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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika ELKHA : Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JADECS (Journal of Art, Design, Art Education and Culture Studies) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi dan Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JE (Journal of Empowerment) Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Ramadhan Rakhmat Sani
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Perbandingan Metode Seleksi Fitur Chi-Square dan Information Gain untuk Peningkatan Interpretabilitas dan Optimasi Kinerja Model TabNet Salsabilla, Annisa Ratna; Sani, Ramadhan Rakhmat; Dewi, Ika Novita
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 3 (2025): Desember 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i3.2025.253-262

Abstract

Breast cancer is one of the most significant global health issues. Machine learning approaches offer the potential to accurately analyze clinical data and aid in early diagnosis. However, conventional machine learning models are often limited in their ability to model complex nonlinear relationships in medical data, which can reduce predictive accuracy. This study employs a deep learning architecture because of its ability to model such relationships. Specifically, the TabNet model was chosen because it is designed for tabular data and offers better interpretability. The public Wisconsin Diagnostic Breast Cancer (WDBC) dataset, which has 30 features and an imbalanced class distribution, was used in this study. Feature selection was necessary to handle the high-dimensional data, and SMOTE-ENN was used for class balancing. Two feature selection methods, Chi-Square and Information Gain, were compared to determine the most effective approach. Hyperparameter optimization was performed using Optuna and validated with stratified k-fold cross-validation to ensure optimal performance. The results of the experiment demonstrate that feature selection and optimization significantly improve performance. The base model with Chi-Square feature selection achieved an accuracy rate of 64.91%. Meanwhile, the Chi-Square model with Optuna optimization increased accuracy to 98.25%. This is 3.51% higher than the accuracy of 94.74% achieved by the optimized model without feature selection. In the final comparison, both methods demonstrated distinct advantages: Chi-Square (75% features) excelled in achieving 100% precision and more efficient computation time. Information Gain (75% features), on the other hand, was the only method to achieve 100% recall, which is crucial for minimizing false negatives. These results demonstrate that the optimal method depends on the context. Information Gain is best for maximum diagnostic sensitivity, and Chi-Square is best for performance balance and efficiency.
Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Ramadhan Rakhmat Sani; Yunita Ayu Pratiwi; Sri Winarno; Erika Devi Udayanti; Farrikh Alzami
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.47983

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.
Kriptografi Teks Berbasis Algoritma Substitusi Vigenere Cipher 8 Bit Nida Aulia Karima; Ade Nurul Aisyah; Hercio Venceslau Silla; Lekso Budi Handoko; Ramadhan Rakhmat Sani
Jurnal Masyarakat Informatika Vol 15, No 1 (2024): May 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.1.60836

Abstract

Vigenere Cipher is one of the classic cryptographic algorithms in the world of cryptography. This research focuses on the use of the Vigenere Cipher method and its implementation in securing an ASCII message text. This research uses four testing methods namely, Avalanche Effect, Character Error Rate (CER), Bit Error Rate (BER), and Entropy. The test results found that the Avalanche Effect value produced on average was at 50% and above, meaning that a good Avalanche Effect value was obtained. In addition, the resulting CER and BER are 0, meaning that no errors occurred during the encryption process. The resulting Entropy value also increases along with the length of the plaintext used and is also influenced by the use of ASCII 256 in the form of letters, numbers, and symbols.
Comparative Evaluation of Machine Learning Algorithms with Data Balancing Approach and Hyperparameter Tuning in Predicting Thyroid Disorder Recurrence Darnell Ignasius; Rhyan David Levandra; Ramadhan Rakhmat Sani; Ika Novita Dewi
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.75073

Abstract

This research evaluates and compares the performance of five machine learning algorithms (Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting) in predicting thyroid disease recurrence using patient data. The analysis was conducted on the Thyroid Disease Dataset from the UCI Machine Learning Repository. The methodology includes data preprocessing, normalization, and class balancing with the Synthetic Minority Over-sampling Technique (SMOTE). Additionally, hyperparameter tuning was conducted using GridSearchCV to optimize model performance. The results demonstrate that ensemble-based models, specifically Random Forest and Gradient Boosting, consistently outperform the other algorithms in terms of accuracy and robustness. These models achieve 95–96% accuracy across various scenarios.A key finding is that SMOTE significantly improves recall for minority classes, highlighting its value in imbalanced medical datasets.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Exploring Public Opinion on the 'Makan Bergizi Gratis' Program on X: A Comparative Analysis of IndoBERT-Large and NusaBERT-Large Models Arunia, Aurelya Prameswari; Sani, Ramadhan Rakhmat; Dewi, Ika Novita; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11757

