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Jurnal Informatika Universitas Pamulang
Published by Universitas Pamulang
ISSN : 25411004     EISSN : 26224615     DOI : https://doi.org/10.32493
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Universitas Pamulang is a periodical scientific journal that contains research results in the field of computer science from all aspects of theory, practice and application. Papers can be in the form of technical papers or surveys of recent developments research (state-of-the-art). Topics cover the following areas (but are not limited to): Artificial Intelligence Big Data Business Intelligence Data mining Decision Support Systems Intelligent Systems Machine Learning Network and Computer Security Optimization Pattern Recognition Soft Computing Software Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG" : 7 Documents clear
Implementasi Teks Mining Pada Website Kemenkes Dengan Metode LDA Menggunakan Algoritma K-Means Ari Setiawan; Deden Wahiddin; Cici Emilia Sukmawati
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.38971

Abstract

This research aims to improving the accessibility and management of health information on the Ministry of Health (Kemenkes) website. Before this research was conducted, content on the Ministry of Health's website was scattered without a clear structure, making it difficult for users to find the health information they needed quickly and efficiently. This results in a decrease in the quality of the user experience and a potential decrease in trust in official health information sources. With the aim of making it easier for users to find relevant information, this research uses the K-Means algorithm to group website content based on themes. Through the text mining method, five main clusters were identified, covering topics such as emergency health, certain diseases, and innovations in handling COVID-19. The results show an increase in navigation efficiency with clustering accuracy reaching 72%. The conclusion of this research is that this grouping succeeded in improving the structure and quality of information on the Ministry of Health's website, supporting data-based decision making, and improving public health services.
A Hybrid Model for Human DNA Sequence Classification Using Convolutional Neural Networks and Random Forests Gregorius Airlangga
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.39355

Abstract

Human DNA sequence classification is a fundamental task in genomics, essential for understanding genetic variations and its implications in disease susceptibility, personalized medicine, and evolutionary biology. This study proposes a novel hybrid model combining Convolutional Neural Networks (CNN) for feature extraction and Random Forest classifiers for final classification. The model was evaluated on a dataset of human DNA sequences, with achieving an accuracy of 75.34%. The results showed that performance metrics, including precision, recall, and F1-scores across multiple classes, showed significant improvements over traditional models. The CNN component effectively captures local dependencies and patterns within the sequences, while the Random Forest classifier handles complex decision boundaries, resulting in enhanced classification accuracy. Comparative analysis demonstrated the superiority of our hybrid approach, with the CNN-LSTM model achieving only 59.47% accuracy, and other RNN-based models like CNN-GRU and CNN-BiLSTM performing similarly lower. These results suggest that hybrid models can leverage the strengths of both deep learning and traditional machine learning techniques an offering a more effective tool for DNA sequence classification. The future work will optimize model architecture and explore larger, thus more diverse datasets to validate our approach's generalizability and robustness.
Implementasi Metode Naive Bayes untuk Klasifikasi Kondisi Gizi Balita Febriansyah Febriansyah
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.39676

Abstract

The determination of a toddler's nutritional status involves calculating weight and height based on age. Naïve Bayes is a machine learning algorithm for classification problems in data mining that utilizes probability mathematics (also known as Bayes' theorem) to distinguish between different classes. This system is designed to facilitate the nutrition staff at the Pajar Bulan Village Health Center in more accurately storing data and automatically determining the nutritional status of toddlers. The system is developed using the Rapid Application Development (RAD) method, which comprises three phases: requirements planning, design workshop, and implementation. The classification system for toddler nutritional status using the Naive Bayes algorithm aims to provide more accurate information to address malnutrition in toddlers. The data processing with the Naive Bayes algorithm results in the development of a system for classifying the nutritional status of toddlers at the Pajar Bulan Village Health Center.
Comparative Analysis of Customer Satisfaction Using The Bandwidth Method Between Coaxial Cables and Fiber Optic Cables PT. Tbk Linknet Hasbi Yallah; Akhmad Fathir; Muhammad Darwis; Retno Hendrowati
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.40010

