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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik JOISIE (Journal Of Information Systems And Informatics Engineering) JATI (Jurnal Mahasiswa Teknik Informatika) Majalah Ilmiah METHODA
Safii, M.
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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Journal : Majalah Ilmiah METHODA

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PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEGUKUR MINAT PENJUALAN SEPATU Lubis, Nur Azizah; Safii, M.; Alfina, Ommi
Majalah Ilmiah METHODA Vol. 13 No. 3 (2023): Majalan Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol13No3.pp337-345

Abstract

The Second Gangbrand shoe shop is one that sells second-hand shoes or what you could also call quality and original second-hand shoes that have certain brands at affordable prices that are cheaper than the original price. This research aims to measure the level of customer interest by comparing the C4.5 algorithm method and the Naive Bayes algorithm. The data source was obtained from second gangbrand stores which were taken based on customer interest. So it is necessary to carry out data analysis to classify customer interest data using the C4.5 and Naive Bayes algorithms to compare accuracy and precision which are the benchmarks in this research. Calculations in this research were carried out manually using Microsoft Excel according to the C4.5 and Naive Bayes algorithm calculation models and then evaluated using the Rapidminer 10.3 tool which was used to help determine accurate values. After conducting research testing, the C4.5 algorithm received an accuracy value of 60.00% and a precision of 50.00%, while the Naive Bayes algorithm received an accuracy value of 60% and a precision of 33.33%. So it can be concluded that the two algorithms have the same accurate accuracy value, but in terms of precision value the C4.5 algorithm is superior in determining customer interest recommendations. It is hoped that the results of this research can provide input and information for future researchers.
Prediksi Jumlah Produksi Kelapa Sawit di Indonesia Menggunakan Algoritma Backpropagation Safii, M.; Alfina, Ommi
Majalah Ilmiah METHODA Vol. 14 No. 2 (2024): Majalan Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol14No2.pp166-174

Abstract

Indonesia is a country that has advantages in the agricultural sector which has the largest plantation and agricultural areas in ASEAN, one of which is oil palm plantations. Indonesia is one of the largest crude palm oil (CPO) business players in the world. More and more palm oil mills and oil palm land are being converted to oil palm cultivation, because oil palm plantations are more beneficial for farmers and palm oil processors. Palm oil plantations are still trying in several ways to maintain stable market demand, one of which is by increasing palm oil production, because palm oil is the main source of other product derivatives. Palm oil production fluctuates every month, but the ups and downs are caused by many factors, namely climate, rainfall, soil fertility, selling prices, and others. Reduced production has a direct impact on the income of farmers and workers in the sector, which in turn can cause economic instability. Actions are needed to ensure the continuity of this industry, one of which is by making predictions. One prediction technique is the Backpropagation artificial neural network. The prediction model can provide very accurate estimates of palm oil production at the provincial level. By analyzing historical data, this research can identify patterns that can help predict future palm oil production. The urgency lies in the strategic role of palm oil in the Indonesian economy.
Co-Authors Alfina, Ommi Belli Nasution Dewi, Rafiqa Fitri Anggraini, Fitri Hambali, Humaidi Hartama, Dedy Lubis, Nur Azizah Syaputri, Vera