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All Journal Journal of Intelligent Software Systems Jurnal Online Skripsi Manajemen Rekayasa Konstruksi (JOS-MRK)
Ramadhan, Alfitra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Transportation

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Lecture Scheduling Using Genetic Algorithm Method Liyan, Sur; Kriestanto, Danny; Ramadhan, Alfitra; Haries, Muhammad; Lukman, Lukman
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1501

Abstract

Lecture scheduling at a university is a very important element, because it determines the progress of the lecture activity process. At the Indonesian Digital Technology University, the lecture scheduling process still uses Microsoft Excel, this is considered less than optimal because it takes a relatively long time, the process is long and requires a high level of accuracy, which is something that often becomes an obstacle in the scheduling process. The genetic algorithm is an algorithm that can be used to solve problems on a large scale and with a high level of complexity, such as lecture scheduling. Genetic algorithms have advantages over other optimization methods, namely that genetic algorithms can optimize problems with complex problems and a very wide search space. There are several stages in a genetic algorithm, namely: initial population initialization, fitness evaluation, selection, crossover and mutation. The results of this research show that scheduling lectures using the genetic algorithm method results in faster and more accurate results, because the process is carried out by the program by finding the best solution from each generation iteration and the process will stop when the required solution is obtained. Meanwhile, scheduling lectures using MS Excel takes longer because it is done manually with the help of the VLOOKUP formula and requires a high level of accuracy so that there are no conflicting lecture schedules. From the test results, using Python software with a genetic algorithm takes 0.609356 seconds with an accuracy level of 100%. Meanwhile, testing using MS Excel with VLOOKUP takes around 20 minutes with an accuracy rate of 95%.Keywords— Scheduling, Lectures, Genetic Algorithm
OPTIMASI ALAT BERAT PEKERJAAN GALIAN DAN TIMBUNAN PROYEK JALAN TOL SOLO – YOGYAKARTA PAKET 1.1 STA 3+000 – STA 6+000 Ramadhan, Alfitra; Setiono, Joko; Riskijah, Sitti Safiatus
Jurnal Online Skripsi Manajemen Rekayasa Konstruksi (JOS-MRK) Vol. 5 No. 1 (2024): EDISI MARET
Publisher : Jurusan Teknik Sipil Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jos-mrk.v5i1.3749

Abstract

Sering dijumpai alat berat dalam keadaan aktif namun tidak terfungsikan saat pekerjaan berlangsung karena target produktifitas alat tidak tercapai atau antrian muatan. Hal ini mengakibatkan kurang efektif dan efisien yang berdampak pada pembengkakan biaya dan memperlambat waktu pengerjaan. Sehingga diperlukan kombinasi alat berat agar tidak terdapat alat berat yang tak terfungsikan. Tujuan dari penelitian ini adalah untuk mengoptimasi alat berat dan menghitung biaya alat berat yang optimal. Penentuan solusi optimal menggunakan metode simpleks program linier. Hasil penelitian diperoleh: Volume pekerjaan striping 49.168,358 m3, pekerjaan galian 187.370,630 m3, dan pekerjaan timbunan 978.533,840 m3. Pekerjaan striping menggunakan alat excavator, dump truk, dan bulldozer dengan 8 alternatif kombinasi, pekerjaan galian menggunakan alat excavator, dumptruk, dan bulldozer dengan 8 alternatif kombinasi, dan pekerjaan timbunan menggunakan alat excavator, dumptruk, motor grader, vibratory roller, dan water tank truk dengan 8 alternatif kombinasi. Kombinasi alat berat yang optimal yaitu pekerjaan striping pada alternatif kombinasi tujuh dengan total biaya Rp 1,194,132,280; Pekerjaan galian pada alternatif kombinasi tujuh dengan total biaya Rp 4,716,870,141; dan Pekerjaan Timbunan pada alternatif kombinasi tiga dengan total biaya Rp 13,231,549,342. Jadi total biaya alat berat pada pekerjaan stripping, pekerjaan galian, dan pekerjaan timbunan sebesar Rp 19,142,551,762.
Co-Authors Danny Kriestanto, Danny Haries, Muhammad JOKO SETIONO Liyan, Sur Lukman Lukman Sitti Safiatus Riskijah