Article Per Year (5 Year)
p-Index From 2021 - 2026
3.947
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Simetris JOIV : International Journal on Informatics Visualization IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Paradigma Journal of Information Technology and Computer Science (JOINTECS) Jurnal CoreIT Techno Nusa Mandiri : Journal of Computing and Information Technology Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research INTI Nusa Mandiri Jurnal Sistem Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Paradigma Jurnal Insan Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS SATIN - Sains dan Teknologi Informasi Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen
Yuris Alkhalifi
Universitas Bina Sarana Informatika, Jakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
DIGITALISASI BERBASIS MICROSOFT OFFICE UNTUK PENGELOLAAN DAN PELAPORAN DATA KELUARGA PADA PEMBERDAYAAN KADER DASAWISMA Yuris Alkhalifi; Khairul Rizal; Amir, Amir; Nur Alam
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 3 No. 6 (2025): Desember
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v3i6.3272

Abstract

Pengabdian ini bertujuan untuk meningkatkan kapasitas Kader Dasawisma RT. 10 Mampang Prapatan dalam mengelola dan melaporkan data keluarga melalui penerapan digitalisasi berbasis Microsoft Office. Metode pengabdian yang digunakan adalah hands-on training yang menekankan praktik langsung, pendampingan personal, serta penyediaan modul digitalisasi sebagai panduan standar. Hasil pengabdian menunjukkan adanya peningkatan signifikan dalam keterampilan kader dalam menggunakan Microsoft Excel untuk pengolahan data keluarga berbasis NIK, Microsoft Word untuk penyusunan laporan bulanan yang baku, serta Microsoft PowerPoint untuk visualisasi data dalam bentuk grafik. Simpulan dari kegiatan ini adalah bahwa digitalisasi berbasis Microsoft Office efektif dalam meningkatkan akurasi, efisiensi, dan kerapian administrasi data keluarga, serta mendukung terwujudnya sistem pendataan yang lebih modern, terstruktur, dan berkelanjutan di tingkat komunitas.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA Mery Oktaviyanti Puspitaningtyas; Kartika Puspita; Yuris Alkhalifi; Yulita Ayu Wardani
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.362

Abstract

Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
Co-Authors Achmad Fachrurozi Achmad Rifai Agung Fazriansyah Agung Fazriansyah Ainun Zumarniansyah Aldo Erianda, Aldo Amir Amir Amir Amir Amir Anas Faisal Annisa Elfina Augustia Annisa Elfina Augustia Destia Sari Rahmadhani Fadillah Dinar Ismunandar Dinar Ismunandar Dipa Putra Pratama Ferda Ernawan Firstianty Wahyuhening Fibriany Fitra Septia Nugraha Gata, Windu Harsono, Muhammad Luthfiy Kurniawan Hasan Basri Imam Budiawan Irmawati Irmawati Irwan Herliawan Ispandi Ispandi, Ispandi Kartika Puspita Kartika Puspita Khairul Rizal khairul rizal Kudiantoro Widianto, Kudiantoro Mayangky, Nissa Almira Mery Oktaviyanti Puspitaningtyas Mohammad Syamsul Azis Muhammad Ilham Prasetya Muhammad Luthfiy Kurniawan Harsono muhammad luthfiy kurniawan harsono Muhammad Rifqi Firdaus Nila Hardi Nur Alam Nurajijah Nurajijah Oky Kurniawan Prasetya, Arfhan Puspita, Kartika Qudsiah Nur Azizah Rahdian Kusuma Atmaja Resi Taufan Rian Ardianto Rifki Sadikin Rino Ramadan Shahreen Kasim, Shahreen Tika Adilah M Tri Rivanie Windu Gata Windu Gata Yan Rianto Yulita Ayu Wardani Zahrah Rachmawaty