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All Journal Journal of Engineering and Technological Sciences Dinamika Teknik Mesin : Jurnal Keilmuan dan Terapan Teknik Mesin REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development
A. Kurniawan
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesa 10, Bandung 40132, Indonesia
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Engineering Environmental Science Mechanical Engineering Physics Transportation

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Improved Predictive Power Control Algorithms to Increase CDMA System Capacity Kurniawan, A.; Iskandar, Iskandar; Machdar, Sayid
Journal of Engineering and Technological Sciences Vol 41, No 2 (2009)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (276.786 KB) | DOI: 10.5614/itbj.eng.sci.2009.41.2.7

Abstract

In  this  paper  capacity  of  CDMA  system  is  evaluated  using  an improved  algorithm  of  channel  prediction-based  power  control  in  Rayleigh fading  channel environments. One  of the most serious problems which degrades the performance of power control algorithm is the effect of feedback delay. To overcome the effect of feedback delay, power control algorithm relies on channel prediction techniques, which utilize the correlation property of the past  channel measurements.  In  CDMA  power  control,  however,  the  correlation  property  of channel  measurements  is destroyed  because the  transmit power  is continuously updated  for  each  power  control  interval.  In  order  to  restore  the  correlation property of the channel,  the past  channel measurements  are compensated  for by the  same  factors  that  were  given  by  power  updating  for   each  power  control interval. The prediction algorithm in this paper is proposed using the least mean square  (LMS) technique. The result shows that the capacity of CDMA systems increase  significantly  when  the  improved  predictive  algorithm  is  used. Numerical evaluation shows that CDMA capacity increases by more than 40 % for fixed step algorithm and more than 50 % for variable step algorithm when the proposed algorithm is employed. 
Prediksi kekasaran permukaan baja S45C terhadap parameter pemesinan dan getaran pada proses bubut menggunakan metode artificial neural network Bismantolo, P.; Utama, F.P.; Kurniawan, A.
Dinamika Teknik Mesin: Jurnal Keilmuan dan Terapan Teknik Mesin Vol 13, No 1 (2023): Dinamika Teknik Mesin: Jurnal Keilmuan dan Terapan Teknik Mesin
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dtm.v13i1.605

Abstract

Based on machining characteristics, this study gives surface roughness modeling for machine parts. The artificial models used the Artificial Neural Network (ANN) modeling approach and multivariable regression analysis were used to create the prediction model. S45C steel was one of the materials utilized in this research. With a depth of cut 0.5 mm, the parameters are spindle (n) of 165, 330, 585, and 1170 rpm and feed (f) of 0.2 mm/rev. Utilizing TIBCO software, surface roughness values will be predicted. Equations derived from multivariable linear regression serve as the study's findings. At 1170 rpm spindle rotation and 0.5 mm of cut depth, the lowest surface roughness measurement of 1.114 (μm) was recorded. At spindle speed 585 and a cut depth of 2.0 mm, a roughness value of 2.999 (μm) was recorded as the maximum value. Roughness rises at spindle speeds between 585 and 900 rpm when cutting at shallower depths. The third modeling had the smallest error value, which was 11.21%, and surface roughness value using an artificial neural network with five simple multi-layer models.
Analisis Potensi Likuefaksi Dengan Alat Swedish Weight Sounding di Desa Tompe Kecamatan Sirenja Kabupaten Donggala Setiawan, H.; Sutrisno, M.; Hasanah, Y.; Rizal, A.; Kurniawan, A.; Qhalbi, A.N.; Gayatri, S.
REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development Vol. 6 Issue 1 (March 2025)
Publisher : Tadulako University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/renstra.v6i1.693

Abstract

In 2018 in Indonesia there has been an earthquake of 7.4 on the richter scale. The epicenter was on land around Sirenja district, Donggala regency, Central Sulawesi. The impact in some areas there is liquefaction of these areas, namely Petobo village and Balaroa village and also subsidence in Tompe Village this study aims to determine whether the soil in segment II of Tompe village has the potential for liquefaction based on the results of Cyclic Stress Ratio (CSR) & Cyclic Resistance Ratio (CRR) analysis and based on the results of Tsuchida grain distribution graph. Samples in this study amounted to 10 points by field testing using Swedish weight sounding tool to obtain the value of Nsw (n/m) correlated to the N-SPT data and analyzed by the CSR & CRR equation to obtain the value of the safety factor (FS). The results obtained with the range of FS values are for the potential 0.13 – 0.98 and not potential 1.06 – 1.72 and based on the results of grading the grain to get the value for the potential 70.31% - 95.87% and not the potential 0.39% - 6.62%  from the results of the 2 methods it is concluded that Tompe village based on the value of  FS 50% has the potential for liquefaction and based on the Tsuchida graph 85.78% has the potential for liquefaction.
Prediksi kekasaran permukaan baja S45C terhadap parameter pemesinan dan getaran pada proses bubut menggunakan metode artificial neural network Bismantolo, P.; Utama, F.P.; Kurniawan, A.
Dinamika Teknik Mesin Vol 13, No 1 (2023): Dinamika Teknik Mesin: Jurnal Keilmuan dan Terapan Teknik Mesin
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dtm.v13i1.605

Abstract

Based on machining characteristics, this study gives surface roughness modeling for machine parts. The artificial models used the Artificial Neural Network (ANN) modeling approach and multivariable regression analysis were used to create the prediction model. S45C steel was one of the materials utilized in this research. With a depth of cut 0.5 mm, the parameters are spindle (n) of 165, 330, 585, and 1170 rpm and feed (f) of 0.2 mm/rev. Utilizing TIBCO software, surface roughness values will be predicted. Equations derived from multivariable linear regression serve as the study's findings. At 1170 rpm spindle rotation and 0.5 mm of cut depth, the lowest surface roughness measurement of 1.114 (μm) was recorded. At spindle speed 585 and a cut depth of 2.0 mm, a roughness value of 2.999 (μm) was recorded as the maximum value. Roughness rises at spindle speeds between 585 and 900 rpm when cutting at shallower depths. The third modeling had the smallest error value, which was 11.21%, and surface roughness value using an artificial neural network with five simple multi-layer models.
Co-Authors Bismantolo, P. H. Setiawan Hasanah, Y. Iskandar Iskandar Qhalbi, A.N. Rizal, A. S. Gayatri Sayid Machdar Sutrisno, M. Utama, F.P.