Pulau Jawa merupakan salah satu daerah rawan gempa bumi dan memiliki populasi penduduk yang padat. Dalam hal ini pemerintah diharuskan untuk memberikan perhatian lebih pada penanggulangan bencana khusunya di pulau jawa. Maka dari itu penelitian ini hadir untuk mengetahui dampak gempa bumi berdasarkan tingkat keparahannya dengan cara melakukan klasterisasi data pesebaran dampak bencana gempa bumi (2012 - 2021) dari Badan Nasional Penanggunlangan Bencana (BNPB) menggunakan algoritma K-Means Clustering. Dari penelitian ini ditemukan bahwa dampak bencana dapat dibagi menjadi 4 klaster. Klaster 1 memiliki dampak bencana paling banyak meliputi meninggal, luka, menderita, mengungsi, kerusakan rumah, fasilitas pendidikan, fasilitas kesehatan, fasilitas ibadah, kantor, jembatan, dan kios. Klaster ini memiliki Mean Absolute Error (MAE) senilai 0,017, Mean Square Error (MSE) senilai 0,002, standar deviasi senilai 0,255 dan variance senilai 0,065. Klaster 2 meliputi meninggal, luka, menderita, mengungsi, rumah, fasilitas pendidikan, fasilitas kesehatan, fasilitas ibadah, pada korban menderita, mengungsi, kerusakan rumah dan fasilitas ibadah. Klaster ini memperoleh MAE senilai 0,053, MSE senilai 0,011, standar deviasi senilai 0,249 dan variance senilai 0,062. Klaster 3 meliputi korban luka, menderita, mengungsi, kerusakan rumah, fasilitas pendidikan, fasilitas kesehatan, fasilitas ibadah dan kantor. klaster ini memperoleh MAE senilai 0,102, MSE senilai 0,039, standar deviasi senilai 0,212 dan variance senilai 0,045. Klaster 0 memiliki dampak bencana paling sedikit meliputi kerusakan rumah, fasilitas pendidikan, fasilitas kesehatan, fasilitas ibadah, kantor, dan kios. klaster ini memperoleh MAE sebesar 0,021, MSE senilai 0,005, standar deviasi senilai 0,251 dan variance senilai 0,063.

Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.

An intrusion detection system (IDS) is required to protect data from security threats that infiltrate unwanted information via a regular channel, both during storage and transmission. This detection system must differentiate between normal data and abnormal or hacker-generated data. Additionally, the intrusion detection system (IDS) must be precise and quick to analyze real-time traffic data. Despite extensive research, there is still a need to improve detection accuracy and speed due to the tremendous increase in internet traffic volume and variety. This paper introduces a novel, efficient, and accurate approach for real-time intrusion detection and classification based on the Residual Feedforward Neural Network (RFNN) algorithm. The RFNN algorithm is developed to avoid overfitting, improve detection accuracy, and accelerate training and inference. Additionally, the suggested algorithm is highly adaptable and straightforward to accommodate different types of intrusion. The prominent NSL-KDD dataset was utilized for training and testing in this study. The accuracy obtained for two and five classes was 84.7 percent and 90.5 percent, respectively. Additionally, the identification speed was $15\ \mu\mathrm{s}$ and $14\ \mu\mathrm{s}$ , respectively, indicating that real-time detection is feasible.

Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies.

M-ary frequency shift keying (FSK) is a low-power, low-data-rate modulation scheme currently used by manufacturers of data transmission equipment. Inter symbol interference (ISI) occurs in all communication systems. To analyze the effect of the raised cosine filter and adaptive equalizer on reducing the ISI, the binary FSK (BFSK) modulation technique has been studied extensively in this research. In this study, we have selected the square root raised cosine filter and the sign least mean square (LMS) equalizer. The bit error rate (BER) performance of the 2-FSK system with the additive white Gaussian noise (AWGN) channel is evaluated and analyzed with the help of Simulink. The best BER value of 1.7e-03 was obtained in this study.

Dalam sebuah perusahaan penyedia layanan jaringan internet, di tuntut untuk memiliki kulitas jaringan high availability dan high reliability. Dalam mendukung kualifikasi jaringan internet terbaik maka salah satunya perlu menjaga kestabilan bandwidth internet pelanggan. Salah satunya menjaga kepadatan traffic data pada jaringan internet. Congestion merupakan pengumpulan paket data yang melebihi kapasitas bandwidth yang tersedia padasebuah link, hal ini memberikan dampak penurunan kinerja jaringan internet. Permasalahan di atas dapat di selesaikan salah satunya dengan menerapkan penggunaan metode link aggregation dan teknologi load balance. Pada penelitian ini penulis menerapkan metode link aggregation yang digunakan dalam merancang redundancy link dan load balance dalam membuat skema membagi traffic untuk penyeimbang beban berdasarkan src address dan src port, serta dst address dan dst port. Dari hasil pengujian penggunaan link aggregation dan load balance pada penenlitian ini dapat membantu menjaga kestabilan throughput dengan nilai pengukuran sebesar 10GB dengan standart MTU (Maximum Transmission Unit) yang menandakan hasil test pengukuran paket data terbesar yang dapat di transmisikan melalui sebuah jaringan. Keyword : link aggregation, load balance, congestion, MTU