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All Journal EGALITA Dinamisia: Jurnal Pengabdian Kepada Masyarakat Infotekmesin JISA (Jurnal Informatika dan Sains) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of English Language and Education TIERS Information Technology Journal Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Jurnal Penelitian Ilmu Pendidikan Indonesia Journal of Artificial Intelligence and Digital Business UNNES International Conference on ELTLT ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities ⁠International Journal of Asian Pacific Community Service Journal of Information Technology and its Utilization Wanastra: Jurnal Bahasa dan Sastra Pengabdian: Jurnal Abdimas
Annisa Romadloni
Politeknik Negeri Cilacap
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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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.
Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest Sari, Laura; Romadloni, Annisa; Listyaningrum, Rostika
Infotekmesin Vol 14 No 1 (2023): Infotekmesin: Januari, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i1.1751

Abstract

Cancer is the second highest cause of death in the world. In Indonesia, it is a disease with a high mortality rate. Most patients do not realize that they have lung cancer thus the treatment is sometimes too late. A prediction method with a high degree of accuracy is needed to detect lung cancer earlier. Previous research used data mining calcification methods with the Naïve Bayes algorithm to predict lung cancer. This research resulted in high recall values for the positive class (Yes class) but low for the negative class (No class). This research was made using the Random Forest algorithm which is known to have good performance. The modeling is optimized by applying the K-fold Cross Validation technique. The Random Forest algorithm produces a higher Accuracy value than the Naïve Bayes algorithm, which is 98.4%. This algorithm produces 100% Recall for the positive class, 80% for the negative class and provides a 100% correct prediction as can be seen from the AUC value of 1. Although a statistical test with a significance level of 5% shows the results of the two algorithms are not significantly different.
Metode Fuzzy Time Series Markov Chain Untuk Peramalan Curah Hujan Harian Sari, Laura; Romadloni, Annisa; Listyaningrum, Rostika; Hazrina, Fadhilla; Rahadi, Nur Wahyu
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.v15i1.2182

Abstract

Cilacap Regency has diverse topography and geographical conditions which cause this region to have rainfall that varies spatially and temporally; therefore, a forecasting method to overcome these uncertainties and fluctuations is needed. Fuzzy Time Series Markov Chain utilizes Fuzzy logic which provides flexibility in handling uncertain and unstructured data. Moreover, the addition of Markov chain elements that utilize Fuzzy logic concepts provides flexibility in handling data allowing the model to capture inter-time relationships and changes in system state that depend on previous states. Therefore, the research aims to see the suitability of the Fuzzy Time Series Markov Chain for predicting daily rainfall in Cilacap Regency. The method is suitable for predicting rainfall data for Cilacap Regency. The accuracy value in this study can be seen from the RMSE and SMAPE values ​​on the training data (in-sample), respectively, which are 58.76469 and 0.7227493. Meanwhile, the testing data (out sample) was 56.01818 and 0.7055117.
Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.
Bahasa Inggris Heti Mulyani; Ricak Agus Setiawan; Musawarman; Annisa Romadloni
Journal of Information Technology and Its Utilization Vol 5 No 2 (2022): December 2022
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.5.2.4894

Abstract

The spread of the coronavirus in Indonesia is quite fast. The spread of Covid 19 is almost evenly distributed in all provinces in Indonesia. Some areas even have a fairly high mortality rate. Therefore, it is necessary to group regions to find out which areas have the highest to lowest Covid cases so that the appropriate response process can be carried out. In addition, data visualization is also needed that provides information on COVID-19 data for each province. In this study, the data were grouped using the K-Means Clustering method. The dataset used is the Indonesian Covid-19 dataset from Kaggle. The criteria for each province's covid cluster are the number of cases and deaths. The Clustering process uses the Python programming language. From the results of this study, it can be seen that there are 3 groups of covid. The first group consists of 30 provinces with several cases below 200,000 and a number of deaths below 6000. The second group contains two provinces that have the highest number of cases, namely above 600,000, but the number of deaths is less than group 3, which is 15000. In group 3 there are 2 provinces where the number of cases is below 500,000 but the death rate is above 30,000.
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.
Penguatan Kapasitas Digital UMKM Desa Menganti melalui Pelatihan Pemanfaatan Aplikasi Online untuk Pemasaran Produk LINDA PERDANA WANTI; Rahmawan Bagus Trianto; Abdul Rohman Supriyono; Annisa Romadloni; Muhammad Abdul Muin; Antonius Agung Hartono; Hety Dwi Hastuti
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.12145

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Menganti Village, Cilacap Regency, play a vital role in driving the local economy, yet they face limitations in utilizing digital technology for product marketing. Most MSMEs still rely on conventional marketing methods, resulting in relatively limited market reach and competitiveness. This community service program aims to improve the digital capacity of MSMEs through training in the use of online applications as a means of marketing their products. The program implementation method includes needs identification, outreach, hands-on training, and mentoring in the use of digital marketing applications, such as social media and marketplace platforms. The training focused on digital account management, creating simple promotional content, and implementing easy-to-implement online marketing strategies tailored to the characteristics of village MSME products. The results of the program indicate an increase in the knowledge, skills, and confidence of MSMEs in utilizing online applications to market their products more widely. Participants are now able to manage digital media independently and understand the importance of digital marketing as part of business development. This program is expected to encourage the independence and sustainability of MSMEs in Menganti Village in facing the challenges of the digital economy and continuously increase the competitiveness of local products.
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.
THE INFLUENCE OF GENDER ON SPEAKING ANXIETY IN ENGLISH CLASSES AT POLITEKNIK NEGERI CILACAP: A MIXED-METHODS STUDY AND PEDAGOGICAL IMPLICATIONS Annisa Romadloni; Laura Sari; Linda Perdana Wati
EGALITA Vol 20, No 2 (2025): December
Publisher : Pusat Studi Gender UIN Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/egalita.v20i2.35966

