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All Journal Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Pelita - Jurnal Penelitian Mahasiswa UNY JUITA : Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Farmasi Indonesia Jurnal Tadbir Peradaban Jurnal Atma Inovasia KONTAN: Jurnal Ekonomi, Manajemen dan Bisnis JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Tika Novita Sari
Unknown Affiliation
Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Medicine & Pharmacology Social Sciences Other

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Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen dengan Bayesian Optimization Yudianto, Muhammad Resa Arif; Zakariah, Masduki; Rozam, Nadhir Fachrul; Rahman, Dzul Fadli; Sari, Tika Novita; Mustofa, Zaenal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11524

Abstract

This study examines the effect of Bayesian Optimization in improving the performance, computational efficiency, and sustainability of Aspect-Based Sentiment Analysis models using Support Vector Machine (SVM). A dataset consisting of 988 customer reviews about Borobudur Temple, classified into six dimensions: Attractiveness, Facilities, Accessibility, Visual Image, Price, and Human Resources is used to compare two scenarios, namely Baseline SVM and SVM enhanced with Bayesian Optimization (BO). Important metrics used include accuracy, computational duration, energy usage, and carbon emissions. The results show that BO significantly improves accuracy, especially on difficult aspects such as Facilities (from 0.7294 to 0.8682) and Price (from 0.8047 to 0.9576). The most complicated aspect, namely visual image due to the very minimal number of datasets (unbalanced), achieved an increase in accuracy from 0.6729 to 0.72. In addition, BO reduces training time, especially for resource-intensive tasks such as the visual image aspect, reducing training time from 13.04 seconds to 9.4 seconds. Substantial reductions in energy consumption and CO₂ emissions are seen in line with sustainable machine learning principles. The hyperparameter adaptability of SVM, with linear kernels performing well in simpler tasks, while polynomial and sigmoid kernels improve performance for more complex parts. BO substantially alleviates the limitations of Baseline SVM, offering a robust, efficient, and environmentally friendly solution for ABSA. Future research can explore more enhancements for complex tasks to improve performance and efficiency.
A Hybrid Case-Based Reasoning Framework Using KNN, Word2Vec, and Cosine Similarity for Employee Attrition Analysis Siregar, Akhmad Arif Faisal; Utami, Ema; Sari, Tika Novita
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Employee attrition prediction remains a longstanding challenge in human resource analytics, as organizations increasingly depend on computational decision-support systems that are transparent, consistent, and operationally accountable. Conventional methods that rely solely on numerical attributes are restricted in their ability to accurately capture the structural and contextual relationships inherent in categorical and text-based employee descriptors. To overcome this limitation, the current study investigates a hybrid Case-Based Reasoning (CBR) retrieval framework that combines K-Nearest Neighbors (KNN) with Word2Vec embeddings derived from the dataset's limited textual attributes, specifically Department, Gender, EducationField, MaritalStatus, and OverTime. Eight experimental configurations were assessed to examine the impact of alternative similarity metrics and diverse feature representations. The optimal configuration of KNN, enhanced with Word2Vec embeddings and cosine similarity, attained an accuracy of 0.8526 and a weighted F1-score of 0.8000, thereby exceeding the performance of baseline models based solely on numerical features and those utilizing Manhattan distance. Nonetheless, the improvements in performance remained limited owing to dataset-specific limitations, such as class imbalance and the inherently superficial characteristics of the textual descriptors, which restrict the semantic richness of Word2Vec embeddings. Furthermore, the IBM attrition dataset does not encompass downsizing or termination situations, highlighting conceptual and ethical constraints when utilizing similarity-based predictions for high-stakes HR decisions. Overall, the findings indicate that hybrid similarity representations, particularly the combination of Word2Vec embeddings with cosine distance, can improve the structural expressiveness of CBR, although their predictive effectiveness is still limited by data sparsity and considerations of fairness.
Co-Authors Andiny, Intan Suci Arih Afra Inayah Darmeinis, Darmeinis Dewi, Lucia Vita Inandha Dewita, Viora Rahma Dyah Susilowati Dzul Fadli Rahman Ema Utami Isjoni Isjoni Liza, Asma Masduki Zakarijah Muhammad Resa Arif Yudianto Nadhir Fachrul Rozam Nafngan Fitriansah Rahmadina, Salsabilla Rahman, Dzul Fadli Ramdani Bayu Putra Ria Astriratma Siregar, Akhmad Arif Faisal Sri Mulyana Tesmanto, Joni Tri Hardiyanti Tugiman ' Zaenal Mustofa Zakariah, Masduki