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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 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 Information Technology and Its Utilization Journal of English Language and Education TIERS Information Technology Journal Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Journal of Artificial Intelligence and Digital Business UNNES International Conference on ELTLT Pengabdian Jurnal Abdimas ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities Journal of Information Technology and its Utilization
Annisa Romadloni
Politeknik Negeri Cilacap
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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An Analysis of Translation Challenges in ESP Programs: A Rubric-Based Evaluation of Polytechnic Students’ Performance Romadloni, Annisa; Sari, Laura
⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities Vol. 2 No. 1 (2025)
Publisher : Universitas Kristen Cipta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71131/k28yv916

Abstract

This study investigates the translation challenges faced by students enrolled in English for Specific Purposes (ESP) programs at polytechnic institutions. Recognizing that translation tasks demand both linguistic and technical competencies, this research aims to assess students’ translation performance across four key criteria: accuracy, grammar, fluency, and adherence to meaning. Employing a mixed-methods approach, fifteen students were assigned to translate technical and narrative texts commonly found in ESP curricula. Their translations were evaluated using a rubric-based system to identify patterns in performance and recurring issues. The results show that while most students demonstrated high levels of accuracy and adherence to meaning, several faced difficulties in grammar and fluency, particularly when translating culturally rich or narrative content. These findings highlight the importance of integrating targeted translation training into ESP courses, with a focus on enhancing grammatical precision and natural language flow. The study contributes to a better understanding of how polytechnic students translate specialized texts and offers practical recommendations for improving translation instruction within ESP frameworks.
Implementasi Media Pembelajaran Interaktif Pembuatan Pupuk Bokashi Berbasis Android Syafirullah, Lutfi; Pramita, Ayu; Supriyono, Abdul Rohman; Romadloni, Annisa; Hastuti, Hety Dwi; Fadillah, Fadillah
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.478

Abstract

Kabupaten Cilacap, Jawa Tengah, merupakan wilayah dengan dominasi sektor pertanian dan peternakan. Dusun Bokol Wetan, Desa Banjarwaru, memiliki sekitar 170 Kepala Keluarga yang mayoritas berprofesi sebagai peternak kambing. Limbah ternak seperti kotoran kambing dan sekam padi tersedia melimpah dan berpotensi diolah menjadi pupuk organik. Proses pengomposan dapat dipercepat dengan bioaktivator EM4, yang juga meningkatkan kualitas pupuk. Penelitian ini bertujuan untuk merancang media pembelajaran interaktif yang dapat dioperasikan melalui perangkat Android untuk meningkatkan pengetahuan peternak dalam memanfaatkan limbah organik menjadi pupuk bokashi menggunakan EM4. Pendekatan yang diterapkan dalam penelitian ini adalah Multimedia Development Life Cycle (MDLC), yang meliputi tahapan analisis, desain, pengembangan, pengujian, implementasi, dan evaluasi. Media pembelajaran yang dikembangkan menyajikan informasi secara sistematis dan mudah diakses melalui perangkat mobile. Temuan penelitian mengindikasikan bahwa aplikasi yang dikembangkan mampu secara efektif meningkatkan tingkat pemahaman peternak kambing terkait pembuatan pupuk organik. Uji kegunaan menghasilkan skor sebesar 89,38%, yang menunjukkan tingkat kepuasan pengguna sangat tinggi. Dengan demikian, media ini berkontribusi dalam mendukung pertanian berkelanjutan melalui edukasi digital berbasis potensi lokal.
Gender and Communication: Analyzing Tweet Length, Sentiment, and Lexical Patterns on X (Twitter) Romadloni, Annisa; Sari, Laura
Journal of English Language and Education Vol 10, No 4 (2025)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jele.v10i4.883

Abstract

This study explores gendered communication patterns on X by examining tweet length, sentiment expression, and lexical choices in 20.050 tweets across 26 variables. Through sentiment analysis using the Bing Lexicon and word frequency analysis, the research investigates how male and female users differ in their digital communication styles. The study also incorporates non-parametric statistical tests, such as the Mann-Whitney U and Wilcoxon rank sum tests, to assess significant differences in tweet length and sentiment scores between genders. Results show that women tend to write shorter, more positive tweets, often reflecting a more personal and relational communication style. In contrast, men’s tweets are generally longer, incorporating more action-oriented language and a broader range of topics. While sentiment analysis revealed a trend of more positive tweets from women, the lack of statistical significance in sentiment differences highlights the complex nature of gendered expression in digital spaces. This research contributes to the understanding of gendered communication on social media and suggests the need for future studies to examine the intersectionality of gender with other social factors.
Penerapan Literasi Digital dan Pemasaran Produk Berbasis Teknologi di Desa Banjarwaru Vikasari, Cahya; Prabowo, Annas Setiawan; Widianingsih, Betti; Bahroni, Isa; Romadloni, Annisa
Madani : Indonesian Journal of Civil Society Vol. 7 No. 2 (2025): Madani : Agustus 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v7i2.2396

Abstract

Banjarwaru Village faces marketing challenges for bamboo weaving products due to the limited local market and lack of coordination. The proposed solution is the establishment of a village coordination unit for business and marketing integration, as well as digital literacy training for artisans. The methods include market needs analysis, selection of application managers, coordination of product photography, and intensive training on digital marketing and e-commerce. As a result, artisans can optimize digital technology, expand market reach, and enhance product competitiveness. This program is capable of increasing artisans' knowledge and skills, as well as supporting the economic growth of Banjarwaru Village through online marketing and e-commerce
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.
Co-Authors Abdul Rohman Supriyono Andesita Prihantara Annas Setiawan Prabowo Cahya Vikasari, Cahya Eka Tripustikasari 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 Listyaningrum, Rostika Lutfi Syafirullah Muhammad Nur Faiz Musawarman, Mr. Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Pramita, Ayu Rahadi, Nur Wahyu Ratih Hafsarah Maharrani Ricak Agus Setiawan Rostika Listyaningrum Rostika Lityaningrum Sari, Laura Supriyono, Abdul Rohman Wanti, Linda Perdana Widianingsih, Betti