Article Per Year (5 Year)
p-Index From 2020 - 2025
2.33
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Proceeding SENDI_U Jurnal Informatika Upgris JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Infotekmesin Moneter : Jurnal Keuangan dan Perbankan
Megantara, Rama Aria
Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search

 1   2 
SUPER ENKRIPSI TEKS KRIPTOGRAFI MENGGUNAKAN ALGORITMA HILL CIPHER DAN TRANSPOSISI KOLOM Megantara, Rama Aria; Rafrastara, Fauzi Adi
Proceeding SENDI_U 2019: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.969 KB)

Abstract

Dalam kemajuan teknologi keamanan data adalah salah satu faktor yang sangat penting. Perancangan kriptografi baru menjadi alternatif apabila metode pengamanan informasi lain sudah ada kriptanalisisnya. Hill Cipher merupakan salah satu algoritma kriptografi yang memanfaatkan matriks sebagai kunci untuk melakukan enkripsi dan Dekripsi dan aritmatika modulo. Setiap karakter pada plaintext ataupun ciphertext dikonversikan kedalam bentuk angka. Enkripsi dilakukan dengan mengalikan matriks kunci dengan matriks plaintext, sedangkan Dekripsi dilakukan dengan mengalikan invers matriks kunci dengan matriks ciphertext. Transposisi kolom yaitu teknik membagi plainteks menjadi blok-blok dengan panjang kunci (k) tertentu yang kemudian blok- blok tersebut disusun dalam bentuk baris dan kolom. Metode SuperEnkripsi dengan metode Hill Cipher dan Transposisi Kolom agar di dapatkan Cipher text yang bersifat aman. Untuk mempermudah penghitungannya, proses Super Enkripsi menggunakan PHP Native serta Javascript
A Combination of Hill CIPHER-LSB in RGB Image Encryption Megantara, Rama Aria; Rafrastara, Fauzi Adi; Mahendra, Syafrie Naufal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 3, August 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1401.076 KB) | DOI: 10.22219/kinetik.v4i3.785

Abstract

The progress of the development of digital technology today, many people communicate by sending and receiving messages. However, along with extensive technological developments, many crimes were committed. In avoiding these crimes, data security needs to be done. Form of data security in the form of cryptography and steganography. One of the cryptographic techniques is the hill cipher algorithm. Hill ciphers include classic cryptographic algorithms that are very difficult to solve. While the most popular steganography technique is Least Significant Bit (LSB). Least Significant Bit (LSB) is a spatial domain steganography technique using substitution methods. This study discusses the merging of message security with hill cipher and LSB. The message used is 24-bit color image for steganography and text with 32, 64 and 128 characters for cryptography. The measuring instruments used in this study are MSE, PSNR, Entropy and travel time (CPU time). Test results prove an increase in security without too damaging the image. This is evidenced by the results of the MSE trial which has a value far below the value 1, the PSNR is> 64 db, the entropy value ranges from 5 to 7 and the results of travel time <1 second.
Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis Alzami, Farrikh; Udayanti, Erika Devi; Prabowo, Dwi Puji; Megantara, Rama Aria
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 3, August 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i3.1066

Abstract

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.
Pengembangan Background Subtraction Menggunakan FCM Untuk Deteksi Objek Bergerak Berdasarkan Pencahayaan Yang Bervariasi Rama Aria Megantara; Ricardus Anggi Pramunendar
Techno.Com Vol 16, No 4 (2017): November 2017
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.519 KB) | DOI: 10.33633/tc.v16i4.1541

Abstract

Pendataan dari video yang direkam pada waktu malam hari memiliki tingkat kesulitan yang lebih tinggi daripada waktu pagi atau siang hari. Perubahan pencahayaan yang dihasilkan dapat mempengaruhi kualitas gambar dari rekaman video yang dihasilkan. Sehingga pengaruh pencahayaan pada saat malam hari menghasilkan kualitas rekaman video yang sangat rendah, hal ini disebabkan karena pencahayaan pada malam hari sering mengalami perubahan secara drastis. Beberapa metode yang sering digunakan dalam menyelesaikan masalah pelacakan objek bergerak antara lain background subtraction dan algoritma OTSU. Dalam menentukan threshold, algoritma OTSU tidak dapat mendeteksi gambar secara optimal saat berhubungan dengan gambar lain dilevel abu-abu. Dengan mengusulkan algoritma adaptive threshold yang didapatkan dari algoritma FCM diharapkan dapat meningkatkan akurasi untuk mendeteksi objek bergerak pada pencahayaan yang bervarisi. Sehingga dapat dilakukan penelitian ke depan untuk analisis cerdas dalam melacak pola dan deteksi perilaku anomali oleh kendaraan di jalan
PREDIKSI SENTIMEN MASYARAKAT TERHADAP PENGGUNAAN VAKSIN COVID 19 MENGGUNAKAN RNN dwi puji prabowo; Ricardus anggi pramunendar; Rama Aria Megantara
Jurnal Informatika Upgris Vol 8, No 1: Juni 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i1.11599

