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All Journal International Journal of Electrical and Computer Engineering JSI: Jurnal Sistem Informasi (E-Journal) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal ICT : Information Communication & Technology Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of Applied Data Sciences Journal of Community Development Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Wahana Jurnal Penelitian Ilmu Pendidikan Indonesia Wanastra: Jurnal Bahasa dan Sastra Pengabdian: Jurnal Abdimas
Linda Perdana Wanti
Politeknik Negeri Cilacap
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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A Reproducible Explainable NLP Workflow for Workplace Sexism Detection: Classification Performance, Rationale Faithfulness, and Sanity Checks Annisa Romadloni; Linda Perdana Wanti; Laura Sari; Muhammad Nur Faiz; Qisthi Alhazmi Hidayaturrohman
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3222

Abstract

Workplace sexism often appears as indirect, deniable language (e.g., patronizing compliments, competence-doubting questions), making automated detection and organizational response difficult. This study evaluates a transparent, explanation-ready NLP pipeline on the Sexist Workplace Statements (SWS) dataset (1,137 items) with its binary labels: certain sexism vs. ambiguous/neutral. Using the provided fixed stratified split (1,023 train; 114 test), we train a TF–IDF (word 1–2, character 3–5 n-grams) logistic regression baseline and report performance stability across five random seeds. To audit model evidence, sparse token rationales are extracted from linear feature contributions and quantify faithfulness with ERASER-style comprehensiveness (logit drop when rationales are removed) and sufficiency (logit change when only rationales are kept), benchmarked against random-token rationales. The baseline achieves 0.768 ± 0.006 accuracy and 0.759 ± 0.007 macro-F1, with errors concentrated in the ambiguous/neutral class. Faithfulness tests show that model-selected rationales substantially affect the sexism logit (comprehensiveness 1.335 ± 0.001), while remaining insufficient in isolation (|sufficiency| 1.075 ± 0.006). Sanity checks reveal modest sensitivity to gender-term swaps and reduced rationale overlap underweight randomization. Overall, results motivate cautious deployment: explanation-driven auditing can surface shortcut risks and clarify where binary labels blur neutral language and deniable sexism, pointing to future work on finer-grained annotation and human rationale collection.
Gendered Self-Perceptions, Inclusive Classroom Climate, and Responsible Generative-AI Use in English for Specific Purposes Annisa Romadloni; Linda Perdana Wanti; Laura Sari
Jurnal Penelitian Ilmu Pendidikan Indonesia Vol. 5 No. 1 (2026)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpion.v5i1.1071

Abstract

Gendered perceptions shape participation and belonging in higher education, and the rapid uptake of generative AI adds new equity and academic-integrity risks in English for Specific Purposes (ESP). This study examined how communal/agentic self-perceptions and perceived gender-inclusive classroom climate relate to responsible generative-AI orientations among Indonesian vocational students. A cross-sectional quantitative secondary analysis was conducted using an end-of-course survey (N=90) with reliability, descriptive, correlational, and regression analyses. Results indicated high communal and moderate agentic self-perceptions, generally positive inclusion perceptions with lingering stereotype signals in group tasks, and high perceived AI utility alongside strong concerns about inaccurate and biased outputs. Inclusion climate and perceived AI utility jointly predicted stronger governance-oriented norms (e.g., disclosure, citation, fairness). Scenario judgments rated AI most acceptable for summarizing, translating, and language correction when students revised/verified outputs, and least acceptable for generating whole reports or slide decks without meaningful authorship.
Evaluasi Kinerja Model Machine Learning dalam Klasifikasi Penyakit THT: Studi Komparatif Naïve Bayes, SVM, dan Random Forest Nur Wachid Adi Prasetya; Linda Perdana Wanti; Riyadi Purwanto; Isa Bahroni; Rostika Listyaningrum
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2798

