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All Journal Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) IJMST
Sulton, Chaerus
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Computer Science & IT Decision Sciences, Operations Research & Management Education Social Sciences

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Determinasi Produktivitas Kerja: Analisis Penggunaan WhatsApp, Kemudahan Akses Informasi, dan Waktu Pelaporan Sulton, Chaerus; Ali, Hapzi
JURNAL MANAJEMEN PENDIDIKAN DAN ILMU SOSIAL Vol. 6 No. 4 (2025): Jurnal Manajemen Pendidikan dan Ilmu Sosial (Juni - Juli 2025)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jmpis.v6i4.4983

Abstract

Determinasi Produktivitas Kerja: Analisis Penggunaan WhatsApp, Kemudahan Akses Informasi, dan Waktu Pelaporan adalah artikel ilmiah studi pustaka dalam ruang lingkup Manajemen Sumber Daya Manusia (MSDM). Tujuan artikel ini membangun hipotesis pengaruh antar variabel yang akan digunakan pada riset selanjutnya. Objek riset pada pustaka online, Google Scholar, Mendeley dan media online akademik lainnya.  Metode riset dengan library research bersumber dari e-book dan open access e-journal. Analisis deskriftif kualitatif.  Hasil artikel ini: 1) Penggunaan WhatsApp berpengaruh terhadap Produktivitas Kerja; 2) Kemudahan Akses Informasi berpengaruh terhadap Produktivitas Kerja; dan 3) Waktu Pelaporan berpengaruh terhadap Produktivitas Kerja.
Sentiment Analysis of User Reviews of TikTok App on Google Play Store Using Naïve Bayes Algorithm Hasanah, Rakyatol; Sani SR, Sahrul; Munzir, Misbahul; Firdaus, Asno Azzawagama; Sulton, Chaerus; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.58-64.2025

Abstract

In recent years, user interaction through mobile applications has grown rapidly, making user reviews an important source of feedback for improving service quality. This study explores sentiment analysis on 5,000 user reviews of the TikTok application, collected from the Google Play Store using the google-play-scraper library. The data underwent several preprocessing steps, such as case folding, text cleaning, and selecting relevant columns like review content and rating score. Sentiment labeling was based on rating values: scores of 4 and 5 were treated as positive, while scores of 1 and 2 were considered negative. From the results, it was observed that negative reviews appeared more frequently, indicating an imbalance in the dataset. Despite this, the Naïve Bayes classification algorithm still achieved a reasonably good performance in categorizing the sentiments. These findings suggest that even with simple models, valuable insights can be gained from user-generated content. Moreover, the results provide meaningful input for TikTok developers to better understand user concerns and emphasize the potential need for applying balancing techniques in future analysis. Further studies are encouraged to explore other algorithms that may improve sentiment classification accuracy on more complex datasets.    
Co-Authors Asno Azzawagama Firdaus Hapzi Ali Hasanah, Rakyatol Muhajir Yunus Munzir, Misbahul Sani SR, Sahrul