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Prediction of the Number of Arrivals of Training Students With the Monte Carlo Method Sopi Sapriadi; Yuhandri Yunus; Rahmatia Wulan Dari
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i1.168

The simulation of predicting student arrivals for training is an estimate of the calculation of the arrival rate of students in a period to conduct training. The number of student visits is too many, sometimes inversely proportional to the programmers who carry out learning, this causes the ongoing service to be less than optimal. This study aims to predict student arrivals in the future better. The data processed in this study were 3 periods sourced from the administration of a private company in West Sumatra. The data will be processed and calculated using the Monte Carlo method. The data were tested with various possible elements using a random sample. A powerful numerical calculation tool by simulating statistical data, this simulation obtains accurate values accurately from the physical form of the system that can be observed. The calculation implementation will be developed using an application-based system that will be built with the Hypertext Preprocessor (PHP) programming language. The system developed is easier and more relevant by applying Information Technology. The results obtained in predicting are 80% for 2017 and 84% for 2018. From the results of 80% accuracy in 2017 and 84% 2018 the system works very well to implement. Based on the results of data processing with the Monte Carlo method, it can be predicted that the number of student arrivals for training, as well as a good and fast decision-making process in the future.

Simulasi Monte Carlo dalam Prediksi Tingkat Penjualan Produk HPAI Rahmatia Wulan Dari
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (2020)
Publisher : SAFE-Network

Predicting sales is an important aspect of sales development. Sales prediction simulation is an estimate about calculating the level of product sales in a certain period. The research objective was to predict the level of sales of HPAI products at HNI Halal Mart. The data used is sales data for HPAI products from 2017 to 2019 which are processed using the Monte Carlo method. Based on the results of testing the prediction of the sales level of HPAI products, an average accuracy of 84,5% is obtained, making it easier in the decision making process and helping in choosing a good business strategy.

Prediksi Tingkat Penjualan Pupuk Urea dengan Metode Monte Carlo Rahmatia Wulan Dari
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i4.251

The development of science and information technology over time is very rapid in today's era, one of which is the agricultural sector. In the agricultural sector, there are many things that utilize technology, such as shallot cultivation. The need for shallots is very high, so many farmers plant shallots. To produce shallots of good quality and have a high selling price, farmers provide nutrition for the shallots they plant. The nutrient that onions really need is urea fertilizer. The fluctuating need for fertilizer for each farmer results in the availability of fertilizer in Kiosks often experiencing shortages. This has an impact on the scarcity of the availability of urea fertilizer. So this research was carried out to predict the level of sales of urea fertilizer in maintaining the need for fertilizer for shallot plants at Kiosk Pak De. Fertilizer availability aims to prepare stocks to avoid scarcity at a later time. The method used in this study is the Monte Carlo Method. This method is a method that can predict based on repeated random sampling. This method can also be used in various aspects of imputation systems and prediction of missing data. The data used in this study are sales data for urea fertilizer from 2020 to 2021. Sales data for 2020 are used to predict sales for 2021 and sales data for 2021 are used to predict sales for 2022. The results obtained from this study are the prediction rate for in 2020 with an accuracy rate of 92% and an accuracy in 2021 of 92.25%. From these results it can be concluded that this method can help Kios Pak De in maintaining scarcity in the sale of urea fertilizer

Simulasi Monte Carlo dalam Prediksi Tingkat Penjualan Produk HPAI Rahmatia Wulan Dari
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (2020)
Publisher : SAFE-Network

Predicting sales is an important aspect of sales development. Sales prediction simulation is an estimate about calculating the level of product sales in a certain period. The research objective was to predict the level of sales of HPAI products at HNI Halal Mart. The data used is sales data for HPAI products from 2017 to 2019 which are processed using the Monte Carlo method. Based on the results of testing the prediction of the sales level of HPAI products, an average accuracy of 84,5% is obtained, making it easier in the decision making process and helping in choosing a good business strategy.

