Article Per Year (5 Year)
p-Index From 2021 - 2026
2.753
P-Index
This Author published in this journals
All Journal EGALITA JURIKOM (Jurnal Riset Komputer) 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 Jurnal Penelitian Ilmu Pendidikan Indonesia UNNES International Conference on ELTLT Pengabdian Jurnal Abdimas ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities Wanastra: Jurnal Bahasa dan Sastra
Sari, Laura
Unknown Affiliation
Religion Arts Humanities 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 Mathematics Mechanical Engineering Social Sciences Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
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.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Faiz, Muhammad Nur; Muhammad, Arif Wirawan; Sari, Laura
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.
Kontrol Kecepatan Berbasis PWM (Pulse Width Modulation) Untuk Mesin Pemarut Kelapa Bertenaga Surya Fadhillah Hazrina; Prima Dewi, Riyani; Widianingsih, Betti; Sari, Laura; Zulfahmi Muassar, Mifta
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.v16i2.2794

Abstract

Solar energy is a new renewable energy (EBT) that can be used as an alternative energy source for electricity generation to replace fossil fuels or supplies from the National Electricity Company (PLN). One of its uses can be applied in everyday life in household appliances, namely, coconut grater machines. Coconut grater machines used in the market still use fossil fuels to crush coconut meat, so solar energy is implemented as an alternative energy to operate the coconut grater machine. The use of solar panels in this study is highly dependent on sunlight exposure. In addition, the tilt position of the solar panel can also determine the power generated by the solar panel. The tilt position of the solar panel can be manually adjusted according to the direction of sunlight at certain times. Around midday, sunlight can be captured optimally. At that time, the accumulator/battery will quickly charge, and the coconut grater machine can be used at low or high speeds. The purpose of this study is to implement a PWM (Pulse Width Modulation) system-based control as a motor speed regulator on a coconut grater machine. PWM technology is installed to obtain optimal rotation results and has the potential to save electrical energy. The research results showed that the installed solar panels could produce an average of 4.86 watts of electrical power at 8:00 a.m. WIB and a maximum of 5 watts of electrical power at 12:00 p.m. WIB. Under no-load operating conditions, the current was 0.38 A and the motor speed was 3,724 Rpm. When the engine was tested under load, the speed was 2,926 Rpm.
THE INFLUENCE OF GENDER ON SPEAKING ANXIETY IN ENGLISH CLASSES AT POLITEKNIK NEGERI CILACAP: A MIXED-METHODS STUDY AND PEDAGOGICAL IMPLICATIONS Romadloni, Annisa; Sari, Laura; Wati, Linda Perdana
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
Eksplorasi Teknik Pre-Processing Berbasis eXtreme Gradient Boosting (XGBoost) pada Serangan DDoS Nur Faiz, Muhammad; Sari, Laura; Imam Riadi; Arif Wirawan Muhammad; Sukma Aji
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9380

Abstract

Distributed Denial of Service (DDoS) attacks represent a critical threat to modern network security, particularly within Internet of Things (IoT) environments characterized by large-scale and heterogeneous traffic patterns. The primary challenges in detecting such attacks involve class imbalance, irrelevant features, and noise within the data, all of which can degrade the performance of machine learning-based detection models. This study evaluates the impact of a pre-processing pipeline—comprising the Synthetic Minority Over-sampling Technique (SMOTE), correlation-based feature selection, and advanced feature selection methods—on the performance of the XGBoost algorithm in detecting DDoS attacks using the CIC-IoT2023 dataset. Experimental results indicate that the XGBoost model trained on RAW data achieves exceptionally high performance, with an accuracy of 0.999983, precision of 0.985531, recall of 0.961390, and an F1-score of 0.999983. However, after applying the pre-processing techniques, all metrics experienced a decline, with accuracy decreasing to 0.958899, precision to 0.865729, recall to 0.748332, and the F1-score to 0.959158. The reduction in recall suggests a higher number of undetected attacks, whereas the drop in precision indicates an increase in false alarms. Nevertheless, the F1-score remaining above 0.95 demonstrates that the model continues to perform effectively overall. These findings reveal that pre-processing does not always lead to performance improvements, especially when the raw dataset is already relatively clean and balanced. This study provides deeper insights into how SMOTE, feature selection, and noise injection influence the generalization of XGBoost on IoT traffic, and emphasizes that the effectiveness of pre-processing is highly dependent on dataset characteristics and the intended application context of intrusion detection systems.
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.
Mixed-Data K-Means Clustering with Hyperparameter-Tuned Random Forest for OSS-Based MSME Investment Profiling and Policy Targeting Sari, Laura; Maharrani, Ratih Hafsarah; Hastuti, Hety Dwi; Ramadhan, Adrian Putra; Windasari, Wahyuni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5545

Abstract

Administrative data of Micro, Small, and Medium Enterprises collected through the Online Single Submission system are highly heterogeneous, combining numerical and categorical attributes that hinder conventional investment segmentation and early-stage policy mapping. This study aims to develop a predictive clustering framework for enterprise investment profiling using mixed-type administrative data. The proposed methodology applies robust preprocessing, including RobustScaler for numerical variables and one-hot encoding with singular value decomposition for categorical features. Mixed-type similarity is computed using Gower distance, followed by a hybrid Gower–K-Means clustering approach, where the optimal number of clusters (k = 3) is determined using Silhouette, Calinski–Harabasz, and Davies–Bouldin indices. A comparative evaluation of clustering algorithms is conducted, with K-Prototypes performing best in the initial assessment and K-Means achieving superior performance after optimization. Cluster membership is subsequently predicted using a Random Forest classifier with hyperparameters optimized through randomized search. Experiments on 20,857 enterprise records identify three distinct clusters representing low-capital micro enterprises, transitional firms, and asset-intensive corporate entities. The optimized K-Means model achieves a Silhouette score of 0.97 and a Davies–Bouldin Index of 0.54. Compared with the untuned baseline, the tuned Random Forest model improves recall from 0.25 to 0.75 (200% increase) and increases the F1-score from 0.40 to 0.86 (114% improvement), while achieving 99.89% accuracy. These gains correspond to an estimated 20–30% improvement in MSME investment mapping effectiveness compared with traditional profiling approaches, providing a scalable AI-based blueprint for targeted regional economic governance.
Co-Authors Annisa Romadloni Arif Wirawan Muhammad Arif Wirawan Muhammad Davito Rasendriya Rizqullah Putra Fadhillah Hazrina 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 Faiz, Muhammad Nur Wachid Adi Prasetya Oman Somantri Perdana Wanti, Linda Prima Dewi, Riyani Purwaningrum, Santi Puspitasari, Lina Rahadi, Nur Wahyu Ramadhan, Adrian Putra Sukma Aji Supriyono, Abdul Rohman Wanti, Linda Perdana Wati, Linda Perdana Widianingsih, Betti Windasari, Wahyuni Zulfahmi Muassar, Mifta