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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 19 Documents
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Search results for , issue "Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)" : 19 Documents clear
Analisis Sentimen Pengguna Twitter Terhadap Universitas Pamulang Periode Penerimaan Mahasiswa Gelombang I Tahun Ajaran 2024/2025 Rohmani, Muhammad Faqih; Makhsun
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

The development of information technology has had a significant impact on various aspects of life, including education. One of the universities that has gained public attention is Universitas Pamulang. As one of the largest private higher education institutions in Indonesia, Universitas Pamulang needs to continuously improve. One of the key references for these improvements is public opinion. To understand public opinion regarding Universitas Pamulang, an analysis was conducted on the social media platform Twitter. Therefore, this study examines public sentiment toward Universitas Pamulang using Twitter data and the Naïve Bayes method. The Naïve Bayes method was chosen due to its advantages in text classification, particularly in sentiment analysis. The research data was collected from Twitter during the first wave of new student admissions for the 2024/2025 academic year. The analysis process involved identifying the dominant sentiment (positive, negative, or neutral) in public opinion, exploring the institution's strengths and weaknesses, and providing recommendations for improving the quality of academic services, administration, and the reputation of Universitas Pamulang. The results of this study indicate that the Naïve Bayes algorithm can be effectively used for sentiment analysis, achieving a high level of accuracy. This research is expected to contribute academically to sentiment analysis studies in the higher education sector in Indonesia.
Analisis Resiko Stunting Di Kota Tangerang Menggunakan Metode Regresi Linier dan Support Vector Machine Muhamad Farid Hasan Khadafi; Achmad Hindasyah; Tukiyat
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Stunting remains a significant public health issue in Indonesia, particularly in Tangerang City, affecting the physical and cognitive development of children. This problem requires serious attention due to its long-term impacts on children's quality of life and their potential in the future.This study aims to analyze the risk factors contributing to the occurrence of stunting in Tangerang City using Linear Regression and Support Vector Machine (SVM) methods. The research question focuses on identifying and predicting the main risk factors influencing the prevalence of stunting. The research method employs Linear Regression Algorithm and Support Vector Machine Algorithm. The study population consists of children under five years old registered at community health centers in Tangerang City. Data samples were collected from 5,376 children, with 80% (4,300 children) used for training and 20% (1,076 children) for model testing. Several socio-economic and health variables were considered as potential risk factors, including household income, maternal education level, access to clean water and sanitation, dietary diversity, and the presence of antenatal care. Data analysis revealed performance differences between the two models used. The SVM model achieved a significantly higher accuracy of 89% with a standard error of 0.4, demonstrating strong predictive capability. In contrast, the Linear Regression model yielded a lower accuracy of 74% with a standard error of 1.5. This difference highlights the potential advantages of SVM in capturing complex and non-linear relationships within the dataset. These findings can inform targeted interventions and policy recommendations to address the causes of stunting in Tangerang City. Further research could explore a broader range of risk factors.
Evaluasi Efektivitas Tata Kelola Teknologi Informasi Di Rumah Sakit Umum Daerah Provinsi Nusa Tenggara Barat Dengan Menggunakan Kerangka Kerja Cobit 2019 Muh. Yusril Hidayat; Agung Budi Susanto
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Hospitals have a strategic responsibility to improve the quality of public health services. However, information technology (IT) management in hospitals often faces challenges such as lack of long-term planning, which causes information to be inefficient and ineffective. This study aims to evaluate the effectiveness of information technology governance at the West Nusa Tenggara Provincial Hospital using the COBIT 2019 framework. The focus of the study includes risk management (APO 12), change management (BAI 06), and security service management (DSS 05). The method used is a case study with a qualitative approach, involving interviews and questionnaires for data collection. The evaluation results show that the current average capability level is 2.5 with a target of 3. Key findings include the need for improvement in risk management and security services. Recommendations for improvement include the development of new risk policies, staff training, and adoption of the latest security technology. Implementation of COBIT 2019-based suggestions is expected to improve the quality of services and performance of information technology at the NTB Provincial Hospital.
Sentimen Analisis Kesehatan Mental Anxiety dengan Metode Decision Tree Menggunakan Software Orange Eva Fauziah; Makhsun
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Mental health, particularly anxiety disorders, has become a global concern due to the rising prevalence of mental health issues worldwide. Anxiety significantly affects individuals' quality of life and productivity, making it essential to accurately analyze and detect its symptoms. This study aims to apply the decision tree method for sentiment analysis of anxiety in texts collected from various sources such as mental health forums and social media. The decision tree method was chosen for its simplicity and effectiveness in classifying data based on identified patterns. Orange software was utilized to build the classification model due to its user-friendly interface and visualization capabilities. The results indicate that the decision tree model was able to effectively identify anxiety patterns in the texts, contributing to a better understanding of sentiment analysis in the mental health context. This study also introduces a more accessible approach for practitioners and researchers in this field.
Klasifikasi Berita Bahasa Indonesia Dengan Menggunakan Metode K-Nearest Neighbor Dan Naive Bayes Komariah Kukum Manieh Nuryasin; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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In the era of rapid development of information technology, the need for a news classification system is crucial to manage the increasing volume of information. This study aims to develop a news classification system in Indonesian into five main categories: Politics, Economy, Health, Security, and Poverty. The methods used include the K-Nearest Neighbor (KNN) algorithm and Naïve Bayes. The dataset consists of 2,000 news items obtained from Kaggle, with preprocessing stages including cleaning, tokenizing, normalization, and TF-IDF weighting. The evaluation was carried out through three data sharing scenarios: 70%-30%, 80%-20%, and 90%-10%. The results showed that the KNN algorithm achieved the highest accuracy of 89% in the 80%-20% and 90%-10% scenarios, while Naïve Bayes produced the best accuracy of 78.66% in the 70%-30% scenario. KNN proved to be more reliable for data with balanced category distribution, while Naïve Bayes required further adjustment, especially for underrepresented data categories. This research provides significant contributions to the development of an automatic news classification system, which can be implemented to improve user experience in accessing information.
KLASIFIKASI PHISHING URL PADA WEBSITE BERBASIS METODE ENSEMBLE Bahrul Ulum; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study analyzes the performance of ensemble learning algorithms in detecting phishing URLs using the PhiUSIIL Phishing URL dataset. The three algorithms compared are CatBoost, XGBoost, and LightGBM. The research stages include data preprocessing, data division into an 80:20 train-test split, and performance evaluation based on accuracy, precision, recall, and F1-score metrics. The results show that XGBoost has the best performance with an accuracy of 97.54% and an ROC AUC of 93.05%, followed by CatBoost with an accuracy of 97.46% and an ROC AUC of 92.94%. LightGBM, although it has lower performance, still shows good results with an accuracy of 96.99% and an ROC AUC of 91.85%. The data cleaning process successfully improves efficiency by eliminating irrelevant attribute analysis. This study confirms that ensemble algorithms can be implemented for the development of more effective and accurate phishing detection systems. XGBoost is recommended as the primary algorithm in detecting phishing threats in cybersecurity applications, thanks to its ability to handle large and complex data.
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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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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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

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