Abstract

Classification of Ear, Nose, and Throat (ENT) diseases is essential to support faster and more accurate diagnosis. However, no prior studies have specifically compared the performance of Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in ENT cases. This study aims to evaluate and compare the three classification models in identifying ENT diseases with or without comorbidities. Medical record data were processed through preprocessing, feature selection using ANOVA, and class balancing with SMOTE. The results showed that SVM outperformed the other models with the highest accuracy (59%), followed by Random Forest (57%), and Naïve Bayes (48%). SVM demonstrated superior performance due to its consistent scores across all evaluation metrics. The study concludes that the choice of classification model significantly impacts the accuracy of ENT disease diagnosis.
Support Vector Machine (SVM) - Based Optimization of Leukemia Cell Image Classification Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.2974

Abstract

Leukemia is a type of blood cancer characterized by the uncontrolled proliferation of abnormal white blood cells that originate from the bone marrow. Early detection of leukemia poses a significant challenge in the medical field, as the conventional diagnostic process still relies on manual microscopic observation by hematologists, which is time-consuming and prone to subjective errors. This study aims to analyze the potential of the Support Vector Machine (SVM) algorithm in optimizing the classification of leukemia cell images based on morphological and texture features extracted from microscopic images. The test results show that the SVM model with the RBF kernel provides the best performance with an accuracy of 96.4%, a precision of 95.8%, a recall of 96.1%, and an F1-score of 96.0%, surpassing the results of linear and polynomial kernels. The analysis shows that the use of a combination of shape and texture features has a significant effect on improving classification accuracy.
Studi Perbandingan Kinerja Support Vector Machine Pada Klasifikasi Diabetes Mellitus Menggunakan Fitur Regular Expression dan Non-Regular Expression Nur Wachid Adi Prasetya; Linda Perdana Wanti; Riyadi Purwanto
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3125

Abstract

Diabetes mellitus is a rapidly progressing non-communicable disease that significantly affects quality of life. Clinical information in electronic medical records, such as prescriptions and laboratory results, often appears as unstructured text and therefore requires text-mining techniques for accurate classification. This research compares the performance of the Support Vector Machine (SVM) classifier on diabetes mellitus data processed with and without feature extraction using Regular Expressions (Regex). The workflow includes data preprocessing, feature extraction, TF-IDF weighting, model training, and evaluation using accuracy, precision, recall, and F1-score. Results show that both approaches achieve high accuracy (98.8–98.9%), with the non-Regex model performing slightly better at 98.93% compared to 98.83% for the Regex-based model. These findings indicate that SVM is effective for classifying text-based clinical data, while Regex provides potential benefits but requires further optimization to ensure its suitability for various medical text contexts.
Improving Diagnostic Accuracy on Prescription Text Data Using SMOTE-Optimized SVM Linda Perdana Wanti; Nur Wachid Adi Prasetya; Riyadi Purwanto; Rahmat Mulyadi; Akmal Fauzan Ananta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7441

Abstract

Disease classification based on drug prescription data plays a crucial role in helping healthcare professionals understand patient health conditions and supporting clinical decision-making. Drug prescription data actually contains a wealth of information regarding disease indications, but is generally presented in unstructured, free-text form. Furthermore, the data distribution across disease classes is often imbalanced, with some diseases receiving less data than others. This can lead to inaccurate classification models that favor disease classes with more data. This study aims to enhance the performance of disease classification based on drug prescription data by combining text mining approaches, the Synthetic Minority Oversampling Technique (SMOTE), and the Support Vector Machine (SVM) algorithm. The research process begins with text preprocessing, which includes case folding, tokenization, stopword removal, and stemming, to clean and normalize the prescription data. Next, the text data is converted into numeric features using the Term Frequency–Inverse Document Frequency (TF-IDF) method to enable processing by machine learning algorithms. To address the class imbalance issue, the SMOTE method is applied to training data by generating synthetic data for a limited number of disease classes. A classification model was then built using the SVM algorithm, known to be effective in handling high-dimensional text data. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the application of SMOTE and parameter optimization in Support Vector Machine significantly improved classification performance, with an accuracy of 92.6%, a precision of 91.8%, a recall of 93.4%, and an F1-score of 92.6%. The increased recall value in the class of patients diagnosed with diabetes indicates that the model is able to correctly identify most diabetes cases based on medical prescription data.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Rujianto Eko Saputro; Fandy Setyo Utomo; Linda Perdana Wanti
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1065

