Article Per Year (5 Year)
p-Index From 2020 - 2025
2.083
P-Index
This Author published in this journals
All Journal ASEAN Journal of Systems Engineering Majalah Forum Teknik UGM Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) J@TI (TEKNIK INDUSTRI) Jurnal Teknosains Journal of Indonesian Economy and Business Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Angkasa: Jurnal Ilmiah Bidang Teknologi International Journal of Supply Chain Management International Journal of Industrial Engineering and Engineering Management JURNAL TEKNIK INDUSTRI Prosiding Seminar Nasional Teknik Industri
Nur Aini Masruroh
Fakultas Teknik, Jurusan Teknik Mesin Dan Industri, Universitas Gadjah Mada Jl. Grafika 2, Kampus UGM, Yogyakarta 55281
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Teknosains

 1 
OPERATIONS RESEARCH STRATEGI EFISIENSI BERMULA DARI PERANG Nur Aini Masruroh
Jurnal Teknosains Vol 3, No 2 (2014): June
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.6028

Abstract

Penggunaan nama operations research inimemang tidak dapat lepas dari sejarah awalperkembangan ilmu ini. Berawal dari zamanperang dunia kedua, ketika pemerintahanInggris dan Amerika menghadapi permasalahanterbatasnya ketersediaan logistikperang yang harus dialokasikan ke beberapaoperasi militer dan aktivitas lain yangmendukung operasi militer ini sehinggamemerlukan sebuah strategi distribusilogistik yang efektif dan efisien. Gunamerumuskan strategi ini, maka dibentuklahtim yang terdiri dari sejumlah ilmuwanuntuk mengaplikasikan pendekatan ilmiahuntuk memecahkan permasalahan ini. Timini diberi tugas untuk melakukan research on(military) operations. Tim ilmuwan inilah yangselanjutnya dikenal sebagai tim operationsresearch (OR) yang pertama kali. Salah satuhasil dari tim ini adalah hasil risetnya tentangstrategi pengaturan operasi kapal selam yangmenghasilkan kemenangan dalam perang diAtlantik utara.
PENGEMBANGAN MODEL MATEMATIKA JARINGAN SUPPLY CHAIN DENGAN MEMPERTIMBANGKAN EMISI PADA INDUSTRI DAUR ULANG KERTAS Asgar Ali; Nur Aini Masruroh
Jurnal Teknosains Vol 5, No 2 (2016): June
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.8526

Abstract

Recycling is part of the green supply chain management which is developed base on the concept of environmentally friendly industry to respond the various issues regarding the environmental problems of the world. Distribution of recycled products starts from consumers and ends to manufacturing. The objective is to limit the waste in order to save energy and prevent the dumping of hazardous materials into the environment. However there are complexities in the supply chain because of some uncertainties such as the return of recycled product and the environmental impact resulting from the operation of supply chain. In this research, linear programming optimization method is proposed to overcome that problem with maximizing profit is set as the objective function. Emissions resulting from the recycling process are considered in the model. These results indicate that the mathematical model provides profit of Rp. 37.909.659, with 1.137.760 grams of CO2 emissions from the production process, 38.473,4 grams of CO and 5.884,9 grams HC + NOX of transport. While the proposed strategy is use the right vehicles and select the most efficient route according to the paper collected from consumers and sold to manufacturing. For the production process, it is required to add 3 pressing machines and 1 chopped machine, so the capacity of the working hours of 384 hours per month can be increased to 960 hours per month.
APLIKASI JARINGAN SARAF TIRUAN DAN PARTICLE SWARM OPTIMIZATION UNTUK PERAMALAN INDEKS HARGA SAHAM BURSA EFEK INDONESIA Desy Wartati; Nur Aini Masruroh
Jurnal Teknosains Vol 6, No 1 (2016): December
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.27616

Abstract

Jakarta Composite Index (JCI) is the main stock index in Indonesia Stock Exchange, which indicates the movement of the performance of all stocks listed. The data of stock price index often experience rapid fluctuations in a short time, so it is needed to carry out an analysis to help investor making the right investment decisions. Forecasting JCI is one of the activities that can be done because it helps to predict the value of the stock price in accordance with the past patterns, so it can be a consideration to make a decision. In this research, there are two forecasting models created to predict JCI, which are Artificial Neural Network (ANN) model with (1) Backpropagation algorithm (BP) and (2) Backpropagation algorithm model combined with Particle Swarm Optimization algorithm (PSO). The development of both models is done from the stage of the training process to obtain optimal weights on each network layer, followed by a stage of the testing process to determine whether the models are valid or not based on the tracking signals that are generated. ANN model is used because it is known to have the ability to process data that is nonlinear such as stock price indices and PSO is used to help ANN to gain weight with a fast computing time and tend to provide optimal results. Forecast results generated from both models are compared based on the error of computation time and forecast error. ANN model with BP algorithm generates computation time of training process for 4,9927 seconds with MSE of training and testing process is respectively 0,0031 and 0,0131, and MAPE of forecast results is 2,55%. ANN model with BP algorithm combined with PSO generates computation time of training process for 4,3867 seconds with MSE of training and testing process is respectively 0,0030 and 0,0062, and MAPE of forecast result is 1,88%. Based on these results, it can be concluded that ANN model with BP algorithm combined with PSO provides a more optimal result than ANN model with BP algorithm.
Co-Authors Anggita Virgiana Prasetyorini Anggita Virgiana Prasetyorini, Anggita Virgiana Anna Maria Sri Asih Annisa Dewi Akbari, Annisa Dewi Ari Ariangga Orranius Putra Patarru Aris Prasetiyo Asgar Ali Budi Hartono Deny Ratna Yuniartha Desy Wartati Dinda Safitri Ramadhani Dzakiyah Widyaningrum Hari Agung Yuniarto Indah Puspitasari Irwan Aufi Istikomah, Sintha Jecky, Jecky Joko Waluyo Joko Wintoko Kusnanto Kusnanto Miranda, Suci Mirwan Ushada Muhammad Kusumawan Herliansyah Niken Siwi Pamungkas Nur Mayke Eka Normasari Pramuditha, Zita Iga Putri Vicky Hapsari Rochim Bakti Cahyono Rony Arjuna Safira, Aretha Setiawan, Fran Setiawan, Fran Subagyo . Susana Evayanti Titis Wijayanto Trifandi Lasalewo Yang, Chao Lung Zita Iga Pramuditha