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All Journal Diponegoro Journal of Accounting Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Pendas : Jurnah Ilmiah Pendidikan Dasar TLEMC (Teaching and Learning English in Multicultural Contexts) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Abdimas Terapan: Jurnal Pengabdian Kepada Masyarakat Terapan Student Research Journal DIMASEKA Gudang Jurnal Ilmu Kesehatan PUDAK: Local Wisdom Community Journal Qoumun: Journal of Social and Humanities
Firda Amalia
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Employees’ Satisfaction and Sentiment Analysis toward BERSATU Application Ayub Prasetyo; Hotniar Siringoringo; Firda Amalia
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 1 (2025): June 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.1.21-32

Abstract

The era of digitalization has boosted companies to adopt innovative technology in seizing competitive superiority. However, adaptation to the change often deals with constraints, affecting operational efficiency and effectiveness. In this regard, PT Beiersdorf Indonesia faces issues regarding the impact of digital transformation of the sales process from manual to digital using Beiersdorf Sales Activity Tracking Routine (BERSATU) application, such as the difficulty of technology adaption by employees, accustomed to manual method, limitations of internet access in some areas, and technical constraints on application as well as insufficient technical support. Therefore, the research aims to analyze sentiment of user review on “BERSATU” application, using various algorithm classification and modeling topic. A total of 600 user reviews had been collected and analyzed to know positive or negative sentiments. Of 600 reviews, 60.33% was positive and 39.67% was negative. Based on the evaluated five algorithms classification, SVM and Naïve Bayes were superior with accuracy above 97% with better F1-Score. Regression Logistics had 96% accuracy, but it had low recall. Random Forest had 94% accuracy with a better F1-Score, but low recall was received. KNN has 93% accuracy, but low recall was obtained. SVM and Naïve Bayes were the recommended for further analysis. Modeling of LDA topics generated three topics with dominant keywords. Topic 1, such as “application,” “bersatu," “data,” “sales,” and “results,” covered 44.5% of the documents. Topic 2, including “application,” “good,” “visit,” and “order,” was relevant at 29.5%. Topic 3, comprising of “application,” “assistance,” “easy,” “sell,” and “store,” was relevant 26.1% of the documents.
Co-Authors Ahmad Saiyet Amiril Mukminin Akyas Daksani A Albiansyah Albiansyah Aldy Fauzan Abdillah Anifa Mustika Prihatini Anisa Silalatus Sa’diyah Anna Sofiyah Anna Yusholluna Alannabi Arindy Alfa Juniarta Ayu Kumala Sari Ayub Prasetyo Bela Desmita Sari Bella Novitasari Bilqis Eka Maharani Cantika Tresna Rahayu Chelsea Kairadinda Adam Dewi Nur Puspita Sari Dwi Desi Yayi Tarina Elisa Perdinawati Elsa Anggraeni Farikhatur Rizkillah Firda Aulia Azzahro Frizalsyah Fatkhurozi Wibowo Hafis Ramadhan Handika Putra Santoso Herry Laksito Hidayatul Mustafidah Hotniar Siringoringo Inayatullatifah Josoa Muda Kurniawan Kartika Mariskhana Kristina Everentia Ngasu Lily Oktavia M. Asfiyak H M. Ridho Nur Hidayah Malik Maya Faisati Moch. Ilham Rahmad Thulloh Mochamad Hilfi Eka Putra Mochammad Nur Asik Maulana MULYADI Naafi'ah, Lubna Naila Waridatus Sa’adah Salya Sahilla Syahirah Rafah Santika Ulfa Rahmawati Ulhaq Zuhdi Unun Nadilah Venny Fatmawati Vivi Putri Hermalia Wafa Satria Kamil Yulis Trinanda Setyoningrum Zulfiah, Farah