Abstract

Cognitive Diagnosis Models (CDMs) in Computerized Adaptive Testing (CAT) are widely used to assess students’ cognitive abilities; however, existing approaches face significant limitations. The Latent Trait Model often suffers from specification errors due to its complexity, the Diagnostic Classification Model encounters difficulties in integrating hierarchical structures, and Deep Learning Models demand substantial computational resources. To address these challenges, this study introduces Salmon Salar Optimization (SSO) to enhance CDM performance and integrates the Partially Observable Markov Decision Process (POMDP) to improve dynamic question selection. The proposed adaptive testing framework comprises three components: preprocessing, CDM, and a selection algorithm. Experimental results on the ASSISTments 2009-2010 dataset demonstrate that SSO outperforms representative baselines from both deep learning: Neural CD and Latent Trait Model: MIRT approaches. Using 5-fold cross-validation, the proposed model achieved superior predictive performance with 75.51% accuracy and an AUC of 0.8191, highlighting its robustness compared to existing state-of-the-art methods. Furthermore, adaptive test simulations reveal that the SSO- and POMDP-based model delivers superior outcomes, attaining 80.3% accuracy with a reward of 8.03 for 10-question exams and 79.8% accuracy with a reward of 11.97 for 15-question exams. These findings confirm the effectiveness of the proposed model in enhancing cognitive diagnosis and adaptive testing performance.
Politeness and Indirectness: When Sexism Hides Behind Advice in Workplace Statements Annisa Romadloni; Linda Perdana Wanti; Laura Sari
Wanastra: Jurnal Bahasa dan Sastra Vol. 18 No. 1 (2026): March
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/wanastra.v18i1.12140

Abstract

Sexism in workplaces is often framed as ordinary guidance or evaluation rather than as overt hostility, which can cause sentiment and toxicity filters to miss it. A corpus analysis was conducted using the Sexist Workplace Statements dataset (1,142 statements; 627 labeled sexist). A pragmatics-informed operationalization was applied to classify sexist statements as benevolent or hostile and to label each statement’s primary speech act as advice, evaluation, insult, joke, or complaint. Benevolent sexism was estimated to constitute 73.8% of sexist statements, while hostile sexism constituted 26.2%. Benevolent sexism was concentrated in evaluation and advice, whereas hostile sexism was concentrated in insults. A sentiment-or-profanity toxicity proxy achieved high precision but low recall for sexism, capturing most hostile sexism while missing most benevolent sexism. A supervised baseline (TF–IDF plus logistic regression) performed well on the binary label but still showed false negatives dominated by benevolent evaluations. The findings were interpreted through ambivalent sexism theory, speech act theory, and politeness theory, highlighting how indirectness and face-work enable discriminatory norms to be advanced under the guise of help.  These results make explicit that sexism detection systems should incorporate pragmatics- and speech-act-aware features to reliably identify benevolent, “helpful”-framed workplace sexism that standard sentiment/toxicity signals systematically overlook.
English Learning Assistance Using Interactive Media for Children with Special Needs to Improve Growth and Development Linda Perdana Wanti; Annisa Romadloni; Oman Somantri; Laura Sari; Nur Wachid Adi Prasetya; Anne Johanna
Pengabdian: Jurnal Abdimas Vol. 1 No. 2 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/abdimas.v1i2.155

