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All Journal JeLAST : Jurnal Teknik Kelautan , PWK , Sipil, dan Tambang Brilliance: Research of Artificial Intelligence
Yudi Pramono
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Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Mathematics Other

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Rancangan Sistem Manajemen Material Pada Proyek Pembangunan Perluasan Hotel Mercure 8 Lantai Pramono, Yudi; Mulyani, RR Endang; ., Lusiana
Jurnal Mahasiswa Teknik Sipil Universitas Tanjungpura Vol 1, No 1 (2014): Jurnal Mahasiswa Teknik Sipil Edisi Februari 2014
Publisher : Jurnal Mahasiswa Teknik Sipil Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sistem ialah suatu totalitas himpunan bagian-bagian yang satu sama lain berinteaksi dan bersama-sama beroperasi mencapai suatu tujuan tertentu didalam suatu lingkaran. Manajemen material didefinisikan sebagai suatu pendekatan organisasional untuk menyelesaikan permasalahan material yang memerlukan kombinasi kemampuan manajerial dan teknis. Pemakaian material merupakan bagian terpenting yang mempunyai persentasi cukup besar dari total biaya proyek. Material konstruksi dalam sebuah proyek diantaranya bahan yang kelak akan menjadi bahan tetap distruktur (bahan permanen) dan material yang dibutuhkan dalam membangun proyek namun tidak menjadi bagian dari struktur (bahan sementara). Penggunaan teknik manajemen yang baik dan tepat untuk membeli, menyimpan, mendistribusikan, dan menghitung material konstruksi menjadi sangat penting. Kegagalan menggunakan dan menjaga suatu sistem manajemen yang sesuai untuk material konstruksi akan berakibat buruk bagi kemajuan dan segi ekonomi pelaksanaan pekerjaan dan berdampak kepada kerugian. Kata kunci : Sistem,manajerial, bahan permanen, bahan sementara
Solar Purchase Volume Prediction Using The K-Nearest Neighbor Algorithm Based On Backward Elimination Prasetyo, Aries Alfian; Pramono, Yudi; Ulfiyah, Laily; Fattah, Misbakhul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4964

Abstract

The profit earned by a Public Filling Station or gas station comes from the purchase of fuel per period and sales in accordance with the volume amount. So the exact purchase volume will determine the turnover of each month. But the profit or turnover is often irregular, there are several causes such as the price of fuel that tends to change, the volume of orders that are not in accordance with consumer demand. With these prediction methods, the expected turnover is increased with more efficient purchases. This research was conducted to study about k-NN Algorithm and then apply k-NN Algorithm in data prediction. The data used are secondary data in the form of data on the number of purchases of BBM in volume liter in the period January 2012 - December 2024. The k values used are k = 1, K = 4, k = 5 and k = 7. Before calculation with k = 1 is done, determined the data of training and data testing, in this research determined as much 70% training data and 30% for data testing. Then the initial cluster determination of the training data based on the interval class. While the cluster in the data testing is determined based on testing with K = 4, k = 5 and k = 7. From the process of analysis and evaluation of the research predicted the volume of fuel purchases using the data ransed dataset that processed data into multivariate data, the process of analysis using K-NN method using 2,3,4 and 5 periods produce the smallest K located in the period to 3, so that the 3rd period will be predicted by K-NN based backward elimination. With the aim of finding the best method to predict the volume of fuel purchases, generate predictions with backward elimination, that the attribute weights in the period xt - 3 and in the period xt 1 selected as the reference in the prediction process, since the weight is 1. K = 13 is the K best way to perform the Analyzing process with K-NN for the prediction of fuel purchase volume, with K = 4 value of 45556,788. So in the analysis and prediction of oil fuel purchasing volume data, for the type of diesel, K is best K = 13 with K-NN analysis method with backward elimination process. The above results show that xt3 or week 3 and week xt1 to 1 in the last period of 2024 can be used as a reference in the purchase in the next year that is 2025.
Solar Purchase Volume Prediction Using The K-Nearest Neighbor Algorithm Based On Backward Elimination Prasetyo, Aries Alfian; Pramono, Yudi; Ulfiyah, Laily; Fattah, Misbakhul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4964

Abstract

The profit earned by a Public Filling Station or gas station comes from the purchase of fuel per period and sales in accordance with the volume amount. So the exact purchase volume will determine the turnover of each month. But the profit or turnover is often irregular, there are several causes such as the price of fuel that tends to change, the volume of orders that are not in accordance with consumer demand. With these prediction methods, the expected turnover is increased with more efficient purchases. This research was conducted to study about k-NN Algorithm and then apply k-NN Algorithm in data prediction. The data used are secondary data in the form of data on the number of purchases of BBM in volume liter in the period January 2012 - December 2024. The k values used are k = 1, K = 4, k = 5 and k = 7. Before calculation with k = 1 is done, determined the data of training and data testing, in this research determined as much 70% training data and 30% for data testing. Then the initial cluster determination of the training data based on the interval class. While the cluster in the data testing is determined based on testing with K = 4, k = 5 and k = 7. From the process of analysis and evaluation of the research predicted the volume of fuel purchases using the data ransed dataset that processed data into multivariate data, the process of analysis using K-NN method using 2,3,4 and 5 periods produce the smallest K located in the period to 3, so that the 3rd period will be predicted by K-NN based backward elimination. With the aim of finding the best method to predict the volume of fuel purchases, generate predictions with backward elimination, that the attribute weights in the period xt - 3 and in the period xt 1 selected as the reference in the prediction process, since the weight is 1. K = 13 is the K best way to perform the Analyzing process with K-NN for the prediction of fuel purchase volume, with K = 4 value of 45556,788. So in the analysis and prediction of oil fuel purchasing volume data, for the type of diesel, K is best K = 13 with K-NN analysis method with backward elimination process. The above results show that xt3 or week 3 and week xt1 to 1 in the last period of 2024 can be used as a reference in the purchase in the next year that is 2025.
Co-Authors Aries Alfian Prasetyo, Aries Alfian Endang Mulyani Fattah, Misbakhul Lusiana . Ulfiyah, Laily