Abstract

This research aims to analyze the comparison of customer satisfaction between the use of coaxial cables and fiber optic cables in PT.LinkNet using the Bandwidth method. The Bandwidth method was used to evaluate network performance and the quality of service received by customers. Data was collected through customer satisfaction surveys covering aspects of service reliability, speed, stability and price. The analysis results indicate a notable difference in customer satisfaction levels between the two types of cable. Fiber optic cables received higher satisfaction ratings compared to coaxial cables.The results shows that the average satisfaction score for fiber optic cables was 4.7 out of 5, whereas coaxial cables received an average score of 3.9 out of 5. This difference is significant since evidenced by a p-value of less than 0.01, confirming that the higher satisfaction with fiber optic cables is not an outcome of random variation. These findings can provide input for PT Link Net in improving service quality and choosing the right technology to meet customer needs.
Application of the K-Nearest Neighbor (KNN) Algorithm for Stunting Diagnosis in Infants Aged 1-12 Months Moh abdul kholik; Cucut Hariz Pratomo; Sapriani Gustina
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.40983

Abstract

Stunting in toddlers must be addressed immediately because it has a negative impact on their growth and development. Stunting is a disorder where toddlers experience chronic malnutrition, thus their physical growth and height do not match their age. According to the Indonesian Nutritional Status Survey (SSGI), stunting is more common among toddlers from aged 0 to 1 year than overall. Stunting can have short-term and long-term impacts. This research examines data from the Temanggung District Health Service on 3,999 toddlers aged 0 to 12 months between 2019 and 2022.  Many studies have exclusively looked at stunting in children aged one to five years, especially research on stunting using the KNN method, even though stunting can actually be recognized from an early age. Therefore, researchers are more specific in using the KNN method for cases of babies 1 to 12 months so as to differentiate it from previous researchers. The aim of this research is to use the K-Nearest Neighbor (KNN) algorithm to detect stunting nutritional status in toddlers. K-Nearest Neighbor (KNN) is a classification algorithm that uses a set of K values ​​from the closest data (its neighbors) as a reference to determine the class of incoming data. KNN classifies data based on its similarity or closeness to other data. The dataset used includes parameters of age, gender and height. The research approach is the CRISP-DM (Cross Industry Standard Process for Data Mining) method, which begins with business knowledge, followed by EDA and modeling, evaluation, testing and report preparation. The result shows that the KNN algorithm can accurately categorize children as stunted or not based on age (U) and height (TB), with the maximum level of accuracy and the lowest error rate at k = 5. At this optimal value (k), this algorithm has an accuracy of 99.87%, Recall 99.84%, and precision 99.73.
Load Balancing untuk Lalu Lintas Tinggi pada Lingkungan Cloud Menggunakan Metode Round Robin Dodi Siregar; Afrilian Ariangga; Sarudin Sarudin; Herlina Harahap; Risko Liza
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.42662

Abstract

The rapid development of technology has changed many aspects of the data communication process, especially in cloud computing. Cloud computing or commonly known as cloud computing is now widely used. With the increase in cloud usage, there is a significant increase in traffic and causes a load imbalance between servers in the cloud environment. To overcome load imbalance on cloud servers, this research proposes the use of load balancing with a round robin method. Load balancing divides traffic equally between servers to prevent overload and system failure. The round robin algorithm ensures fair distribution by sending requests alternately to available servers. Testing shows a significant reduction in error rates when using load balancing. As many as 30 thousand users per second, the error dropped to 42.37%, 50 thousand users per second caused an error of 66.31%, and 100 thousand users per second resulted in an error of 63.08%. This indicates increased system scalability and ability to effectively handle surges in user requests. Overall, implementing load balancing using the round robin method is able to improve system performance in handling high traffic in a cloud environment, maintain stability, and minimize the risk of failure.
Implementasi Sitamia dalam Pemetaan Budidaya Ikan Air Tawar Berbasis Website di Daerah Lahan Kering TTU Krisantus Jumarto Tey Seran; Yasinta Oktaviana Legu Rema
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.35550

Abstract

Fresh water fish pond information spreading over North Central Timor is still very limited to be known by the community. The hardship to obtain the location impacts the fish farmers in doing the transaction with their consumers. This research is conducted with the aim of developing Geographic Information System to the public so that they can easily find the spreading of fish pond locations, particularly around the North Central Timor Regency. The fish pond information system being developed in this research is called SITAMIA. The SITAMIA information system is able to spread information on fish pond, fish products being sold, fish pond location, the distance between buyers and sellers, and the navigation map to the fish selling area. Prototype method is utilized as the roadmap in developing the SITAMIA. The usage of this method is due to its prototype implementation is in line with the users’ need in the community. The development and the application adjustment of this method are based upon the SITAMIA users’ inputs. There are five stages in prototype method namely: Communication, Quick Plan, Modelling Quick Design, Prototype, Deployment Delivery and Feedback. Black box method was employed in website testing where each function of the system was tested. The test result showed that SITAMIA could manage and perform information as expected.

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