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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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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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Securing Medical Images Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for Image Steganography Pramudya, Elkaf Rahmawan; Handoko, L. Budi; Harjo, Budi; Sani, Ramadhan Rakhmat; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Sarker, Md. Kamruzzaman
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4426

Abstract

Steganography is a technique for embedding secret information into digital media, such as medical images, without significantly affecting their visual quality. The primary challenge in medical image steganography is preserving the quality of the cover image while ensuring robustness against distortions such as compression or data manipulation attacks, which may impact diagnostic accuracy. This study proposes an enhanced steganographic method based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to improve the security and robustness of medical image embedding. DWT decomposes the medical image into four frequency sub-bands (LL, LH, HL, HH), while SVD is applied to embed the secret image while maintaining essential medical features. Experimental results show that the proposed method achieves a PSNR value of up to 78 dB and an SSIM value approaching 1, indicating that the stego image quality is nearly identical to the original cover image. Compared to previous DCT-SVD and IWT-SVD-based approaches, the DWT-SVD method offers superior robustness and imperceptibility, particularly in preserving image quality in complex-textured medical images. This method contributes to enhancing data security in telemedicine and AI-based medical imaging applications by ensuring that sensitive medical data remains protected while preserving image integrity for diagnostic use.
Fuzzy tahani Implementation for Food Nutritional Status in Achieving Balanced Nutritional Dietary Dewi, Ika Novita; Priyo Utomo, Rino Agung; Sani, Ramadhan Rakhmat
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3644

Abstract

Fulfilling nutritional needs with the Recommended Dietary Allowances (RDA) shows the average value of the number of vitamins, protein and other nutrients the body needs to function properly. Each person's nutritional needs are different, so the RDA can be calculated based on different age groups and gender. In general, there are still many people who do not know the nutritional value of food and consume food without considering whether it meets the body's needs. This usually happens because calculating the RDA value is not yet familiar to do. Efforts are needed to increase awareness about the importance of a balanced diet through RDA calculations. Calculation and determination of nutritional status using the RDA number serves as a measuring tool to monitor whether a person's nutritional intake is in accordance with daily needs. This research developed a web-based application to calculate RDA numbers and group RDA numbers into nutritional status of less, enough, or more. Determination of nutritional status is carried out using the fuzzy tahani method, by displaying the results in percentage form, so that users can easily see the proportion or percentage of nutritional status obtained. This application not only calculates nutritional values, but provides a food record feature to help users manage healthy eating patterns.
Implementing BOOM in Designing a Knowledge Management System for the Information Systems Study Program at Dian Nuswantoro University Sani, Ramadhan Rakhmat; Sukamto, Titien S.; Rohmani, Asih; Maszuda, Akbar Alvian
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.3677

Abstract

Knowledge management within the Information Systems Study Program at Dian Nuswantoro University is currently still at an ineffective stage. Although the program focuses on the development and distribution of information systems, the lack of an implemented Knowledge Management System (KMS) has hindered the effective organization and utilization of knowledge within the department. By implementing a KMS, the program could more easily manage, store, and collaborate on knowledge assets. To address this issue, a web-based KMS design is proposed using the Business Object Oriented Modelling (BOOM) method. This method involves several stages: SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, discovery, construction, and final verification and validation—each aimed at optimizing knowledge management within the academic scope of the study program. The design process was supported by data collected through questionnaires distributed to both faculty members and students. The final output of this study includes a test scenario and user interface design for the Information Systems Program's Knowledge Management System website. This proposed design is expected to enhance and streamline knowledge management within the department.
PENYULUHAN GAME & PSIKOLOGI 2.0 KEPADA PESERTA DIDIK DAN GURU Budi, Setyo; Gamayanto, Indra; Novianto, Sendi; Haryanto, Hanny; Wibowo, Sasono; Sani, Ramadhan Rakhmat; Sukamto, Titien Suhartini
JE (Journal of Empowerment) Vol 4, No 1 (2023): JUNI
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v4i1.2851

