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Jurnal Ilmu Komputer
Published by Universitas Pamulang
ISSN : -     EISSN : 3031125X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Ilmu Komputer merupakan jurnal ilmiah dalam bidang Ilmu Komputer, Informatika, IoT, Network Security dan Digital Forensics yang diterbitkan secara konsisten oleh Program Studi Teknik Informatika S-2, Program Pascasarjana, Universitas Pamulang, Indonesia. Tujuan penerbitannya adalah untuk memberikan informasi terkini dan berkualitas kepada para pembaca yang memiliki ketertarikan terhadap perkembangan ilmu pengetahuan dan teknologi di bidang-bidang tersebut. Setiap artikel yang dimuat dalam Jurnal Ilmu Kompute merupakan hasil kegiatan penelitian, tinjauan pustaka, dan best-practice. Jurnal Ilmu Komputer terbit dua kali dalam setahun, tepatnya pada bulan Juni dan Desember. Jumlah artikel untuk setiap terbitan adalah 10 artikel.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 63 Documents
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Cyberattack Detection on IoT Devices in the Context of Large Data Volumes and Network Complexity: Cyberattack Detection on IoT Devices in the Context of Large Data Volumes and Network Complexity Zikrullah, Mochamad Fachrudin; Dr TUKIYAT, M.Si; Dr MURNI HANDAYANI, S.Si., M.Sc
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

The Internet of Things (IoT) has become an essential part of everyday life, enabling devices to communicate and work together seamlessly, boosting productivity, efficiency, and convenience across various domains such as healthcare, transportation, manufacturing, and smart homes. However, as IoT adoption grows rapidly, so do the challenges related to cybersecurity. The vast amounts of data generated by these devices and the increasing complexity of IoT networks create vulnerabilities that cybercriminals are quick to exploit. Factors like the diversity of IoT devices, differing communication protocols, and inconsistent security standards only add to the problem. Cyberattacks such as Distributed Denial of Service (DDoS), malware, and data sniffing are becoming increasingly sophisticated, threatening the security and functionality of IoT ecosystems. To combat these issues, it is crucial to develop robust and adaptive methods that can detect and mitigate these threats in real-time. This paper reviews current methods for detecting cyberattacks on IoT devices, with a focus on integrating machine learning, data analytics, and blockchain technologies. Traditional rule-based systems, while effective against known threats, struggle to keep up with the complexity and ever-evolving nature of modern cyberattacks. Machine learning techniques, especially deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have shown exceptional capabilities in analyzing large datasets to identify patterns and anomalies. Additionally, blockchain technology offers enhanced security through its decentralized and tamper-resistant nature, ensuring data integrity across IoT networks. The study explores IoT-related threats, discusses methodologies to counter them, and presents case studies to highlight the practical application of these advanced techniques. It emphasizes the need for scalable, efficient, and adaptable solutions to secure IoT ecosystems against the growing sophistication of cyber threats.
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

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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 Transaksi Pembayaran Tiket Kereta Api (KAI) Dengan Pembayaran Via Bank Menggunakan Metode K-Nearest Neighbors Dan Naïve Bayes Studi kasus PT XYZ Rizki Agustian; Murni Handayani; Abu Khalid Rivai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

In the digital era, train ticket payment patterns are increasingly complex with the increasing use of bank payment methods. PT XYZ, as a train ticket service provider, faces challenges in understanding customer behavior based on available transaction data. The main problem in this study is how to effectively group customer data based on their transaction characteristics to support service improvement and marketing strategies. This study implements two data mining classification algorithms, namely K-Nearest Neighbors (KNN) and Naïve Bayes, to analyze train ticket payment transaction patterns. Processing is carried out through the RapidMiner application, with an approach based on historical transaction data collected and processed using Microsoft Excel. The research methodology includes the stages of data collection, preprocessing, classification modeling, and model performance evaluation based on accuracy, precision, and recall metrics. The results show that the Naïve Bayes algorithm has superior performance compared to KNN, with an accuracy of 99.10%, a precision of 99.07%, and a recall of 99.14%. This indicates that Naïve Bayes is more effective in classifying customer transaction data. Companies can implement the Naïve Bayes algorithm in internal analytics systems to support data-driven decision-making, particularly in marketing strategies and customer service personalization
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