Abstract

Background. Children with special needs (ABK) are children who are in several ways different from other children in general. Among the crew members are Special Children (ALB), which consists of children who are blind, deaf, mentally retarded, quadriplegic, mentally disabled and double disabled. Some of the main things that need to be considered in the learning process for ALB are teachers, learning methods, learning approaches, infrastructure and learning support media (teaching aids). Purpose. The purpose of this community service activity is to solve the problems faced by partners in the English learning process, namely when a disorder results in disruption in daily functioning, especially in learning, the student requires special services (children with special educational needs) and requires specific learning methods in addition to appropriate and interactive learning media. Method. The solution offered to overcome this problem is the optimization of teaching methods. The recommended approach in the English language assistance activities for the Cilacap State Polytechnic PkM Team is in the form of prompts and demonstrations. Meanwhile, teaching English can use total physical response (TPR) by maximizing lip reading technique in addition to maximizing the use of flash cards to attract students' interest and focus. Results. The results obtained from this community service activity are increasing the ability of children with special needs to say a few simple words in English. The growth and development of children with special needs increase after the community service activities are completed. This is shown from the evaluation results carried out by the service team by conducting a post-test on ABK. Conclusion. To get significant results, namely increasing the growth and development of ABK, especially in the pronunciation of words in English, it is better if this activity is carried out regularly in the future.
Wood Waste Crushing Machine Training at BUMDes Banjarwaru Sejahtera Linda Perdana Wanti; Nur Akhlis Sarihidaya Laksana; Unggul Satria Jati; Roy Aries Permana Tarigan; Bayu Aji Girawan; Radhi Ariawan; Nur Wachid Adi Prasetya; Ganjar Ndaru Ikhtiagung
Pengabdian: Jurnal Abdimas Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/abdimas.v2i3.847

Abstract

Background. BUMDes Banjarwaru Sejahtera is a business owned and managed by the people of Banjarwaru Village, Cilacap Regency. Some of the businesses that have been managed by BUMDes Banjarwaru Sejahtera include renting molen machines and making handicrafts such as broom handles and woven bamboo household crafts which produce an abundance of wood waste left over from the production process. So far, this wood waste has been sold to tofu craftsmen as fuel for the tofu production process. Purpose. People can use wood waste as added value by processing wood waste into handicrafts that have high economic value so that they can improve the welfare of the people of Banjarwaru village. Through this community service activity, training will be held on the use of wood waste chopping machines so that processed wood waste can be made into handicrafts such as particle boards or other handicrafts. Method. The method used is a lecture method where the community service team who are lecturers at the Cilacap State Polytechnic provide training on the use of wood waste chopping machines to members of BUMDes Banjarwaru Sejahtera and several wood craftsmen in Banjarwaru village. Results. The results of this community service activity have had a positive impact on business development at BUMDes Banjarwaru Sejahtera. Conclusion. Through this community service activity, it can be concluded that this activity has had a positive impact on the business development of BUMDes Banjarwaru Sejahtera. Apart from this, training in the use of wood waste chopping machines can also increase the competence of wood craftsmen in Banjarwaru village so they can produce more varied crafts made from wood waste.
Co-Authors Abdul Rohman Supriyono Akmal Fauzan Ananta Alif Iftitah Andesita Prihantara Andriansyah Zakaria Anggita Dwi Fatimah Anne Johanna Annisa Romadloni Annisa Romadloni Antonius Agung Hartono Bayu Aji Girawan Bella Adinda Putri Dwi Novia Prasetyanti Eka Tripustikasari Erna Alimudin Fadillah Fadillah Fandy Setyo Hutomo Fandy Setyo Utomo Ganjar Ndaru Ikhtiagung Hety Dwi Hastuti Hijriah Fajar Muhammad Insan Ilham Aditya Pangestu Isa Bahroni, Isa Krisna Nuresa Qodri Kukuh Muhammad Kurniawan Yogi Laksono Laura Sari Laura Sari Lina Puspitasari Muhammad Abdul Muin Muhammad Nur Faiz Muhammad Ramadani Nur Akhlis Sarihidaya Laksana Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Qisthi Alhazmi Hidayaturrohman Radhi Ariawan Rahmat Mulyadi Rahmawan Bagus Trianto Ratih Hafsarah Maharrani Ratih Ratih Renanda Sinta Nurul Kholia Riyadi Purwanto Rostika Listyaningrum Roy Aries Permana Tarigan Rujianto Eko Saputro Satriawan Desmana Sofyan Setiajid Syahrur Romadhon Titin Kartiyani Unggul Satria Jati Zahrun Nafisa