Article Per Year (5 Year)
p-Index From 2019 - 2024
0.983
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Engineering and Technological Sciences Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Meditya Wasesa
Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Data Driven Building Electricity Consumption Model Using Support Vector Regression FX Nugroho Soelami; Putu Handre Kertha Utama; Irsyad Nashirul Haq; Justin Pradipta; Edi Leksono; Meditya Wasesa
Journal of Engineering and Technological Sciences Vol. 53 No. 3 (2021)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2021.53.3.13

Abstract

Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.
Agent-based Truck Appointment System for Containers Pick-up Time Negotiation Fakhri Ihsan Ramadhan; Meditya Wasesa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 1 (2020): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51274

Abstract

Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times.
Pemodelan Manajemen Energi Microgrid pada Sistem Bangunan Cerdas FX Nugroho Soelami; Edi Leksono; Irsyad Nashirul Haq; Justin Pradipta; Putu Handre Kertha Utama; Aretha Fieradiella Pahrevi; Faizatuzzahrah Rahmaniah; Meditya Wasesa
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 4: November 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1528.314 KB) | DOI: 10.22146/jnteti.v9i4.488

Abstract

From the electricity system point of view, smart buildings can be seen as an integration of a microgrid electricity network that connects solar PV, storage system, and building load distribution. The operation condition of the microgrid needs to be evaluated and optimized to obtain efficient and reliable performance. This contribution presents an energy management modeling for the microgrid optimization process in a smart building system. The energy sources connected to the microgrid are solar PV, battery storage system, and the PLN (utility) grid. Combinations of load scenarios are evaluated, which consists of building a lighting system, water pump, dan HVAC system. The optimization goal is to find the optimal estimation of Self Consumption (SC) and Self Sufficiency (SS) values. A simulation result before the optimization shows that the system is operating with SC of 63.2% and SS of 96.32%. After the optimization, the values become SC = 84.68% and SS = 83.27%. Therefore, the amount of energy sourced from the Solar PV system is increased and the microgrid is working more optimally.
Industry 4.0 Maturity Models to Support Smart Manufacturing Transformation: A Systematic Literature Review Akhmad Hadi Susanto; Togar Simatupang; Meditya Wasesa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4588

Abstract

With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0 (I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state. Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart technology, products and services have been treated as the popular evaluation dimensions which will define the state of an industry’s maturity level.
Sistem Arsitektur Manajemen Bangunan untuk Memaksimalkan Swakonsumsi pada Bangunan Universitas: Studi Kasus Yumna Puspita; Rezky Mahesa Nanda; Reyza Arif M. Natawidjaja; Koko Friansa; Justin Pradipta; Rizki Armanto Mangkuto; Irsyad N. Haq; Edi Leksono; Meditya Wasesa
Jurnal Otomasi Kontrol dan Instrumentasi Vol 15 No 2 (2023): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2023.15.2.5

Abstract

Due to its intermittent nature, significant adoption of solar PV into the grid can decrease grid reliability. One solution to increase it is to increase PV self-consumption with two methods: adding Energy Storage System (ESS) and conducting Demand Side Management (DSM). University building has a distinct characteristic in its complex dynamics. Therefore, there is a lack of research to control both methods of increasing self-consumption. This paper aimed to do an integrated literature review on increasing self-consumption and then propose a system architecture recommendation for university building management based on the review. The Smart Grid Architectural Model (SGAM) evaluated the case study object. The result showed that a data-driven controller has been chosen as the most suitable controller for the university building management system. The data needed to build a data-driven controller could be obtained through readily available sensors in the case study object, making it feasible for implementation.
Co-Authors Akhmad Hadi Susanto Aretha Fieradiella Pahrevi Edi Leksono Faizatuzzahrah Rahmaniah Fakhri Ihsan Ramadhan FX Nugroho Soelami Irsyad N. Haq Irsyad Nashirul Haq Justin Pradipta Koko Friansa Putu Handre Kertha Utama Reyza Arif M. Natawidjaja Rezky Mahesa Nanda Rizki Armanto Mangkuto Togar Simatupang Yumna Puspita