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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
Analisis Topik Penelitian Pendidikan Matematika Di Indonesia Dengan Menggunakan Metode Latent Dirichlet Allocation (LDA) junedi, Beni; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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On the research topic of Mathematics Education readers or researchers still have difficulty identifying research topics in the field of Mathematics Education. This is because there is no system or model that can be seen or used in determining research topics. Besides that, there is no automation of the research direction of Mathematics Education in Indonesia using topic modeling, so it is necessary to conduct a study or research on this. In research, the most important thing is the trend of research that is currently developing so that it can determine the novelty of the studies that have been done before. While there is no system used to determine trends and state of the art from research in the field of Mathematics Education. The aim of the research is to find out an overview of the research topics in Mathematics Education in Indonesia in 2020-2023 and to find out the implementation of modeling research topics in Mathematics Education in Indonesia using the Latent Dirichlet Allocation (LDA) method for 2020-2023. The research design consisted of literature study, data collection, data pre-processing: tokenization, case folding, stopword removal, and stemming, topic analysis with LDA, evaluation of the LDA method, and conclusions. Analysis of Topic Modeling with Latent Dirichlet Allocation using packages used from python including the Gensim and pyLDAvis packages. Based on the coherence score, the best number of topics (K) = 18, with a coherence score = 0.426 (the highest), it can be concluded that the number of topics produced is 18 topics.
Analisis Eksperimental Kinerja Transformers, VADER, dan Naive Bayes dalam Analisis Sentimen Teks Bahasa Indonesia: Studi Kasus Komentar Terkait Judi Online Sugiyo; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on detecting and classifying opinions expressed in textual data. In the digital social context, the increasing volume of public comments related to online gambling in Indonesia highlights the need to map public perception. This study aims to conduct an experimental analysis of the performance of three popular sentiment analysis approaches: VADER (Valence Aware Dictionary and sEntiment Reasoner), Naive Bayes, and Transformers-based models, specifically on Indonesian-language text. The dataset consists of public comments from social media and digital platforms containing keywords related to online gambling. The research process involves text preprocessing, data labeling, model training (for Naive Bayes and Transformers), and performance testing. Evaluation metrics include accuracy, precision, recall, and F1-score. The experimental results show that the Transformers model (using IndoBERT) achieves the highest performance in terms of accuracy and generalization ability, while VADER performs less optimally due to its limitations in understanding Indonesian linguistic context. Naive Bayes demonstrates moderate and consistent performance but lacks the capability to capture complex contextual meanings. These findings contribute to selecting appropriate sentiment analysis methods for non-English languages and support the development of more accurate public opinion detection systems in the future
Systematic Literature Review : Tren Perkembangan Model dan Algoritma Analisis Video Kerumunan-Padat Fristiyanto, Doni; Abu Khalid Rivai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
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Dense-crowd video analysis is a branch of computer vision that has various important applications in public safety, emergency management, urban planning, pedestrian traffic engineering, and crowd management at large events, such as religious activities, music concerts, and sports matches. This study presents a Systematic Literature Review (SLR) of 30 scientific publications published between 2010 and 2025. The main objective of this review is to identify the latest research trends, classification of algorithms used, application domains, and the main challenges still faced in crowd video analysis. The results of this SLR show that deep learning-based approaches, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer, still dominate various applications, especially in anomaly detection which aims to recognize suspicious behavior in dense crowds. This technology has significant potential for preventing  dangerous events such as riots, mass panic, or accidents. In addition, trends in the integration of new technologies are also found, such as the use of hybrid algorithms that combine several approaches, federated learning for distributed model training, and the use of multimodal data and drones to improve monitoring effectiveness. However, many challenges remain, such as limited representative datasets, decreased accuracy under extreme conditions, computational limitations for real-time applications, and issues of privacy and model interpretability. Therefore, the results of this SLR are expected to make a strategic contribution to the development of more sophisticated, adaptive, and relevant crowd analytics systems.
Analisis Prediksi Penerima Bantuan Bea Study Menggunakan Algoritma Id3, Naïve Bayes Dan K-Nearest Neighbor (Studi Kasus Pada Lembaga Amil Zakat Rydha) Muhamad Sibli; Taswanda Taryo; Murni Handayani
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
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The RYDHA Amil Zakat Institution has not yet implemented a data-driven predictive system to objectively determine B-Best scholarship recipients, leaving the selection process manual and prone to bias. This study aims to compare the performance of ID3, Naïve Bayes, and K-Nearest Neighbors (KNN) algorithms in classifying scholarship eligibility. Primary data were obtained from the 2024 B-Best applicants’ records, including demographic, socio-economic, academic, and supporting documents, while secondary data consisted of selection guidelines and internal reports, collected through interviews, documentation, and observation. Data analysis employed the three algorithms with evaluation using the Confusion Matrix and ROC Curve. The results show that KNN achieved the best performance with 96.3% accuracy, 0.958 AUC, 0.944 F1-score, 0.944 precision, and 0.944 recall, thus recommended as the predictive model to support a more objective and accurate scholarship selection system.
Analisis Tipe Kecerdasan Majemuk Siswa Sekolah Dasar Berbasis Catatan Perilaku Menggunakan Algoritma Naive Bayes, K-Nearest Neighbors, dan Support Vector Machine Nursalam, Asep Herman; Agung Budi Susanto; Taswanda Taryo
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study aims to identify the types of multiple intelligences of elementary school students based on Howard Gardner's theory by utilizing machine learning algorithms, namely Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data used comes from student behavior records and intelligence type questionnaires obtained from students or parents. The SEMMA method (Sample, Explore, Modify, Model, Assess) is used, including text preprocessing and TF-IDF feature extraction. The classification process is carried out using Orange Data Mining software and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The evaluation results show that the SVM algorithm provides the best performance with an accuracy of 93.30% and AUC of 0.997. Naive Bayes follows with 90.50% accuracy and 0.994 AUC, while KNN reaches 89.50% accuracy and 0.941 AUC. The study also results in a web-based application prototype that classifies students' intelligence types and provides personalized learning recommendations. This confirms the effectiveness of machine learning in supporting personalized learning and student potential development.
Rancang Bangun Sistem Informasi Penjualan Kebutuhan Komputer Berbasis Web Pada Ruko Bantenbiz Komputer Menggunakan Metode Waterfall Ramdan, Fahru
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Ruko Bantenbiz Komputer is a business engaged in the sale of computer-related needs, including hardware, accessories, and other equipment. The sales process is still conducted manually, leading to several issues such as difficulties in managing transaction data, delayed services, and limited market reach. To address these problems, a web-based sales information system was designed and developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was built using PHP Native as the programming language, Dreamweaver as the Integrated Development Environment (IDE), and MySQL as the database. With this system, the business owner can manage sales and inventory more efficiently, accelerate transaction processes, and provide easier access for customers to view product information and make online purchases. This system is expected to improve operational effectiveness and expand the market reach of Ruko Bantenbiz Komputer.

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