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Penerapan Kriptografi Algoritma Blowfish pada Pengamanan Pesan Data Teks Budi Prasetiyo; Much Aziz Muslim; Hendi Susanto
Techno.Com Vol 16, No 4 (2017): November 2017
Publisher : LPPM Universitas Dian Nuswantoro

Kriptografi dibutuhkan untuk pengamanan data dalam jaringan komunikasi. Artikel ini membahas implementasi algoritma Blowfish menggunakan Microsoft Visual Basic. Permasalahan pada tulisan ini bagaimana penerapan algoritma blowfish pada pengamanan dayta teks dan bagaiaman performa algoritma dalam mengkesekusi proses enkripsi maupun deksripsi. Metode yang digunakan yaitu menggunakan SDLC dengan membangun perangkat lunak menggunakan Visual Basic. Algoritma kriptografi yang digunakan yaitu Blowfish. Data uji menggunakan beberapa file dengan ukuran berbeda mulai dari 64Kb, 128Kb, 256Kb, 512Kb, dan 1024Kb. Berdasar hasil implementasi algoritma Blowfish menggunakan MS Visual data berhasil dienkripsi maupun didekripsi dan dapat kembali seperti semula, sehingga dapat digunakan untuk melakukan pengamanan data. Hasil pengujian waktu eksekusi menunjukkan proses enkripsi membutuhkan waktu yang lebih lama daripada proses dekripsi. Data yang diperoleh menunjukkan proses deksripsi 33% lebih cepat daripada proses enkripsi.

Application of Lean UX and System Usability Scale (SUS) Methods in Redesigning User Interface and User Experience on Adella Hospital Online Registration Website Sabrina Desy Rahmawati; Budi Prasetiyo
Journal of Advances in Information Systems and Technology Vol. 6 No. 2 (2024): October
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v6i2.2430

This research aims to evaluate user experience of a hospital online registration website. The SUS method will be used to accurately measure users’ experience. Testing using SUS is carried out by distributing a questionnaire containing 10 SUS template questions and 13 general questions. After the questionnaire is distributed, the collected data will be processed using SUS assessment standards. In addition, several respondents will be interviewed in order to design a new website prototype. The prototype will be developed using Lean UX method. Testing design improvements using Lean UX method shows improvements compared to the old design.

Optimization of Brain Tumor Segmentation on Magnetic Resonance Imaging (MRI) Using Attention Gate U-Net Fariska Ratna Fauziah; Budi Prasetiyo
Recursive Journal of Informatics Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i1.27152

Abstract. Brain tumor segmentation using Magnetic Resonance Imaging (MRI) plays a vital role in medical diagnosis, requiring high precision to support clinical decisions and reduce mortality rates. Purpose: This research aims to enhance the segmentation process by implementing an Attention Gate into the U-Net model. Methods/Study design/approach: In the segmentation stage, Attention Gate on U-Net is integrated to filter out relevant information from the extracted features, resulting in a more precise segmentation of the brain tumor to determine the location of the tumor. Result/Findings: The performance of the model is assessed by calculating several evaluation metrics such as dice coefficient and intersection-over-union (IoU) for the segmentation process. The results showed that adding Attention Gate to the U-Net achieved a dice coefficient of 87.08% and IoU of 72.70% Novelty/Originality/Value: The novelty of this study lies in the integration of the Attention Gate mechanism within the U-Net decoder stage to enhance focus on tumor regions. While U-Net is widely used in medical image segmentation, this specific attention-based enhancement significantly improves performance compared to conventional U-Net models without attention. This research contributes to advancing more accurate and efficient decision-support systems in the field of medical image analysis.

Sentiment Analysis on Twitter Social Media Regarding Covid-19 Vaccination with Naive Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) Angga Riski Dwi Saputra; Budi Prasetiyo
Recursive Journal of Informatics Vol. 2 No. 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/7h63ma50

Abstract. The Covid-19 vaccine is an important tool to stop the Covid-19 pandemic, however, there are pros and cons from the public regarding this Covid-19 vaccine. Purpose: These responses were conveyed by the public in many ways, one of which is through social media such as Twitter. Responses given by the public regarding the Covid-19 vaccination can be analyzed and categorized into responses with positive, neutral or negative sentiments. Methods: In this study, sentiment analysis was carried out regarding Covid-19 vaccination originating from Twitter using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The data used in this study is public tweet data regarding the Covid-19 vaccination with a total of 29,447 tweet data in English. Result: Sentiment analysis begins with data preprocessing on the dataset used for data normalization and data cleaning before classification. Then word vectorization was performed with TF-IDF and data classification was performed using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. From the classification results, an accuracy value of 73% was obtained for the Naïve Bayes Classifier (NBC) algorithm and 83% for the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Novelty: A direct comparison between classical models such as NBC and modern deep learning models such as BERT offers new insights into the advantages and disadvantages of both approaches in processing Twitter data. Additionally, this study proposes temporal sentiment analysis, which allows evaluating changes in public sentiment regarding vaccination over time. Another innovation is the implementation of a hybrid approach to data cleansing that combines traditional methods with the natural language processing capabilities of BERT, which more effectively addresses typical Twitter data issues such as slang and spelling errors. Finally, this research also expands sentiment classification to be multi-label, identifying more specific sentiment categories such as trust, fear, or doubt, which provides a deeper understanding of public opinion.

