Article Per Year (5 Year)
p-Index From 2020 - 2025
1.434
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi IPSSJ Jurnal Ilmu Komputer
Arya Adhyaksa Waskita
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : Jurnal Ilmu Komputer

 1 
Analisis Sentimen Pembangunan IKN (Ibu Kota Nusantara) Pada Twitter Menggunakan Metode K- Nearest Neighbor, Naive Bayes Dan Support Vector Machines Prasmono, Yossy Veiebrian Fitri; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research investigates the Nusantara Capital City (IKN) relocation, which has generated diverse opinions, including concerns over the chosen location and the swift ratification of related laws. Recently, the Indonesian government has called on the public to support IKN's development. To assess public sentiment regarding this relocation, sentiment analysis was performed on a dataset of tweets. After data cleaning, 502 tweets were analyzed, yielding 337 positive and 163 negative comments. The analysis utilized Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (K-NN) algorithms, incorporating feature selection through Particle Swarm Optimization (PSO). This study compares the performance of Naive Bayes, SVM, and K-NN without feature selection against those methods with feature selection, specifically analyzing their Area Under Curve (AUC) values to identify the most effective algorithm. The results indicate that the PSO-based SVM algorithm achieved the highest performance, with an accuracy of 97.63% and an AUC of 0.997. This research successfully identifies an optimal algorithm for classifying positive and negative comments regarding the relocation of the Nusantara Capital City, contributing valuable insights to public sentiment analysis in this context.
Analisis Kinerja Sistem Deteksi Intrusi Jaringan Internet Of Things Berbasis Metode Ensemble Eko Kristianto; Arya Adhyaksa Waskita; Thoyyibah Tanjung
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Network intrusion has rapidly evolved, posing significant risks to IT infrastructure. To address this, ensemble learning, known for its robust classification capabilities, is applied to IoT network traffic using the public RT_IOT2022 dataset. Models such as CatBoost, Extreme Gradient Boost (XGBoost), and LightGBM were developed and evaluated. The dataset was normalized using the Normalizer and MinMaxScaler functions from the scikit-learn framework. Model training was conducted with an 80:20 fixed data split for training and testing, along with 5-fold cross-validation. Testing revealed that XGBoost with MinMaxScaler and the 80:20 split achieved the highest accuracy of 99.89%. However, accuracy decreased to 94.04% when using 5-fold cross-validation. Nevertheless, XGBoost with MinMaxScaler consistently demonstrated the fastest computation time across all schemes. For instance, it required only 15 seconds for the fixed split scheme compared to 59 seconds for 5-fold cross-validation. These findings highlight the efficiency and accuracy of XGBoost when combined with MinMaxScaler under specific validation schemes.
Analis Perancangan Dan Penerapan Keamanan Jaringan Menggunakan Metode Intrusion Detection System (IDS), Intrusion Prevention System (IPS) Dan Demilitarized Zone (DMZ) Pada PT. Maha Digital Indonesia (Mahapay) Trijanitra, Evan; Arya Adhyaksa Waskita; Taswanda Taryo
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Network security systems, in recent years have become the main focus in the world of securing other important data, this is due to the high number of suspicious threats (Suspicious Threats) and attacks from the Internet. Network security involves efforts to protect data and computer systems from detrimental threats, such as cyberattacks, malware, and data theft. The existence of increasingly complex and evolving threats has increased awareness of the need for strong network security. PT. Maha Digital Indonesia (Mahapay) is a company operating in the field of EDC Field Service where it is very important that client data is kept confidential. This requires good network security to maintain the confidentiality of the data. So the aim of this research is to implement network security using the Intrusion Detection System (IDS), Intrusion Prevention System (IPS) and Demilitarized Zone (DMZ) methods as network security at PT . Maha Digital Indonesia (Mahapay). The results of this research are the formation of connections between networks in the topology along with the successful functioning of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) detecting and preventing suspicious activities carried out by attackers and the operation of rules for the DMZ area. Success in the application is tested again by carrying out several attack methods that will be analyzed such as Syn Flood Attack, Ping Of Death and Port Scanning which will be handled by the configuration that has been applied to the network and server.
Optimizing Learning Rate, Epoch, and Batch Size in Deep Learning Models for Skin Disease Classification Rahman, Taufiqur; Anggai, Sajarwo; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores the best combination of learning rate, number of epochs, and batch size for training deep learning models to classify skin diseases. The experiments involved analyzing how loss changes with learning rates on a logarithmic scale. The findings reveal that a learning rate of approximately 10-2 is most effective, with 5×10−3 offering additional stability during training. Various combinations of epochs and batch sizes were tested, ranging from 20 to 100 epochs and batch sizes between 32 and 128. The results show that using a batch size of 32 yielded the best outcomes, achieving a validation accuracy of 97.35% and the lowest validation loss of 0.1074. While a batch size of 128 was more efficient in terms of time, it resulted in slightly lower accuracy. The model performed optimally with 25 epochs and a batch size of 32, avoiding any signs of overfitting. Data preparation also played a crucial role, involving steps like image resizing, pixel normalization, and data augmentation to align with the requirements of models such as VGG-19, Inception-V4, and ResNet-152. Visualizing the dataset distribution ensured data quality and class balance, allowing the model to better recognize patterns. This study offers practical insights for effectively and efficiently training deep learning models, particularly for tasks related to skin disease classification.
Analisis Sentimen Terhadap Istana Garuda Di Ibukota Nusantara (IKN) Menggunakan Algoritma Random Forest Dan Support Vektor Machine Jihansyah, Muhamad; Agung Budi Susanto; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ABSTRACT The relocation of Indonesia's capital city (IKN) to East Kalimantan is a national strategic project that has sparked diverse public opinions, particularly regarding the construction of Garuda Palace. This study aims to analyze public sentiment toward the Garuda Palace project using Random Forest and Support Vector Machine (SVM) algorithms and to compare their performance based on accuracy, precision, recall, and F1-score. This research offers three key novelties. First, it focuses on public opinion regarding the Garuda Palace project at IKN, which is underexplored in both local and international literature. Second, the use of Inset and Senti labeling techniques introduces a novel approach to sentiment categorization. Third, the comprehensive evaluation of Random Forest and SVM performance provides new insights into their effectiveness in large-scale infrastructure sentiment analysis in Indonesia. The methodology consists of five stages: (1) Data collection through web scraping from Twitter (July-August 2024) using keywords related to "Garuda Palace" and "IKN"; (2) Data preprocessing, including tokenization, stopword removal, stemming, and TF-IDF transformation; (3) Data labeling using Inset and Senti approaches; (4) Model training with Random Forest and SVM algorithms; (5) Model evaluation using confusion matrices and performance metrics such as accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved 77% (Inset) and 89% (Senti) accuracy, excelling in detecting negative sentiment with an F1-score of 0.93 on the Senti dataset. SVM achieved 89% (Inset) and 91% (Senti) accuracy, performing better in detecting positive sentiment with a precision of 0.96 on the Senti dataset. This study provides valuable insights into public perceptions of national infrastructure projects, supports data-driven decision-making, and serves as a reference for future sentiment analysis systems
Co-Authors AGUNG BUDI SUSANTO Ahmad Musyafa Anggai, Sajarwo Eko Kristianto Jihansyah, Muhamad Kamaludin Jumaidi Muhammad Rafif Misbahuddin Pebrianto, Juri Prasmono, Yossy Veiebrian Fitri Rafi Mahmud Zain Taswanda Taryo Taufiqur Rahman, Taufiqur Thoyyibah Tanjung Trijanitra, Evan Tukiyat Tukiyat Tukiyat, Tukiyat