Abstract

Memahami sentimen dari opini publik terkait vaksin COVID-19 merupakan tantangan untuk meningkatkan penerimaan vaksin di masyarakat. Analisis sentimen telah memberikan banyak manfaat termasuk di bidang kesehatan. Analisis Sentimen dapat membantu memberikan gambaran yang dirasakan dan dipikirkan oleh para penerima vaksin. RNN merupakan salah satu metode deep learning yang sering diterapkan untuk penelitian analisis sentimen. RNN dengan arsitekur LSTM telah terbukti unggul dibandingkan metode deep learning lainnya dalam menyelesaikan tugas analisis sentimen. Penelitian ini mengusulkan model RNN-LSTM yang menerapkan arsitektur Bidirectional Layer (Bi-LSTM) agar penyerapan informasi kontekstual data lebih optimal karena data input diproses secara forward dan backward. Serta menambahkan mekanisme variational dropout pada layer LSTM untuk mendapatkan model yang optimal dan terhindar dari overfitting. Namun, keberhasilan dan keoptimalan model deep learning sangat bergantung pada ukuran dataset, jenis tugas dan penentuan parameternya. Dalam penelitian ini eksperimen terhadap nilai parameter arsitektur model dilakukan untuk mendapatkan model yang optimal dalam melakukan analisis sentimen opini publik terkait Vaksin COVID-19. Sehingga parameter terbaik didapatkan untuk model Bi-LSTM ini yaitu seperti berikut: maxlen =50, embedding size= 300, recurrent unit = 50, variational dropout = 0.25, optimizer Nadam, dan epoch = 100. Hasil evaluasi menunjukkan model BI-LSTM ini mampu melakukan analisis sentimen terhadap opini publik terkait vaksin COVID-19 ke dalam tiga kelas sentimen (positif, netral dan negatif) dengan baik dan mendapatkan akurasi sebesar 89.15% dengan rata-rata presisi 88%, recall 89% dan F1-score 88.43%
Implementation Of ETL E-Commerce For Customer Clustering Using RFM And K-Means Clustering Farrikh Alzami; Fikri Diva Sambasri; Rifqi Mulya Kiswanto; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p05

Abstract

E-commerce is the activity of selling and buying goods through an online system or online. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One of the things that need to be considered in this business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers so that they can maintain good relations with customers and can increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature. There are also several ETL stages of research that must be carried out, namely taking data from the open public data site (Kaggle), which consist of more than 9 tables (extract), then merging the data to select some data that needs to be used (transform and load), understanding data by displaying it in graphic form, conducting data selection to select features / attributes. which is in accordance with the proposed method, performs data preprocessing, and creates a model to get the cluster. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
DEVELOPMENT OF TIME-SERIES-BASED MLOPS ARCHITECTURE FOR PREDICTING SALES QUANTITY IN MICRO, SMALL, AND MEDIUM ENTERPRISES (MSMES) Lesmarna, Salsabila Putri; Alzami, Farrikh; Rizqa, Ifan; Salam, Abu; Aqmala, Diana; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 2 April (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.2.64-69

Abstract

Micro, Small, and Medium Enterprises (MSMEs) constitute a significant portion of the economy in many developing countries, playing a vital role in employment generation and economic growth. Sales performance is a critical factor for MSMEs, influenced by various internal and external factors. Time-series analysis offers a valuable tool to predict sales quantities by analyzing historical data and identifying patterns and trends. In this context, the SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) model emerges as a suitable method to forecast future sales, leveraging both historical data and external variables. This research explores the synergy between time-series analysis, specifically SARIMAX modeling, and MLOps (Machine Learning Operations). Finally, this research aims to provide a framework for the practical application of MLOps to enhance sales forecasting and decision-making processes within MSMEs, fostering their growth and sustainability in a competitive market landscape.
Peningkatan Deteksi Posisi Wajah Manusia dengan Metode Normal PDF berbasis Algoritma Viola-Jones Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Alzami, Farrikh; Prabowo, Dwi Puji; Pergiwati, Dewi; Sinaga, Daurat
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 1 (2024): JURNAL SIMETRIS VOLUME 15 NO 1 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i1.10617

