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All Journal Orbith : Majalah Ilmiah Pengembangan Rekayasa dan Sosial Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil
Marchus Budi Utomo, Marchus Budi
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Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Library & Information Science Transportation

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APLIKASI PEMBUATAN DOKUMEN PENAWARAN KONTRAKTOR DI KOTA SEMARANG UNTUK MEMENANGKAN PELELANGAN Utomo, Marchus Budi
Orbith Vol 10, No 1 (2014): Maret 2014
Publisher : Orbith

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Abstract

Kontraktor dalam membuat dokumen penawaran dipengaruhi oleh beberapa komponen yang harus  disesuaikan dengan persyaratan yang terdapat dalam rencana kerja dan syarat-syarat, yang terdiri dari faktor usulan biaya, faktor syarat teknis dan faktor syarat administrasi. Dari hasil pengolahan data dihasilkan bahwa: Pertama usulan biaya (nilai penawaran) mempunyai peluang menjadi pemenang lelang sebesar 58,02% terhadap  penawaran itu sendiri. Kedua Faktor usulan teknis (ustek) mempunyai peluang untuk memenangkan pelelangan sebesar 10,22% terhadap  penawaran itu sendiri Ketiga Faktor administrasi mempunyai peluang untuk memenangkan pelelangan sebesar 31,75 %  terhadap  penawaran itu sendiri. Dengan demikian para penyedia jasa konstruksi secara umum dapat disimpulkan bahwa dalam  pembuatan dokumen penawaran untuk memenangkan pelelangan selalu berkonsentrasi pada ketiga hal tersebut.
PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING Santosa, Stefanus; Suroso, Suroso; Utomo, Marchus Budi; Martono, Martono; Mawardi, Mawardi
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1917

Abstract

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.
PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING Santosa, Stefanus; Suroso, Suroso; Utomo, Marchus Budi; Martono, Martono; Mawardi, Mawardi
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1917

Abstract

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.
PENGARUH PENAMBAHAN LIMBAH PLASTIK JENIS THERMOSETTING TERHADAP MUTU BATA RINGAN (HEBEL) Supriyadi, Supriyadi; Kusdiyono, Kusdiyono; Wahyono, Herry Ludiro; Utomo, Marchus Budi; Nurhadi, Imam
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 2 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i2.2160

Abstract

At this time, plastic is a material that is needed by the wider community and its impact is also extraordinary after the plastic is used in everyday life which can cause serious problems if the management is not done properly. The problem of plastic waste does not only occur in the city of Semarang, but also in other cities, so that the Ministry of Environment and Forestry to impose a program to use paid plastic bags in the short term. But this is only to deal with problems in the short term. In the long run, it will not solve the problem of "plastic waste", because the policy actually encourages people to buy plastic which, of course, will add a new burden on the community to buy it. The results showed that the compressive strength of light brick with the model / type of BN s.d. B10.0 with 10 variations of the mixture of the addition of plastic waste starting from (1.0 to 10.0)% to the weight of Portland Cement (PC) there is a decrease in the average compressive strength. The lowest was the addition of 10.0% plastic waste with an average compressive strength of 9.88 kg / cm². The regression equation obtained Y = 0.042 X ² - 1.177 X + 18.84 with a correlation value R ² = 0.934, meaning that the addition of plastic waste ranging from (1.0 to 10.0)% of the weight of Portland Cement (PC) has the effect of "very strong "against the compressive strength. So we can get a picture that by adding the addition of plastic waste affects the compressive strength decreases. So that it can be investigated with other compositions by adding Fly Ash and sand made with a mixture ratio of 1 PC: 2 Aggregate (Sand and Fly Ash), with the hope that this Fly Ash waste can also be used for lightweight brick building elements
Co-Authors Abd. Rasyid Syamsuri Kusdiyono, Kusdiyono Martono Martono Nurhadi, Imam Stefanus Santosa Supriyadi Supriyadi Suroso Suroso Wahyono, Herry Ludiro