Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Sistem dan Informatika Jurnal Kecerdasan Buatan dan Teknologi Informasi
Sya'roni
Institut Teknologi dan Bisnis Mesuji
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Studi Literature Analisis Potensi Pasar Marketplace terhadap Penjualan Roni, Sya; Crysdian, Cahyo
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 2 (2022): Desember 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i2.9055

Abstract

Marketplace allows Customer to Customer (C2C) transactions between consumers without being bound by place and time. This change also occurs in human spending habits. So it becomes an opportunity for sellers to market their wares. In facing market competition, business people also need analysis to find out what products are selling best. However, there are many factors that affect the complexity of the marketplace in Indonesia. Then a classification-based simulation using KNN and C4.5 is needed, where the weight of each sales product group affects market potential so that it benefits sellers to choose which marketplace is suitable for the goods to be sold. So it can be concluded that (1). Factors that influence the complexity of marketplaces in Indonesia are price, number of sales, discounts, ratings and reviews. (2). The most optimal method used for analysis of sales market potential is K-Nearest Neighbor and C4.5.
Analisis Potensi Marketplace Terhadap Penjualan Menggunakan K-Nearest Neighbors Sya'roni
Jurnal Sistem dan Informatika (JSI) Vol 19 No 2 (2025): Jurnal Sistem dan Informatika (JSI)
Publisher : Direktorat Penelitian,Pengabdian Masyarakat dan HKI - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/jsi.v19i2.724

Abstract

Perkembangan teknologi informasi dan komunikasi telah mengubah pola konsumsi Masyarakat, terutama dalam berbelanja daring melalui platform e-commerce di Indonesia seperti Shopee, Tokopedia, dan Bukalapak. Namun, pelaku usaha masih kesulitan menentukan platform yang paling potensial untuk memasarkan produknya akibat kurangnya informasi dan kemampuan analisis data. Penelitian ini bertujuan untuk menganalisis potensi pasar produk di ketiga marketplace tersebut menggunakan algoritma K-Nearest Neighbors (KNN). Data yang digunakan diperoleh melalui scraping dari masing-masing platform pada tahun 2022, dengan total 2.250 record yang mencakup variabel harga, jumlah penjualan, ulasan, dan diskon. Hasil analisis berdasarkan marketplace dan kategori menunjukkan pola yang jelas, Shopee unggul pada kategori fashion (skor 0,5293), Bukalapak pada elektronik (skor 1,0000), dan Tokopedia pada otomotif (skor 0,1653). Evaluasi menunjukkan bahwa model KNN mampu mengklasifikasikan produk ke dalam kategori terlaris dan tidak terlaris dengan akurasi sebesar 84,62%, dan nilai AUC sebesar 0,9185, yang menunjukkan kemampuan model dalam membedakan produk dengan potensi penjualan tinggi sedang dan rendah. Temuan ini memberikan wawasan strategis bagi pelaku usaha dalam pengambilan keputusan terkait pemasaran dan manajemen stok. Studi ini menekankan pentingnya menggunakan analisis berbasis data untuk meningkatkan daya saing di pasar digital.
PERFORMANCE COMPARISON OF C4.5 AND K-NEAREST NEIGHBOR ALGORITHMS FOR MARKETPLACE SALES POTENTIAL ANALYSIS Sya'roni
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.505

Abstract

The rapid growth of digital marketplace platforms such as Shopee, Tokopedia, and Bukalapak has transformed online business competition and increased the importance of data-driven sales analysis. Marketplace data, including product price, ratings, reviews, sales volume, views, and seller location, contain valuable information that can be utilized to predict product market potential. However, the large volume, heterogeneous characteristics, and dynamic nature of marketplace data make manual analysis inefficient. Therefore, this study aims to analyze and compare the performance of the C4.5 and K-Nearest Neighbor (KNN) algorithms in classifying marketplace sales potential. The dataset used in this research was collected through data scraping from Shopee, Tokopedia, and Bukalapak using the BigSeller application in March 2022, consisting of 21,750 product records with numerical and categorical attributes. Data preprocessing was conducted using Orange Data Mining, including data cleaning, missing value handling, normalization, feature transformation, and dataset partitioning. The classification process categorized products into three market potential levels: low, medium, and high. Model performance was evaluated using a confusion matrix based on accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the C4.5 algorithm outperformed KNN, achieving an accuracy of 0.86, while KNN obtained an accuracy of 0.70. Moreover, C4.5 showed higher precision, recall, and F1-score values, indicating better classification consistency and stability. The findings suggest that C4.5 is more effective for marketplace sales potential classification due to its ability to identify influential attributes and manage heterogeneous marketplace datasets. This study contributes to marketplace sales prediction and supports data-driven decision-making in e-commerce environments.
Co-Authors Cahyo Crysdian