Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Rekayasa Sistem Industri Jurnal IPTEK
Shamaradewa, Shanggabuana Adhitya
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Industrial & Manufacturing Engineering Mechanical Engineering

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

Found 3 Documents
Search

 1 
3D Virtual prototyping dalam Perbaikan Desain Fasilitas Tempat Kerja: Studi Kasus CV. XYZ Muttaqin, Benazir Imam Arif; Asfari, Ully; Mardhiana, Hawwin; Shamaradewa, Shanggabuana Adhitya; Dawangga, Gagas Putra
Jurnal IPTEK Vol 25, No 1 (2021)
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2021.v25i1.1164

Abstract

Seperti yang kita ketahui, tata letak fasilitas merupakan hal yang sangat penting karena berpengaruh terhadap produktivitas kerja. Di industri manufaktur, perancangan dan optimasi tata letak fasilitas umumnya dilakukan dengan pendekatan facility layout planning berdasarkan kriteria ongkos material handling. Pendekatan tersebut memiliki beberapa kelemahan, salah satunya adalah bias terkait jarak dan kurang mempertimbangkan faktor lain seperti faktor psikologis dan perspektif dari pengguna/operator. Virtual reality (VR) dan 3D Virtual prototyping adalah salah satu pendekatan terkini yang dimanfaatkan dalam perancangan dan optimasi tata letak fasilitas. Penelitian ini adalah tahap awal dari pengembangan prosedur perancangan tempat kerja secara virtual dengan mengintegrasikan pendekatan facility layout planning dan simulasi VR. Penelitian ini menggunakan data empirik yang diperoleh dari studi kasus pada salah satu perusahaan manufaktur yang bergerak di bidang usaha konveksi. Hasil dari penelitian ini berupa 3D Virtual prototyping dari rancangan tata letak fasilitas yang nantinya akan dievaluasi menggunakan simulasi VR.
Perbandingan Metode Moving Average dan Exponential Smoothing untuk Mengestimasi Jumlah Gangguan Layanan Internet Prastyabudi, Wahyu Andy; Shamaradewa, Shanggabuana Adhitya
Jurnal Rekayasa Sistem Industri Vol. 13 No. 2 (2024): Vol. 13 No. 2 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i2.7609.157-176

Abstract

Fairly tight competition in telecommunication business requires companies to continue improving the quality of their services, including handling network problems or customer complaints. However, the amount of disturbance that occurs is often unpredictable. Indeed, this condition will impact the allocation of resources and budget for handling customer disturbances in the following period, with uncertainty. This research aims to apply the moving average (MA) and single exponential smoothing (SES) forecasting methods to predict the number of internet service disruptions. The case study used in this paper is an internet service provider company with service coverage in the northern part of Surabaya. The number of disturbances data was collected from January 2022 to May 2022, with 18,453 data in total. The disturbances can be divided into five types: physical, mass, logical, PSB/migration, and others. Forecasting is carried out using the MA method with a period of three months. Meanwhile, forecasting using the SES method was carried out by first determining the alpha value that produces the smallest Mean Absolute Percentage Error (MAPE) value. The analysis results show that both forecasting methods are relatively effective and efficient in estimating the number of disturbances. Forecasting performance testing was carried out by measuring the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) values. The results of forecasting performance measurements show that the SES method is much better than MA for all types of disturbance data, with MAPE values ​​below 2%.
Perbandingan Metode Moving Average dan Exponential Smoothing untuk Mengestimasi Jumlah Gangguan Layanan Internet Prastyabudi, Wahyu Andy; Shamaradewa, Shanggabuana Adhitya
Jurnal Rekayasa Sistem Industri Vol. 13 No. 2 (2024): Vol. 13 No. 2 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i2.7609.157-176

Abstract

Fairly tight competition in telecommunication business requires companies to continue improving the quality of their services, including handling network problems or customer complaints. However, the amount of disturbance that occurs is often unpredictable. Indeed, this condition will impact the allocation of resources and budget for handling customer disturbances in the following period, with uncertainty. This research aims to apply the moving average (MA) and single exponential smoothing (SES) forecasting methods to predict the number of internet service disruptions. The case study used in this paper is an internet service provider company with service coverage in the northern part of Surabaya. The number of disturbances data was collected from January 2022 to May 2022, with 18,453 data in total. The disturbances can be divided into five types: physical, mass, logical, PSB/migration, and others. Forecasting is carried out using the MA method with a period of three months. Meanwhile, forecasting using the SES method was carried out by first determining the alpha value that produces the smallest Mean Absolute Percentage Error (MAPE) value. The analysis results show that both forecasting methods are relatively effective and efficient in estimating the number of disturbances. Forecasting performance testing was carried out by measuring the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) values. The results of forecasting performance measurements show that the SES method is much better than MA for all types of disturbance data, with MAPE values ​​below 2%.
Co-Authors Benazir Imam Arif Muttaqin Dawangga, Gagas Putra Hawwin Mardhiana Ully Asfari Wahyu Andy Prastyabudi