Abstract

Program Makan Bergizi Gratis (MBG) has triggered extensive discourse on social media platform X, which serves as a primary space for public expression of opinions toward government policies. This study aims to analyze public sentiment toward the MBG program while simultaneously comparing the performance of two prominent Transformer-based models, namely IndoBERT-Large and NusaBERT-Large. This research adopts a quantitative approach employing supervised learning on 10,201 Indonesian-language posts (tweets) collected through web scraping from February 2024 to September 2025. A total of 2,000 samples were manually annotated as ground truth, achieving a high level of inter-annotator reliability (Cohen’s Kappa, κ = 0.81). The experimental results indicate that IndoBERT-Large outperforms NusaBERT-Large, achieving an accuracy of 83.00%, while NusaBERT-Large demonstrates competitive performance with an accuracy of 80.50%. Substantively, public discourse is dominated by negative sentiment, accounting for nearly 50% of the total data, reflecting public concerns regarding budgetary constraints and technical implementation issues. Positive sentiment ranges between 33% and 36%, indicating sustained and substantial public support for the program. These findings confirm the effectiveness of Transformer-based models in accurately capturing the dynamics of public opinion toward government policies using social media data.
IMPLEMENTASI METODE DESIGN THINKING DAN SYSTEM USABILITY SCALE PADA USER EXPERIENCE APLIKASI BELAJAR BAHASA INGGRIS TALKTALES MELALUI CERITA RAKYAT Muhammad Nabhan Rifa’i; Ramadhan Rakhmat Sani; Suharnawi Suharnawi; Resha Meiranadi Caturkusuma
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3742

Abstract

Indonesia faces significant challenges in improving English proficiency among its population. According to the EF Education First English Proficiency Index 2023, Indonesia ranks 79th out of 113 countries. On the other hand, the current generation begins to forget cultural elements such as folklore or myths that have been passed down from the nation's ancestors. This study aims to design an English learning application for children and teenagers using the design thinking method. The stages of design thinking that are used are empathize, define, ideate, prototyping, and testing. In the prototyping stage, low-fidelity and high-fidelity prototypes were created to visualize the application's design and functionality. Testing was conducted by using task scenarios and the System Usability Scale (SUS). The task scenario testing results revealed that the effectiveness and efficiency rate of 85.71%, indicated that most tasks could be completed successfully by users. The SUS testing results showed an average score of 86.5%, indicated that the application has a high level of usability and well-received by users. Thus, the application's interface is considered easy to use and effective in supporting the English learning process for the target users. This research provides a positive contribution to the development of educational applications using a design thinking approach.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abdussalam Abdussalam, Abdussalam Abu Salam Ade Nurul Aisyah Agung Priyo Utomo, Rino Ahmad Khotibul Umam, Ahmad Khotibul Al zami, Farrikh Alzami, Farrikh Ardytha Luthfiarta ARIYANTO, MUHAMMAD Arta Moro Sundjaja, Arta Moro Arunia, Aurelya Prameswari Asih Rohmani Asih Rohmani Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Bernadette Chayeenee Norman , Maria Budi Harjo Budi, Setyo Candra Irawan Catur Supriyanto Christy Atika Sari Darnell Ignasius Defri Kurniawan Defri Kurniawan Diana Aqmala Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erika Devi Udayanti Fahmi Amiq Farah Syadza Mufidah Farrikh Al Zami Farrikh Al Zami Fauzi Adi Rafrastara Fauzi Adi Rafrastara Florentina Esti Nilasari Florentina Esti Nilawati Guruh Fajar Shidik Hanny Haryanto Harun Al Azies Hercio Venceslau Silla Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ignasius, Darnell Ika Novita Dewi Ikhwansyah Kurniawan Indra Gamayanto Iswahyudi ISWAHYUDI ISWAHYUDI Ivan Bayu Fachreza Junta Zeniarja Karin, Tan Regina Kiki Widia Kurniawan, Defri L. Budi Handoko Lekso Budi Handoko Maszuda, Akbar Alvian Megantara, Rama Aria Melati Anggreni Sitorus Muhammad Fais Ramadhani Muhammad Nabhan Rifa’i Muhammad Naufal MY. Teguh Sulistyono Nadya Azizah Nida Aulia Karima Novita Dewi , Ika Nugraha, Purwa Esti Pangesti, Galih Mentari Paramita, Cinantya Pergiwati, Dewi Priyo Utomo, Rino Agung Pulung Nurtantio Andono Purwanto Purwanto Ramadhani, Dwi Arya Resha Meiranadi Caturkusuma Rhyan David Levandra Ricardus Anggi Pramunendar Richard Emmerig S. Sukamto, Titien Salsabilla, Annisa Ratna Sarker, Md. Kamruzzaman Sasono Wibowo Sendi Novianto Sendi Novianto Sendi Novianto Setyo Budi Setyo Budi Sirait, Tamsir Hasudungan Soares, Gilardinho Javiere Oscoraldo Pedrosa Sri Winarno Sri Winarno Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sukamto, Titien S. Sukamto, Titien Suhartini Sulistyono, Teguh Syahrizal, Muhammad Iqbal Titien Suhartini Sukamto Titien Suhartini Sukamto Utomo, Danang Wahyu Wibowo, Isro' Rizky Wildanil Ghozi Wulan Puspita Loka Yani Parti Astuti Yanuaresta, Dianna Yunita Ayu Pratiwi Yupie Kusumawati Zahro, Azzula Cerliana Zami, Farrikh Al