Abstract

ABSTRAK Kata perubahan merupakan kata penting karena perubahan membutuhkan tingkat kreativitas tinggi serta implementasi bertahap. Dalam dunia game, perubahan terjadi sangat signifikan. Game yang ada sekarang dapat memberikan dampak besar terhadap pemainnya yaitu dampak positif dan negatif. Peserta Didik/Murid dan Guru harus mengetahui  dampak yang diakibatkan dari permainan game,  kemudian mengetahui solusi atau alternatif apa yang terbaik untuk mengatasi dampak tersebut. Agar pemahaman dasar tentang game ini dimiliki oleh Murid dan Guru SMA Negeri 3 Semarang, maka diperlukan penyuluhan terkait permaian game dan psikologi. Metode yang digunakan pada Pengabdian kepada Masyarakat (PkM) ini adalah model penyuluhan. Untuk mengetahui indikator keberhasilan pada penyuluhan ini yaitu dengan membagikan kuisioner terkait dengan materi yang disampaikan. Hasil dari penyuluhan ini adalah Murid dan Guru dapat memahami dampak yang lebih jelas mengenai game, serta solusi apa saja yang perlu dipertimbangkan untuk mendapatkan hal-hal yang positif dari permainan game. ABSTRACTThe word change is an important word because change requires a high level of creativity and gradual implementation. In the world of games, changes are very significant. Games that exist now can have a big impact on players, namely positive and negative impacts. Learners/Students and Teachers must know the impact caused by playing games, then know what solutions or alternatives are best to overcome these impacts. So that the students and teachers of SMA Negeri 3 Semarang have a basic understanding of this game, counseling is needed regarding game play and psychology. The method used in Community Service (PkM) is an extension model. To find out the indicators of success in this extension, namely by distributing questionnaires related to the material presented. The result of this counseling is that students and teachers can understand a clearer impact on games, as well as what solutions need to be considered to get positive things from playing games.
Medical Named Entity Recognition from Indonesian Health-News using BiLSTM-CRF with Static and Contextual Embeddings Ignasius, Darnell; Novita Dewi , Ika; Bernadette Chayeenee Norman , Maria; Rakhmat Sani, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11574

Abstract

Named Entity Recognition (NER) is vital for structuring medical texts by identifying entities such as diseases, symptoms, and drugs. However, research on Indonesian medical NER remain limited due to the lack of annotated corpora and linguistic resources. This scarcity often leads to difficulties in learning meaningful word representations, which are crucial for accurate entity identification. This research aims to compare the effectiveness of static and contextual embeddings in enhancing entity recognition on Indonesian biomedical text. The experimental setup involved utilizing both static (Word2Vec) and contextual (IndoBERT) embeddings in conjunction with neural architectures (BiLSTM) along with Conditional Random Fields (CRF). The BiLSTM architecture was selected for its ability to capture bidirectional dependencies in language sequences. Specifically, four models: Word2Vec-BiLSTM, Word2Vec-BiLSTM-CRF, IndoBERT-BiLSTM, and IndoBERT-BiLSTM-CRF were evaluated to assess the impact of contextual representations and structured decoding. The models were trained on a manually annotated DetikHealth corpus, where specific medical entities such as diseases, symptoms, and drugs were labeled with the BIO-tagging scheme. Performance was subsequently evaluated based on standard metrics: precision, recall, and F1-score. Results indicate that IndoBERT’s contextual embeddings significantly outperform static Word2Vec features. The IndoBERT-BiLSTM-CRF model achieved the highest performance micro-F1 0.4330, macro-F1 0.3297, with the Disease entity reaching an F1-score of 0.5882. Combining contextual embeddings with CRF-based decoding enhances semantic understanding and boundary consistency, demonstrating superior performance for Indonesian biomedical NER. Future work should explore domain-adaptive pretraining and larger biomedical corpora to further improve contextual accuracy.
Enhancing Feature-Efficient Network Intrusion Detection Using Gradient Boosting and Chi-Square Selection on NSL-KDD Soares, Gilardinho Javiere Oscoraldo Pedrosa; Fauzi Adi Rafrastara; Ramadhan Rakhmat Sani
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15650