Hyperparameter Tuning of Long Short-Term Memory Model for Clickbait Classification in News Headlines Grace Yudha Satriawan; Budi Prasetiyo
Recursive Journal of Informatics Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/19mypm04

Abstract. The information available on the internet nowadays is diverse and moves very quickly. Information is becoming easier to obtain by the general public with the numerous online media outlets, including news portals that provide up-to-date information insights. Various news portals earn revenue from advertising using pay-per-click methods that encourage article writers to use clickbait techniques to attract visitors. However, the negative effects of clickbait include a decrease in journalism quality and the spread of hoaxes. This problem can be prevented by using text classification to classify clickbait in news titles. One method that can be used for text classification is a neural network. Artificial neural networks use algorithms that can independently adjust input coefficient weights. This makes this algorithm highly effective for modeling non-linear statistical data. The artificial neural network algorithm, especially the Long Short-Term Memory (LSTM), has been widely used in various natural language processing fields with satisfying results, including text classification. To improve the performance of the neural network model, adjustments can be made to the model's hyperparameters. Hyperparameters are parameters that cannot be obtained through data and must be defined before the training process. In this research, the Long Short-Term Memory (LSTM) model was used in clickbait classification in news titles. Sixteen neural network models were trained with different hyperparameter configurations for each model. Hyperparameter tuning was carried out using the random search algorithm. The dataset used was the CLICK-ID dataset published by William & Sari, 2020[1], with a total of 15,000 annotated data. The research results show that the developed LSTM model has a validation accuracy of 0.8030, higher than William & Sari's research, and a validation loss of 0.4876. Using this model, researchers were able to classify clickbait in news titles with fairly good accuracy. Purpose: The study was to develop and evaluate a LSTM model with hyperparameter tuning for clickbait classification on news headlines. The thesis also aims to compare the performance of simple LSTM and bidirectional LSTM for this task. Methods: This study uses CLICK-ID dataset and applies different text preprocessing techniques. The dataset later was used to build and train 16 LSTM models with different hyperparameters and evaluates them using validation accuracy and loss. This study uses random search for hyperparameter tuning. Result: The results of the study show that the best model for clickbait classification on news headlines is a bidirectional LSTM model with one layer, 64 units, 0.2 dropout rate, and 0.001 learning rate. This model achieves a validation accuracy of 0.8030 and a validation loss of 0.4876. The results also show that hyperparameter tuning using random search can improve the performance of the LSTM models by avoiding zero probabilities and finding the optimal values for the hyperparameters. Novelty: This study compares and analyzes the different preprocessing methods on text and the different configurations of the models to find the best model for clickbait classification on news headlines. The study also uses hyperparameter tuning to tune the model into the best model and finding the optimal values for the hyperparameters.

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting Salsabila Rahadatul Aisy; Budi Prasetiyo
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/zbxqcc36

Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law. Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law. Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm. Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law. Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.

Analisis Komparasi Model BERT dan Model DISTILBERT Pada Klasifikasi Struktur Judul Berita Clickbait Online Berbahasa Indonesia Rananggana Trustha Dewangga; Budi Prasetiyo
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26479

Clickbait uses sensational or misleading headlines to attract readers, which can degrade information quality in online news. This study presents a comparative evaluation of BERT and DistilBERT for detecting clickbait headline structures in the Indonesian language using the CLICK-ID dataset. The approach examines how class imbalance influences performance by training models on multiple dataset variants created through oversampling, undersampling, and data augmentation. Inputs are tokenized with model specific tokenizers and evaluated with accuracy, precision, recall, and F1-score. Confusion matrices are used to interpret error patterns across classes. Experimental results show that DistilBERT trained on an oversampled dataset achieves 94% for accuracy, precision, recall, and F1-score, while BERT on the same oversampled setting reaches 93%. Models trained on unbalanced data yield the lowest recall and F1 for the clickbait class, confirming the adverse effect of skewed distributions. Augmented and undersampled variants produce slightly lower but competitive results in the 92% to 93% range. Error analysis shows that DistilBERT reduces missed clickbait while maintaining a similar level of false positives, producing more balanced behavior across classes. These results outperform prior CLICK-ID studies and highlight the advantage of transformer architectures combined with effective class balancing for Indonesian clickbait detection.

