Pemodelan Reward Rule Game Streamer Indonesia Tingkat Amatir dengan Orange Data Mining

Ambarsari, Erlin Windia

doi:10.30998/string.v4i1.3606

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Oleh sebab itu, dalam kondisi tersebut kemungkinan pemerintah akan memberikan solusi untuk menjaga kestabilan ekonomi di indonesia salah satunya memberikan bantuan kepada para pelaku usaha yang terdampak khususnya UMKM. Namun dalam pemberian bantuan masih terdapat permasalahan yaitu berdasarkan bantuan UMKM di pandemi covid-19 masih banyaknya laporan bahwa bantuan UMKM kurang tepat sasaran dan pemerintah pun kesulitan untuk menganalisis kesesuaian bantuan kepada para pelaku UMKM [1], [2]. Hal tersebut didukung adanya sentimen masyarakat yang terhadap bantuan covid-19 yang cenderung negatif [3].…”

Section: Pendahuluanunclassified

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Suseno,

Amin,

Mahardika

2023

JoSYC

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The Covid-19 pandemic has had an impact on the economies of countries in the world, including Indonesia. The low trade balance, public consumption and the implementation of large-scale social restrictions (PSBB) have caused business actors to suffer losses and even go out of business, especially Micro, Small and Medium Enterprises (MSMEs). Assistance to MSMEs has been provided by the government to maintain the functioning of the economy in the micro environment. However, the provision of this assistance needs to be carried out further analysis because there are reports that the assistance is not on target, causing the budget spent to be ineffective. In this research, an analysis will be carried out on the provision of assistance from the government to MSMEs, especially in Pekalongan district, using data mining techniques, especially classification. The algorithms used are K-Nearest Neighbor (K-NN) and C.45 which are then compared to determine the highest level of effectiveness in recommendations for providing MSME assistance in the District. Pekalongan. The data used was 312 MSMEs and after going through the data preprocessing process we got accurate data, namely 279. The data was divided into 2, namely training data of 200 data and testing data of 79 data. The results of this research, the K-NN algorithm obtained an accuracy level of 94.94%, precision 94.73% and recall 94.73%, while the C.45 algorithm obtained an accuracy level of 86.08%, precision 87.21% and recall 88.3%. Based on the results of this research, it can be concluded that the use of data mining techniques with the K-NN and C4.5 algorithms has a high level of accuracy for recommendations for assistance to MSMEs, however for the best results you can use the K-NN algorithm which has an accuracy level of 94.94%.

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Section: Pendahuluanunclassified

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Suseno,

Amin,

Mahardika

2023

JoSYC

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“…Pemerintah Indonesia melakukan beberapa langkah untuk menjaga aktivitas perekonomian tetap berjalan seperti, melalui Kementerian Perindustrian (Kemenperin) memberikan pinjaman dengan bunga rendah kepada UMKM untuk pengadaan bahan baku, restrukturisasi kredit, menghubungkan para pelaku UMKM dengan platform toko online untuk membantu pemasaran dan penjualan produk-produk UMKM seperti Tokopedia, Shopee, dan Blibli Kementerian Ketenagakerjaan (Kemnaker) memberikan bantuan berupa subsidi gaji atau upah bagi pekerja buruh dalam penanganan dampak Covid-19 Kementerian Koperasi dan Usaha Kecil Menengah (KemenkopUKM) melakukan kelonggaran pembayaran pinjaman, keringanan pajak UMKM enam bulan, transfer tunai untuk bisnis skala mikro. Pemerintah mengupayakan bantuan transfer tunai agar tepat sasaran karena selama ini mengalami kesulitan untuk mendapatkan data yang riil [1]- [2] Menurut ari rohmawati pelatihan serta bantuan dari pemerintah sangatlah penting untuk umkm saat ini agar umkm yang ada di kabupaten pesawaran tidak semakin terpuruk. Namun karena banyaknya umkm yang ada di pesawaran dan belum terklasifikasi dengan baik antara usaha mikro,kecil dan menengah menyebabkan masih banyaknya pemberian bantuan maupun bimbingan dari pemerintah yang tidak tepat sasaran.…”

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Klasifikasi Pemberian Bantuan UMKM Cirebon dengan Menggunakan Algoritma K-Nearest Neighbor

Azizah

Nurdiawan

Dwilestari

et al. 2022

JoSYC

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The Indonesian government in obtaining Real Time data on MSMEs who are entitled to assistance, accuracy in distributing MSME assistance, and accelerating Indonesia's economic growth through MSMEs, especially the Cirebon Regency area. There are several ways so that cash transfer assistance for micro-scale SMEs from the government is right on target, in this study the authors will use data mining techniques with the k-nearest neighbors method in classifying receiving assistance from SMEs. The data used in this study uses secondary data with attributes of Regency, District, Business Name, Product Name, Business License, Assets and Turnover. The application of the KNN algorithm uses the retrieval operator, cross validation, and in developing the model using the KNN algorithm operator, apply model and performance. The results of the accuracy are 98.46 % with details, namely the Prediction Results are Eligible and it turns out to be true as many as 339 Data. The Prediction Result is Eligible and it turns out to be true Not Eligible as much as 2 Data. Prediction results are not eligible and it turns out to be true as much as 4 data. Prediction results are not eligible and it turns out to be true, 42 data are not eligible. Recommendations for the pattern of knowledge obtained using the K-NN algorithm. Researchers provide recommendations that are feasible to be given assistance for MSMEs as many as 339 MSME participant data spread across the Cirebon district and included in the affected category. Then there are several MSME participants who cannot receive MSME assistance according to the application of the KNN algorithm, which is 42 data, and there are 2 data from participants who are proposed to receive MSME assistance. The hope of the research for participants who receive assistance from the government can survive in conditions like this covid 19

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Orange Software Usage in Data Mining Classification Method on The Dataset Lenses

Ishak¹,

Siregar²,

Aspriyati³

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Data Mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from various large databases. Decision tree is a very interesting classification method that involves the construction of a decision tree consisting of decision nodes which are connected by branches from the root node to the leaf node (end). The problem that will be examined in this study is about classification of dataset lenses obtained from using orange software. The method used in this paper is the method of classification process is performed on a decision tree using the orange software. Tree construction begins with the formation of roots (located at the top). Then the data is divided based on attributes that are suitable to be used as leaves. Decision rule information is to make decision rules from trees that have been formed.

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Pemodelan Reward Rule Game Streamer Indonesia Tingkat Amatir dengan Orange Data Mining

Cited by 4 publications

References 9 publications

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

Klasifikasi Pemberian Bantuan UMKM Cirebon dengan Menggunakan Algoritma K-Nearest Neighbor

Orange Software Usage in Data Mining Classification Method on The Dataset Lenses

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