Metode Multi Attribute Utility Theory (MAUT) untuk Sistem Pendukung Keputusan Pemilihan Mobil Bekas Rahmatia Wulan Dari; Sopi Sapriadi; Nadya Alinda Rahmi; Pradani Ayu Widya Purnama; Ilmawati
Jurnal KomtekInfo Vol. 10 No. 2 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i2.378

Transportasi merupakan kebutuhan primer dalam memindahkan barang dan orang. Kendaraan pribadi, seperti mobil dan sepeda motor, menjadi preferensi bagi sebagian orang karena kenyamanan dan kemewahan yang ditawarkan. Namun, proses penjualan mobil bekas seringkali menghadapi kendala dalam pencatatan manual. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan menggunakan metode Multi Attribute Utility Theory (MAUT) dalam transaksi penjualan mobil bekas. Sistem ini diharapkan dapat mempermudah dan meningkatkan efektivitas serta efisiensi proses penjualan mobil bekas. Metode MAUT ini memungkinkan penilaian relatif terhadap setiap atribut mobil bekas yang relevan, sehingga memudahkan penjual dalam memilih mobil bekas yang sesuai dengan preferensi dan kebutuhan konsumen. Dataset yang digunakan dalam penelitian ini mencakup informasi tentang mobil bekas, termasuk harga, kondisi mesin, usia, warna, dan atribut lainnya. Data ini digunakan sebagai dasar dalam pengambilan keputusan pemilihan mobil bekas terbaik. Hasil penelitian menunjukkan bahwa sistem pendukung keputusan dengan metode MAUT dapat membantu penjual dalam memilih mobil bekas yang paling sesuai dengan kebutuhan konsumen. Penggunaan sistem ini mempercepat proses pencatatan penjualan mobil bekas, meningkatkan akurasi data, dan memudahkan analisis serta pelaporan. Sistem pendukung keputusan yang dikembangkan dapat menjadi alat yang efektif dan efisien dalam membantu penjual dalam mengambil keputusan yang tepat dalam pemilihan mobil bekas yang akan dijual kepada konsumen.

PESISIR DIGITAL: PENGEMBANGAN SISTEM INFORMASI UNTUK MENINGKATKAN KESEJAHTERAAN NELAYAN Rahmatia Wulan Dari; Ilmawati; Mardhiiah Masril
JURMAS BANGSA Vol. 2 No. 1 (2024): JURMAS BANGSA
Publisher : Riset Sinergi Indonesia

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

Pengabdian ini bertujuan untuk meningkatkan kesejahteraan nelayan pesisir melalui pengembangan sistem informasi. Permasalahan utama terletak pada akses terbatas nelayan terhadap informasi strategis seperti cuaca, kondisi laut, dan perubahan pasar ikan. TujuanPengabdian adalah mengatasi kendala tersebut dengan merancang sistem informasi yang efektif dan berkelanjutan. Metode yang digunakan melibatkan analisis kebutuhan nelayan, pengembangan aplikasi berbasis teknologi informasi, dan pelatihan penggunaan sistem. Hasil Pengabdian mencakup implementasi sistem informasi yang memberikan akses real-time terhadap informasi strategis, pemantauan pasar ikan, dan edukasi mengenai praktik penangkapan berkelanjutan. Dengan demikian, diharapkan nelayan dapat mengoptimalkan keputusan operasional, meningkatkan efisiensi pemasaran hasil tangkapan, dan berpartisipasi aktif dalam manajemen sumber daya perairan secara berkelanjutan, secara keseluruhan meningkatkan kesejahteraan ekonomi dan lingkungan komunitas nelayan pesisir.

Optimizing Scholarship Recipient Selection in Vocational High Schools: A Strategic Approach with the Simple Additive Weighting (SAW) Method Rahmatia Wulan Dari; Ilmawati; Yesri Elva
Journal of Hypermedia & Technology-Enhanced Learning (J-HyTEL) Vol. 2 No. 2 (2024): J-HyTEL | Meta World
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.v2i2.114

This study aims to address issues in the scholarship recipient selection process in vocational high schools (SMK). The main challenge lies in the limited availability of scholarship, necessitating careful selection. The objective of this study is to implement the Simple Additive Weighting (SAW) method as a solution to enhance the structure, efficiency, and objectivity of the selection process. The SAW method is used as the primary approach, which involves steps such as determining the criteria, assigning preference weights, and ranking alternatives. The data used included a sample of five scholarship candidates from SMK N 1 Hiliran Gumanti, with the criteria divided into benefits and costs. The results of the selection of scholarships demonstrate the success of SAW in providing rankings according to predefined criteria. This research highlights the effectiveness of the SAW method in delivering objective rankings to scholarship candidates. The final results can help the selection team to determine the best scholarship recipients. The implementation of SAW is expected to create a more structured and efficient selection system at the SMK, opening opportunities for improved educational access for financially needy students.

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