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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Informasi dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Kontigensi: Jurnal Ilmiah Manajemen Nusantara Journal of Computers and its Applications TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Ira Puspitasari
Information Systems Study Program, Faculty Of Science And Technology, Airlangga University, Mulyorejo Street, Surabaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences Other

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Opinion mining toward work from office policies on post-pandemic covid-19 by using supervised learning Tri Hadi Wicaksono; Imam Yuadi; Ira Puspitasari
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 1 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i1.525

Abstract

Post-pandemic COVID-19, many companies have re-implemented work from office policies for their employees. However, the policy has been controversial among social media activists, especially in Twitter. The sentiment arose according to them, during the pandemic COVID-19 they believe that working from home has many advantages over working in an office. The emergence of 'work from office' sentiments is an interesting target for opinion-mining research. Opinion mining or sentiment analysis is a general research area of ​​data mining that helps to explore and analyze existing views and opinions to obtain useful information. The analysis process involves the use of machine learning with several supporting algorithms. This study used four classification algorithm models of supervised learning, including naive Bayes, support vector machines, k-nearest neighbors, and random forests.  The selection of those algorithms also aims to find out which model produced a good performance for the results. The performance results of each model were evaluated by the confusion matrix and the k-nearest neighbor algorithm model with an accuracy value of 96.62% was found to give the best results and to be the most used model in the classification process. On the other hand, the algorithm model that obtains the lowest accuracy is a random forest with 72.08%.  
Benchmarking Machine Learning Paradigms for Resume Screening on Imbalanced Data Fitri Noor Febriana; Ira Puspitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7123

Abstract

Manual resume screening is an inefficient and bias-prone process, yet comprehensive benchmarks of machine learning models on imbalanced, real-world recruitment data remain scarce. This study addresses this gap by benchmarking seven models from classical, ensemble, and deep learning paradigms for automated resume classification. Using a private dataset of 2,483 resumes across 24 job categories, this study evaluates the models with distinct TF-IDF and BERT embedding feature pipelines and an adaptive strategy for handling class imbalance (Class Weights, SMOTE, SMOTEENN). The results showed that the XGBoost model achieved the highest performance (weighted F1-score of 0.779), followed by the highly competitive BERT (F1 0.728) and Random Forest (F1 0.711) models. Despite these methods, all models struggled with extreme minority classes, confirming data scarcity as a primary limitation. This study provides a valuable benchmark and an evidence-based framework for HR practitioners, highlighting the critical trade-off between predictive performance (XGBoost), interpretability (Random Forest), and semantic capability (BERT). The findings conclude that the primary challenge is data representation, steering future work towards data augmentation and fairness audits.
Co-Authors Andi H Zaidan Badrus Zaman Elzha Odie Syahputra Febdian Rusydi Ferry Jie Fitri N Febriana fitri noor Febriana Furqon Sandiva Utomo Putra I.M. Ayub Imam Yuadi Indra Kharisma Raharjana Irvan Zidny M.N.F. Jamaluddin Mochammad Edris Effendi Nurul Firdausy Okfalisa Okfalisa Ray Adderley JM Gining Rivanda Putra S.S.M. Fauzi Salsabila Devina Atmaranti Samuel E P P Masan Sembiring, Rinawati Tri Hadi Wicaksono Umi Athiyah Wahyuri Wahyuri Yunita Nita