IMPLEMENTASI MODEL DEBERTA DENGAN OPTIMASI HYPERPARAMETER BERBASIS BAYESIAN OPTIMIZATION UNTUK DETEKSI BERITA PALSU Muhammad Nafis Hafi; Budi Prasetiyo
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7098

Saat ini, media sosial memegang peran kunci sebagai sumber informasi utama bagi masyarakat modern karena menawarkan akses berita yang cepat dan praktis. Namun, kemudahan akses ini memiliki sisi gelap, di mana platform digital memfasilitasi penyebaran berita palsu dalam skala besar tanpa proses verifikasi yang memadai. Fenomena ini seringkali didorong oleh kesengajaan untuk menipu, yang menimbulkan dampak negatif serius pada stabilitas sosial dan politik. Untuk mengatasi permasalahan tersebut, pendekatan Natural Language Processing (NLP) berbasis deep learning dikembangkan sebagai solusi deteksi otomatis. Penelitian ini bertujuan mengimplementasikan arsitektur Decoding-enhanced BERT with Disentangled Attention (DeBERTa) yang dikombinasikan dengan Bayesian Optimization. Teknik optimasi ini digunakan untuk menentukan konfigurasi hyperparameter terbaik secara otomatis dan menghindari penyetelan secara manual. Kinerja model dievaluasi menggunakan metode 5-Fold Cross Validation dengan membandingkan hasil optimasi terhadap model baseline yang menggunakan hyperparameter default. Hasil eksperimen menunjukkan bahwa integrasi Bayesian Optimization berhasil meningkatkan performa model secara signifikan. Model yang diusulkan mencapai rata-rata accuracy sebesar 98,06%, mengungguli model baseline yang hanya mencatatkan accuracy 97,52%. Temuan ini membuktikan bahwa kombinasi DeBERTa dengan optimasi hyperparameter dinamis lebih efektif dan stabil dalam mendeteksi berita palsu dibandingkan pendekatan konvensional.

Sentiment Analysis of Claude Design Features on X using Support Vector Machine Azhar Dwi Nugroho; Budi Prasetiyo
SISTEMASI Vol 15, No 9 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i9.6948

The development of artificial intelligence technology has introduced various tools that support digital design and product development processes, one of which is the Claude Design feature. The emergence of this technology has generated diverse user responses that can be analyzed through social media. This study aims to analyze the sentiment of users on the X platform toward the use of the Claude Design feature using the Support Vector Machine (SVM) method. The research data were obtained by scraping 2,000 posts using the keyword “Claude Design.” The data then underwent preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and lemmatization. Sentiment labeling was performed using the Valence Aware Dictionary and sEntiment Reasoner (VADER) method. After removing neutral data, 1,438 posts remained, consisting of 1,152 positive and 286 negative sentiment posts. Subsequently, features were extracted using Term Frequency–Inverse Document Frequency (TF-IDF) and classified using the SVM algorithm. The evaluation results show that the SVM model achieved an accuracy of 82.29%, precision of 81%, recall of 82%, and an F1-score of 82%. The findings indicate that the majority of users expressed positive sentiment toward Claude Design and demonstrate that the combination of VADER, TF-IDF, and SVM can be effectively applied to sentiment analysis of social media data.

Analisis Sentimen Ulasan Pengguna Aplikasi YouCut Pada Google Play Store Menggunakan Metode Logistic Regression Classifier: Sentiment Analysis of YouCut Application User Reviews on the Google Play Store Using the Logistic Regression Classifier Method Athifa Dihyan Calysta; Budi Prasetiyo; Regina Ayumi Ulayyaa
Indonesian Journal of Mathematics and Natural Sciences Vol. 49 No. 1 (2026): Volume 49 Nomor 1 April 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jm.v49i1.27188

The rapid development of digital technology encourages people to be more active in reviewing digital services, including mobile applications. User reviews play an important role in shaping an app's reputation and success in the market. However, the high volume of reviews makes it difficult to analyze manually. This research aims to automatically analyze the sentiment of YouCut app user reviews with a machine learning approach using the Logistic Regression algorithm. The dataset in the form of 1,000 user reviews in Indonesian language is taken automatically from Google Play Store and processed through the stages of data cleaning, text preprocessing, lexicon-based sentiment labeling, feature extraction with TF-IDF, and model training and evaluation. The analysis results show that the Logistic Regression model is able to classify sentiment with an accuracy of 89%. Visualization of sentiment distribution shows the dominance of positive sentiment by 62.2%. Analysis of dominant words through wordcloud revealed that users appreciated the ease of use and video editing results, but also voiced complaints about performance and ad interruptions. The findings provide strategic insights for developers to continuously improve the features and user experience of the YouCut app.

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