Abstract

Abstract Speaking anxiety serves as a significant barrier to oral proficiency in English for Specific Purposes (ESP) courses. Yet, the influence of gender on this affective factor within Indonesian vocational education remains underexplored. This mixed-methods study investigated speaking anxiety patterns at Politeknik Negeri Cilacap. Data were collected from 90 students (45 male, 45 female) using a modified FLCAS questionnaire,written reflections, and classroom observations, complemented by in-depth interviews with twelve selected participants. Although aggregate anxiety scores did not differ significantly between genders, item-level analysis revealed distinct differences. Female students reported significantly higher anxiety when called upon without preparation (cold calls), when fearing negative evaluation, and when speaking before the whole class. At the same time, both genders experienced comparable anxiety levels during formal presentations. Qualitative data confirmed that the fear of making mistakes, sensitivity to lecturer and peer judgment, and a lack of preparation time were the primary drivers of these patterns. Based on these findings, the research advocates for the implementation of gender-responsive scaffolding. Suggested pedagogical strategies include providing structured "think time" before spontaneous questions, utilizing tiered speaking activities that progress from pair discussions to plenary reporting, and cultivating an error-friendly classroom culture. This approach aims to mitigate specific anxiety triggers for female students while building an inclusive environment that prepares all ESP learners for authentic professional communication.  Keywords: Gender; Speaking Anxiety; English for Spesific Purposes; Vocational Education.Abstrak Kecemasan berbicara merupakan hambatan signifikan bagi kemahiran lisan dalam mata kuliah English for Specific Purposes (ESP), namun pengaruh gender terhadap faktor afektif ini dalam pendidikan vokasi di Indonesia masih jarang diteliti. Studi mixed-methods ini menginvestigasi pola kecemasan berbicara di Politeknik Negeri Cilacap. Data dikumpulkan dari 90 mahasiswa (45 laki-laki, 45 perempuan) menggunakan kuesioner FLCAS yang dimodifikasi, refleksi tertulis, dan observasi kelas, dilengkapi dengan wawancara mendalam terhadap dua belas mahasiswa terpilih. Meskipun skor kecemasan agregat tidak berbeda signifikan antar-gender, analisis tingkat butir mengungkapkan perbedaan nyata. Mahasiswa perempuan melaporkan kecemasan yang jauh lebih tinggi saat dipanggil tanpa persiapan (cold calls), saat takut akan evaluasi negatif, dan ketika berbicara di depan seluruh kelas, sementara kedua gender memiliki tingkat kecemasan yang setara dalam presentasi formal. Data kualitatif mengonfirmasi bahwa ketakutan membuat kesalahan, sensitivitas terhadap penilaian dosen dan teman sebaya, serta kurangnya waktu persiapan merupakan pemicu utama pola tersebut. Berdasarkan temuan ini, penelitian merekomendasikan penerapan scaffolding responsif gender. Strategi pedagogis yang disarankan meliputi pemberian "waktu berpikir" terstruktur sebelum pertanyaan spontan, aktivitas berbicara berjenjang dari diskusi pasangan ke pelaporan pleno, serta penciptaan budaya kelas yang ramah terhadap kesalahan. Pendekatan ini bertujuan untuk memitigasi pemicu kecemasan spesifik pada mahasiswa perempuan sekaligus membangun lingkungan inklusif yang mempersiapkan seluruh pembelajar ESP untuk komunikasi profesional yang autentik.  Kata Kunci: Gender; Kecemasa Berbicara; English For Spesific Purposes; Pendidikan Vokasi
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.
Co-Authors Abdul Rohman Supriyono Andesita Prihantara Annas Setiawan Prabowo Anne Johanna Antonius Agung Hartono Cahya Vikasari, Cahya Eka Tripustikasari, Eka Fadhilla Hazrina Fadillah Fadillah Fadillah Fadillah Ganjar Ndaru Ikhtiagung Hastuti, Hety Dwi Hazrina, Fadhilla Hety Dwi Hastuti Ilham Aditya Pangestu Isa Bahroni, Isa Johanna, Anne Kukuh Muhammad Laura Sari Lina Puspitasari Linda Perdana Wanti Linda Perdana Wati Listyaningrum, Rostika Lutfi Syafirullah Muhammad Abdul Muin Muhammad Nur Faiz Musawarman, Mr. Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Pramita, Ayu Rahadi, Nur Wahyu Rahmawan Bagus Trianto Ratih Hafsarah Maharrani Ricak Agus Setiawan Rostika Listyaningrum Rostika Lityaningrum Sari, Laura Supriyono, Abdul Rohman Wanti, Linda Perdana Widianingsih, Betti