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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi
Indra, Ahmad
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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PERANCANGAN SISTEM INFORMASI PRODUK DAN JASABERBASIS WEB PADA PT LASER JAYA SAKTI Indra, Ahmad
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 2 (2018): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.184 KB)

Abstract

Di zaman yang serba teknologi sebuah perusahaan membutuhkan website untuk mengkomunikasikan produk dan jasa khususnya untuk PT Laser Jaya Sakti. Sebelumnya PT Laser Jaya sakti mengalami masalah pada customer yang tidak bisa melihat secara langsung kegiatan pengolahan produk fabrikasi gas dan minyak PT Laser Jaya Sakti di karenakan kesibukan dan daerah wilayah yang jauh.Penulis membuat website untuk PT Laser Jaya Sakti menggunakan PHP dan Mysql, dengan adanya sebuah website untuk PT laser jaya sakti akan memudahkan customer dan masyarakat umum utnuk melihat detail perusahaan , produk dan kegiatan melalu website PT Laser Jaya Sakti.
Komparasi Naive Bayes dan SVM untuk Analisis Sentimen Pada E-Commerce Seller Center Yanuar Laik, Abraham Adrian; Nabilla, Adinda; Diah, Andi; Sumanto; Indra, Ahmad; Arya, Yudi
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3211

Abstract

The development of e-commerce drives the need to understand customer opinions through sentiment analysis to improveservice quality. Tokopedia and TikTok Shop as popular e-commerce platforms provide a review feature that can be asource of data to analyze consumer perceptions. This study aims to compare the performance of two text classificationalgorithms, namely Naive Bayes and Support Vector Machine (SVM), in analyzing the sentiment of customer reviews takenfrom the TikTok Tokopedia Seller Center dataset. The research method used is a computational experiment with aquantitative approach. The dataset used is sourced from the Kaggle site and is available in clean and labeled conditions(positive and negative). Model evaluation is done by measuring accuracy, precision, recall and F1-score. The results showthat Naive Bayes is superior with 97.50% accuracy and 84.00% F1-score, compared to SVM which obtained 94.90%accuracy and 76.80% F1-score. Thus, Naive Bayes is considered more effective for sentiment analysis of e-commercecustomer reviews
Co-Authors Arya, Yudi Diah, Andi Nabilla, Adinda SUMANTO Yanuar Laik, Abraham Adrian