Hasil Pencarian  ::  Simpan CSV :: Kembali

"[ABSTRAKPerkembangan sistem informasi saat ini menyebabkan sistem informasi yang digunakan dalam sebuah organisasi terus bertambah dan semakin kompleks. Halini juga memunculkan fenomena meningkatnya jumlah data yang diolah dandihasilkan oleh sistem informasi. Kondisi ini membawa tantangan baru dalampengawasan operasional sistem informasi, seperti keterlambatan peringatankesalahan atau membanjirnya jumlah peringatan yang tidak tepat sasaran. Penelitian ini bertujuan membangun sebuah sistem pengawasan aplikasi padasistem informasi di PT. XYZ menggunakan Event Driven Architecture dan Machine Learning. Pengembangan ini menggunakan perangkat lunak R dan TIBCO StreamBase.

---

ABSTRACTAdvancement in information system nowadays has generated more quantities and complexities of an organization?s information system. This factalso leads to a phenomenon of the increase of data volume being processed andalso generated by any information system. This condition has brought a newchallenge in the operation and monitoring of the information systems, such asdelays in failure alert and also floods of incorrect alerts. This research aims to build a monitoring system for applications in the PT. XYZ information systems, using Event Driven Architecture and Machine Learning techniques. This development is done using R software and also TIBCO StreamBase. , Advancement in information system nowadays has generated more quantities and complexities of an organization’s information system. This factalso leads to a phenomenon of the increase of data volume being processed andalso generated by any information system. This condition has brought a newchallenge in the operation and monitoring of the information systems, such asdelays in failure alert and also floods of incorrect alerts. This research aims to build a monitoring system for applications in the PT. XYZ information systems, using Event Driven Architecture and Machine Learning techniques. This development is done using R software and also TIBCO StreamBase. ]"

"Pemrosesan data real-time dalam skala besar semakin krusial seiring meningkatnya volume data dari berbagai sumber seperti sensor dan perangkat Internet of Things (IoT). Apache Kafka merupakan platform event streaming yang banyak digunakan dalam sistem pemrosesan data real-time karena sifatnya yang scalable dan fault-tolerant. Namun, efektivitas Kafka sangat bergantung pada konfigurasi sistemnya, termasuk strategi multi-threading dan partitioning. Penelitian ini bertujuan untuk mengevaluasi dampak implementasi multi-threading Kafka consumer pada arsitektur berbasis Kafka dalam konteks sistem homogenisasi dan Complex Event Processing (CEP). Dengan mengadopsi arsitektur milik Corral-Plaza et al. (2020) sebagai baseline dan membandingkannya dengan pendekatan multi-threading yang diadaptasi dari Leang et al. (2019), dilakukan eksperimen pada tiga skenario: single-threaded, multi-threading pada Kafka consumer, dan multi-threading pada homogenisasi atau pemrosesan data. Setiap skenario diuji dalam berbagai message rate mulai dari 5.000 hingga 200.000 msg/s, dengan pengukuran metrik throughput, consumer lag, dan penggunaan resource. Hasil eksperimen menunjukkan bahwa penerapan multi-threading secara signifikan meningkatkan throughput dan mengurangi consumer lag tanpa menimbulkan lonjakan konsumsi resources yang tidak proporsional. Di antara skenario multi-threading tersebut, skenario multi-threading pada homogenisasi (S3) memang menunjukkan penggunaan CPU yang lebih tinggi dan konsumsi memori yang setara dengan S2. Namun, keunggulan utama S3 terletak pada kemampuannya menjaga tingkat consumer lag tetap rendah, terutama pada message rate tinggi, meskipun tidak menghasilkan throughput yang secara signifikan lebih besar dibandingkan skenario S2. Dengan demikian, penelitian ini membuktikan bahwa strategi multi-threading Kafka consumer, jika diterapkan secara tepat, dapat meningkatkan performa sistem stream processing Kafka secara signifikan, terutama dalam skenario aplikasi real-time yang membutuhkan latensi rendah dan throughput tinggi.

---

Real-time data processing at scale is increasingly crucial as the volume of data from various sources such as sensors and Internet of Things (IoT) devices continues to grow. Apache Kafka is widely used as an event streaming platform in real-time systems due to its scalable and fault-tolerant architecture. However, Kafka’s performance is highly dependent on proper configuration, particularly regarding multi-threading strategies and partitioning. This study aims to evaluate the impact of multi-threading in a existing Kafka-based architecture within the context of data homogenization and Complex Event Processing (CEP). Experiments were conducted on three scenarios: original Single Stream Thread and the two proposed architecture: multi-threaded Kafka stream thread and multi-threaded data processing. Each scenario was tested with message rates ranging from 5,000 to 200,000 messages per second. Evaluation metrics include throughput, consumer lag, and system resource usage. The results show that multi-threading significantly improves throughput and reduces consumer lag without causing disproportionate increases in resource consumption. Among the multi-threaded scenarios, the data processing multi-threading model exhibited higher CPU usage and memory usage comparable to the consumer multi-threading scenario. Its main advantage lies in maintaining lower consumer lag under high message rates, although it does not deliver significantly higher throughput than multi-threaded stream thread. These findings indicate that, when applied appropriately, multi-threading strategies can significantly enhance the performance of Kafka-based stream processing systems."