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All Journal Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of English Language and Education UNNES International Conference on ELTLT ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities
Sari, Laura
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Sari, Laura; Faiz, Muhammad Nur; Muhammad, Arif Wirawan
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
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.
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.
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.
Classification of DDoS Attacks based on Network Traffic Patterns Using the k-Nearest Neighbor (k-NN) Algorithm Faiz, Muhammad Nur; Maharrani, Ratih Hafsarah; Sari, Laura; Muhammad, Arif Wirawan; Supriyono, Abdul Rohman
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1834

Abstract

Many server attacks disrupt industrial or business operations. Attacks that flood bandwidth with simultaneous requests can overwhelm a system, leading to significant downtime and financial losses. Additionally, breaches that compromise sensitive data can damage a company's reputation and erode customer trust. DDoS attacks, or Distributed Denial of Service attacks, are among the most common types of server attacks. DDoS has been proven to cause server downtime, and one effective way to mitigate this attack is to detect and classify it using a machine learning approach. The K-Nearest Neighbor (KNN) algorithm, a simple yet effective classification method based on similarity measures, is known for its high accuracy. The current research builds upon two stages: the feature extraction stage and the classification stage, with the ultimate goal of improving the accuracy of DDoS identification using the CICDDoS2019 dataset. Based on this premise, the detection accuracy can be improved by enhancing these two stages. At a value of k equal to 3, this study produces an accuracy of 99.73%.
A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Riadi, Imam; Fadlil , Abdul; Faiz , Muhammad Nur; Arif Wirawan Muhammad; Purwaningrum, Santi; Sari, Laura
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Abstract

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.
Implementasi Metode Research and Development Pada Pengembangan Pembelajaran Matematika Berbasis Multimedia Perdana Wanti, Linda; Sari, Laura
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.279

Abstract

Mathematics is a lesson that is still a frightening specter for some students. There are several methods used to make mathematics fun to learn. One of them is to package material in mathematics to be more attractive and interesting, especially for children this can make them become interested in learning mathematics. The developed using research and development methods. This method begins by exploring the problem, collecting data needed, designing the product to be developed, validating the product design, testing the use of the system to be developed, revising the product, testing the product, revising the product and product design if there are errors or deficiencies and the last is mass production of product. This research aims to develop an interactive multimedia-based mathematics learning which can later be optimized to increase student interest in learning mathematics and be used to improve the quality of education.
Sistem Pakar Deteksi Dini Penyakit Preeklamsia pada Ibu Hamil Menggunakan Metode Certainty Factor Adi Prasetya, Nur Wachid; Perdana Wanti, Linda; Sari, Laura; Puspitasari, Lina
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1050

Abstract

Preeclampsia is a disease in pregnant women characterized by high blood pressure and positive urine protein. The disease has a high risk of maternal and fetal death, so there is a need for early detection of mothers at risk of preeclampsia. Early online detection of preeclampsia is the best solution during the Covid-19 pandemic by analyzing the influencing factors. The purpose of this study is to build an expert system for early detection of preeclampsia in pregnant women using the Certainty Factor method and the waterfall system development model in order to provide the possibility of pregnant women suffering from preeclampsia. Testing the accuracy of 30 medical record data for pregnant women resulted in a system accuracy level of 90%, while usability testing resulted in a user satisfaction level of 55 with the System Usability Testing (SUS) score criteria being "Poor", therefore improvements are needed on expert system in the future.
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.
Co-Authors Annisa Romadloni Arif Wirawan Muhammad Arif Wirawan Muhammad Davito Rasendriya Rizqullah Putra Fadlil , Abdul Faiz , Muhammad Nur Hastuti, Hety Dwi Hazrina, Fadhilla Imam Riadi Johanna, Anne Listyaningrum, Rostika Maharrani, Ratih Hafsarah Muhammad Nur Faiz, Muhammad Nur Nur Wachid Adi Prasetya Oman Somantri Perdana Wanti, Linda Purwaningrum, Santi Puspitasari, Lina Rahadi, Nur Wahyu Ramadhan, Adrian Putra Sukma Aji Supriyono, Abdul Rohman Wanti, Linda Perdana Windasari, Wahyuni