Abstract

This study examines the growing complexity of cyber threats that increasingly challenge the effectiveness of traditional Network Intrusion Detection Systems (NIDS). Modern attacks, particularly zero-day intrusions, require detection approaches capable of handling high-dimensional network traffic data. However, existing studies rarely examine the trade-off between feature efficiency and generalization performance in boosting-based NIDS under controlled feature-reduction strategies. Moreover, the role of statistical feature selection in mitigating overfitting in classical boosting models remains underexplored. This study evaluates the performance of NIDS by combining boosting ensemble algorithms, namely AdaBoost, Gradient Boosting, and XGBoost, with filter-based feature selection methods, including Information Gain, Chi-Square, and ReliefF. The NSL-KDD dataset is used as the primary benchmark, with Min–Max normalization applied during preprocessing to ensure numerical feature consistency. Model development is conducted using Orange Data Mining, and performance is assessed through 10-fold cross-validation. Experimental results show that Gradient Boosting achieves the highest baseline accuracy among the evaluated models. Further performance improvements are obtained through feature selection, with the Chi-Square method yielding the best result at 81.2% accuracy using 19 selected features. Information Gain also enhances performance, achieving 80.8% accuracy with 13 features, while ReliefF provides comparatively lower gains. These findings demonstrate that effective feature reduction improves generalization performance, reduces computational complexity, and mitigates overfitting. Overall, the proposed combination of Gradient Boosting and statistical feature selection provides a feature-efficient, generalizable intrusion detection strategy for modern network environments.
Identifikasi Faktor Risiko Serangan Jantung di Indonesia Menggunakan Model Prediktif LightGBM Fahmi, Amiq; Muhammad Fais Ramadhani; Ramadhan Rakhmat Sani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9065

Abstract

Peningkatan prevalensi serangan jantung di Indonesia telah menjadi isu kesehatan publik yang signifikan karena penyakit ini konsisten termasuk penyebab kematian tertinggi secara nasional. Meskipun berbagai studi epidemiologis telah mengidentifikasi faktor risiko klinis maupun perilaku, pendekatan berbasis data untuk prediksi individual masih relatif terbatas, terutama pada konteks populasi Indonesia dengan karakteristik heterogen. Untuk menjawab kesenjangan tersebut, penelitian ini mengembangkan model prediktif serangan jantung menggunakan algoritma Light Gradient Boosting Machine (LightGBM) yang dikenal efisien pada data berukuran besar. Dataset terdiri dari 158.355 observasi dan 28 fitur demografis, gaya hidup, dan indikator medis. Prosedur prapemrosesan mencakup imputasi nilai hilang, pengkodean variabel kategorikal, seleksi fitur menggunakan Principal Component Analysis (PCA), serta penyeimbangan distribusi kelas melalui Synthetic Minority Over-Sampling Technique (SMOTE). Kinerja prediksi dievaluasi menggunakan metrik klasifikasi standar, di mana LightGBM mencapai akurasi 83,39% (train) dan 77,92% (test); presisi 85,67% dan 79,38%; recall 80,19% dan 75,44%; F1-score 82,84% dan 77,36%; serta AUC-ROC 91,84% dan 87,37%. Analisis komponen utama menunjukkan kontribusi varians yang tinggi pada fitur terkait pola konsumsi, penggunaan obat, stres, dan hipertensi. Hasil ini mengindikasikan bahwa LightGBM merupakan pendekatan yang menjanjikan untuk mendukung deteksi risiko serangan jantung secara lebih awal dan berpotensi meningkatkan strategi mitigasi penyakit kardiovaskular di Indonesia.
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): November 2022
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.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abu Salam Ade Nurul Aisyah Agnes Putri Istiwana Agung Priyo Utomo, Rino Ahmad Khotibul Umam, Ahmad Khotibul Al zami, Farrikh Alzami, Farrikh Annisa Ratna Salsabilla 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 Galih Mentari Pangesti 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 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 Najwa Ratu Afi Nida Aulia Karima Novita Dewi , Ika Nugraha, Purwa Esti 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 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 Winarsih, Nurul Anisa Sri Wulan Puspita Loka Yani Parti Astuti Yanuaresta, Dianna Yunita Ayu Pratiwi Yupie Kusumawati Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana Zami, Farrikh Al