Dunia menghadapi ancaman pandemi Covid-19 pada akhir tahun 2019. Tanggapan terhadap situasi pandemi menimbulkan perubahan dalam cara bekerja dan memaksa sebagian besar pekerja non-esensial untuk beradaptasi dengan bekerja dari jarak jauh. Setelah penyebaran virus dan tingkat kematian akibat Covid-19 mulai menurun, organisasi dan perusahaan mulai memberlakukan kembali kebijakan WFO. Namun, kembalinya aktivitas bekerja secara normal tidak disambut baik oleh para pekerja. Sejumlah survei mengungkapkan bahwa banyak pekerja yang enggan kembali bekerja di kantor setelah beradaptasi dengan bekerja dari rumah selama dua tahun pandemi. Penelitian menggunakan experimental research untuk melakukan analisis sentimen dan pemodelan topik terhadap kebijakan WFO. Analisis sentimen dilakukan dengan membandingkan lima algoritma pembelajaran mesin, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, dan Neural Networks. Sedangkan pemodelan topik menggunakan algoritma Latent Dirichlet Allocation. Penelitian menggunakan data dari Twitter yang diambil sejak bulan Januari 2022 hingga Mei 2023. Berdasarkan hasil eksperimen, algoritma Neural Networks dengan sampel data oversampling memberikan performa terbaik dalam memprediksi sentimen. Model menghasilkan nilai akurasi sebesar 75,61% dan f1-score 75,16%. Berdasarkan hasil penelitian, sentiment yang paling banyak diungkapkan di Twitter terkait kebijakan WFO adalah netral, disusul negatif, dan terakhir positif. Sedangkan dari hasil pemodelan topik, sentiment positif menghasilkan 3 topik, yaitu “keseruan WFO karena bertemu teman kantor”, “bekerja secara WFO meningkatkan fokus dan produktivitas”, serta “aktivitas jajan dan makan siang saat WFO”. Sentimen negatif 4 menghasilkan topik, di antaranya “kemacetan lalu lintas saat WFO”, “peningkatan biaya transport dan pengeluaran saat WFO”, “efek WFO terhadap kesehatan”, serta “kemalasan di pagi hari saat WFO”. Sedangkan sentimen netral menghasilkan 3 topik, yaitu “lowongan kerja hybrid working atau WFA”, “bekerja secara WFO”, dan “rutinintas WFO”.

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The world faced the threat of the Covid-19 pandemic at the end of 2019. The response to the pandemic situation led to changes in the way of working and forced most non- essential workers to adapt to remote working. After the spread of the virus and the death rate due to Covid-19 began to decline, organizations and companies began to re-impose WFO policies. However, the return to normal work activities was not welcomed by workers. Several surveys reveal that many workers are reluctant to return to the office after adapting to working from home during the two years of the pandemic. The research uses experimental research to conduct sentiment analysis and topic modeling on WFO policies. Sentiment analysis is carried out by comparing five machine learning algorithms, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and Neural Networks. Meanwhile, topic modeling uses the Latent Dirichlet Allocation algorithm. The research uses data from Twitter taken from January 2022 to May 2023. Based on experimental results, the Neural Networks algorithm with oversampling data samples provides the best performance in predicting the sentiment. The model produces an accuracy value of 75.61% and an f1-score of 75.16%. Based on research results, the most expressed sentiment on Twitter regarding WFO policies is neutral, followed by negative, and then positive. Meanwhile, from the results of topic modeling, positive sentiment resulted in 3 topics, namely "the excitement of WFO because of meeting office friends", "working in WFO increases focus and productivity", and "lunch activities during WFO". Negative sentiment generated 4 topics, including "traffic jams during WFO", "increased transport costs and expenses during WFO", "effects of WFO on health", and "laziness in the morning during WFO". Meanwhile, neutral sentiment resulted in 3 topics, namely "hybrid working or WFA job vacancies", "working activities in the office", and "WFO routines".