Article Per Year (5 Year)
p-Index From 2021 - 2026
3.492
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Journal of Community Development Jurnal Abdi Panca Marga Journal of Multimedia Trend and Technology Jurnal Penelitian Ilmu Pendidikan Indonesia UNNES International Conference on ELTLT Pengabdian Jurnal Abdimas Damhil: Jurnal Pengabdian kepada Masyarakat Wanastra: Jurnal Bahasa dan Sastra
Wanti, Linda Perdana
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 20 Documents
Search

 1   2 
Exploring Speaking-Anxiety Triggers in Polytechnic ESP Course: An Inductive Thematic Analysis Romadloni, Annisa; Sari, Laura; Wanti, Linda Perdana
The Proceedings of English Language Teaching, Literature, and Translation (ELTLT) Vol. 14 (2025)
Publisher : The Proceedings of English Language Teaching, Literature, and Translation (ELTLT)

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

Abstract

This study investigates the specific moments and conditions that trigger speaking anxiety among engineering undergraduates in an English for Specific Purposes (ESP) course at a Politeknik Negeri Cilacap. Drawing on open‐ended responses from 101 students, the primary goal was to uncover the classroom scenarios—beyond general anxiety scales—that most disrupt learners’ oral performance. Narrative data were repeatedly reviewed following Braun and Clarke’s inductive thematic analysis procedures; provisional codes for anxiety‐provoking incidents (e.g., more comfortable in a small group, fear of being laughed at, nervous when unprepared) were generated and organized into coherent themes. It is anticipated that speaking anxiety will be found at a moderate level, with the greatest distress being associated with lexical retrieval under time pressure and unprepared, impromptu speaking tasks. Secondary triggers are expected to include concerns about grammatical accuracy and pronunciation, while social factors—such as instructor scrutiny or mixed‐gender audiences—will likely play a smaller role. These predicted patterns underscore the dual burden of technical content mastery and language production in ESP contexts. By pinpointing discipline‐specific anxiety triggers, this work aims to inform targeted pedagogical interventions—like scaffolded vocabulary drills, brief planning aids, and supportive feedback practices—to help ESP instructors foster more confident, resilient speakers.
Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease Wanti, Linda Perdana; Adi Prasetya, Nur Wachid; Somantri, Oman
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1797

Abstract

This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
Evaluasi Kinerja Model Machine Learning dalam Klasifikasi Penyakit THT: Studi Komparatif Naïve Bayes, SVM, dan Random Forest Prasetya, Nur Wachid Adi; Wanti, Linda Perdana; Purwanto, Riyadi; Bahroni, Isa; Listyaningrum, Rostika
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.
Pemanfaatan Bak Depurasi Yutuk (Undur – Undur Laut) di Desa Widarapayung Wetan Sebagai Upaya Menjaga Keamanan Pangan Kristiningsih, Ari; Wittriansyah, Khoeruddin; Purwaningrum, Santi; Prasetya, Nur Wachid Adi; Wanti, Linda Perdana; Hastuti, Hety Dwi; Ariawan, Radhi; Sarihidaya, Nur Akhlis
Abdi Panca Marga Vol 4 No 1 (2023): Jurnal Abdi Panca Marga Edisi Mei 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas Panca Marga Probolinggo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/abdipancamarga.v4i1.1309

Abstract

Yutuk, which is also known as undur - undur laut, is a typical souvenir from Widarapayung Wetan beach. The handling of yutuk before consumption by community groups processing yutuk is still in a simple way by soaking it in a bucket or tub. Depuration of yutuk or shellfish makes the dirt contained in the organs of the body come out and can reduce the heavy metal content contained therein. This Community Service activity aims to increase the education of yutuk processing groups to use the depuration method with a special yutuk tub with a recirculation system that uses natural filters such as ginger coral, zeolite and activated charcoal. The Community Service activity stage begins with interviews and field observations and then continues with a Focus Group Discussion (FGD) and then implements a yutuk depuration tub for the yutuk processing group in Widarapayung Wetan village. Through Community Service activity, the yutuk processing community groups are equipped with good and correct depuration techniques so that they can be achieved properly and the community can consume them safely and comfortably.
Optimization of Extreme Programming Methods in Plastics Waste Management Company Websites Wanti, Linda Perdana; Somantri, Oman; Romadloni, Annisa; Tripustikasari, Eka
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1018

Abstract

Plastic waste needs to be handled properly according to its type to reduce its negative impact on the earth, such as the issue of global warming which is still being widely discussed among the public. Good and correct plastic waste management has a significant long-term impact on the issue of global warming. Using the optimization of the extreme programming (XP) method to develop a plastic waste management system. With the system development method used, namely extreme programming, this system helps the community to be aware of waste and manage waste as well and wisely as possible. Extreme programming flexibility supports all changes that occur during the process of building this plastic waste management system. The output produced in the construction of this system is the management and sale of plastic waste that can be recycled according to its type. With usability testing that has been carried out, this system has been evaluated and shows a result of 88.07%, this value means that the plastic waste management system is well accepted to be used in plastic waste management.
Support Vector Machine (SVM) - Based Optimization of Leukemia Cell Image Classification Wanti, Linda Perdana; Romadloni, Annisa; Muhammad, Kukuh; Supriyono, Abdul Rohman
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 Prasetya, Nur Wachid Adi; Wanti, Linda Perdana; Purwanto, Riyadi
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.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Saputro, Rujianto Eko; Utomo, Fandy Setyo; Wanti, Linda Perdana
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.
Gendered Self-Perceptions, Inclusive Classroom Climate, and Responsible Generative-AI Use in English for Specific Purposes Romadloni, Annisa; Wanti, Linda Perdana; Sari, Laura
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.
Politeness and Indirectness: When Sexism Hides Behind Advice in Workplace Statements Romadloni, Annisa; Wanti, Linda Perdana; Sari, Laura
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.
Co-Authors Aditya Saputra, Muhammad Bintang Annisa Romadloni Awaludin, Ihza Dwi Krisbiantoro, Dwi Dwi Kumara, Dimas Maulana Eka Tripustikasari, Eka Fandy Setyo Utomo Furi, Syamaidzar Nadifa Girawan, Bayu Aji Hastuti, Hety Dwi Ikhtiagung, Ganjar Ndaru Isa Bahroni, Isa Jati, Unggul Satria Johanna, Anne Karyati, Titin Kholia, Renanda Sinta Nurul Kristiningsih, Ari Listyaningrum, Rostika Muhammad, Kukuh Nafisa, Zahrun Nur Akhlis Sarihidaya Laksana, Nur Akhlis Sarihidaya Nur Wachid Adi Prasetya Oman Somantri Prihantoro, Andesita Purwaningrum, Santi Radhi Ariawan Rahmat Hidayat Ramadani, Muhammad Riyadi Purwanto Roy Aries Permana Tarigan Rujianto Eko Saputro Sari, Laura Sarihidaya Laksana, Nur Akhlis Sarihidaya, Nur Akhlis Supriyono, Abdul Rohman Wittriansyah, Khoeruddin Wittriansyah, Khoerudin