Abstract

Deteksi kulit manusia dalam pengolahan citra memiliki peran penting dalam aplikasi seperti analisis gerakan, pencarian citra berbasis konten, interaksi manusia komputer, dan analisis pelacakan gerakan manusia. Meskipun banyak penelitian telah dilakukan, masih ada kendala dalam menghadapi variasi warna kulit manusia yang kompleks. Dalam penelitian ini, diusulkan peningkatan kinerja deteksi kulit manusia dengan memanfaatkan algoritma deteksi wajah Viola-Jones untuk menentukan posisi wajah dalam citra. Selain itu, diterapkan juga teknik pemisahan region kasar dan halus pada wajah guna meningkatkan hasil deteksi kulit manusia. Penggunaan Normal PDF digunakan untuk mencari probabilitas piksel kulit dalam citra. Metode yang diusulkan berhasil mencapai tingkat akurasi tinggi, di mana sebagian besar citra uji memiliki akurasi di atas 90%. Meskipun terdapat beberapa citra yang memiliki akurasi lebih rendah dibandingkan metode sebelumnya, secara keseluruhan metode yang diusulkan mampu meningkatkan kinerja deteksi kulit manusia. Oleh karena itu, penelitian ini memberikan kontribusi berharga dalam pengembangan metode deteksi kulit manusia yang lebih baik.
Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation Khoirunnisa, Emila; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Naufal, Muhammad; Al-Azies, Harun; Winarno, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14308

Abstract

Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizontal flipping, rotation, and zoom transformations to address dataset limitations. Our methodology incorporates transfer learning through ImageNet pre-trained weights and custom layer modifications to optimize the MobileNetV2 architecture for batik-specific features. The model achieves 100% accuracy on validation data, with precision, recall, and F1-scores consistently above 0.98 across all classes. The confusion matrix analysis reveals minimal misclassification between similar motif patterns, particularly in the Batik Blekok Warak and Batik Kembang Sepatu classes. This research contributes to cultural heritage preservation by providing an efficient, resource-conscious solution for automated batik pattern recognition, potentially supporting educational and commercial applications in the batik industry.
Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks Prabowo, Dwi Puji; Rohman, Muhammad Syaifur; Megantara, Rama Aria; Pergiwati, Dewi; Saraswati, Galuh Wilujeng; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar; Andono, Pulung Nurtantio
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2726

Abstract

Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential due to the unique characteristics of these images. The methodology includes testing different augmentation methods, epoch parameters, and CNN parameters on a coral reef image dataset. Five optimization algorithms (AIWPSO, GA, GWO, PSO, and FOX) are compared. The highest accuracy, 95.64%, is achieved at the 10th epoch. AIWPSO and GA show the highest average accuracies, 93.44%, and 93.50%, respectively, with no significant performance differences among the algorithms. Statistical analysis using the Wilcoxon test indicates a significant difference between training and validation accuracy (p-value = 0.0020). These findings underscore the importance of selecting augmentation methods that align with the characteristics of each optimization algorithm to enhance classification performance. The results provide valuable insights into improving the quality and diversity of input data for classification algorithms in underwater image analysis. They highlight the necessity of matching augmentation methods to specific optimization algorithms to boost accuracy and effectiveness significantly. Future research should explore additional augmentation methods and optimization algorithms further to enhance the robustness and accuracy of underwater image classification.
Co-Authors Abu Salam Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Akrom, Ahmad Al-Azies, Harun Alzami, Farrikh Asih Rohmani, Asih Budi, Setyo Chaerul Umam Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Erika Devi Udayanti Fahmi Amiq Fauzi Adi Rafrastara Fikri Diva Sambasri Firman Wahyudi, Firman Guruh Fajar Shidik Harun Al Azies Ifan Rizqa Ika Novita Dewi ISWAHYUDI ISWAHYUDI Khoirunnisa, Emila L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahendra, Syafrie Naufal Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muslih Muslih Nurhindarto, Aris Pergiwati, Dewi Pulung Nurtantio Andono Puri Sulistiyawati Puri Sulistiyawati Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Ritzkal, Ritzkal Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Sri Winarno Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana