, Marini; Hakim, Arif Rahman; Erlambang, Yaumil Putri; Sari, Chintya Komala; Supono, Ihsan; Widiyaningrum, Retno Ayu
ASEAN Journal of Systems Engineering Vol 9, No 1 (2025): ASEAN Journal of Systems Engineering
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v9i1.101472

Abstract

The national Life Cycle Inventory (LCI) database needs to be built, collaborated and integrated to harmonize LCI data, research and information systems across all stakeholders nationally.  The goals of national LCI data harmonization are to: advance national data, research, and information systems by leveraging multi-agency resources and expertise; improve consistency in Life Cycle Assessment (LCA) methods developed by each institution to develop LCA results for decision-making and public disclosure; and enhance public and national institutions to access harmonized data in a standardized searchable format from a common repository. However, the low number of LCI datasets originating from Indonesia results in using other countries' LCI databases that have the potential for high errors and uncertainties and do not represent supply chain data for specific geographical locations in conducting LCA for Indonesian products. The Research Center for Sustainable Production Systems and Life Cycle Assessment (PR SPB PDH) at the National Research and Innovation Agency (BRIN), an institution tasked with establishing a national database for LCI in Indonesia, is currently entering the stage of collecting LCI datasets. This paper proposes recommendations for developing a national platform for the LCI database in Indonesia. The method used is descriptive qualitative analysis from a comparative review of national databases of various countries. The study reveals that the development that has started fulfilled several criteria. However, some requirements must still be met to become a comprehensive LCI national database.

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