Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security

A Novel Hybrid Model for High-Accuracy Malware Detection in The Internet of Medical Things (IoMT) Environment.

The Internet of Medical Things (IoMT) has revolutionized modern healthcare by enabling the collection and analysis of real-time data. However, this interconnected ecosystem also introduces significant security risks, particularly malware attacks that compromise patient safety and data privacy. Traditional security measures are often insufficient because of resource constraints and the real-time operational demands of IoMT devices. This research proposes an optimized hybrid machine learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM), random forest (RF), and principal component analysis (PCA) to enhance malware detection in IoMT environments. The proposed method includes an adaptive feature selection mechanism, a resource-efficient architecture, and an ensemble learning model with machine learning capabilities. Validation through experimentation using the CIC-MalMem-2022 dataset, which comprises labeled memory dumps from benign and various malware processes, demonstrated that the proposed framework outperformed current hybrid models while reducing computational costs, achieving a detection accuracy of 99.59%. This study presents a scalable and efficient security solution designed to address the constraints of IoMT devices, addressing critical challenges in healthcare cybersecurity. ABSTRAK: Internet Benda Medikal (IoMT) telah merevolusikan penjagaan kesihatan moden dengan membolehkan pengumpulan dan analisis data masa nyata. Walau bagaimanapun, ekosistem saling berkaitan ini juga memperkenalkan risiko keselamatan yang ketara, terutamanya serangan perisian hasad yang menjejaskan keselamatan pesakit dan privasi data. Langkah keselamatan tradisional selalunya tidak mencukupi kerana kekangan sumber dan permintaan operasi masa nyata peranti IoMT. Penyelidikan ini mencadangkan rangka kerja pembelajaran mesin hibrid yang dioptimumkan dengan menyepadu Rangkaian Konvolusi Neural (CNN), Memori Jangka Panjang Pendek (LSTM), Rawak Forest (RF) dan Analisis Komponen Prinsipal (PCA) bagi meningkatkan pengesanan perisian Malware dalam persekitaran IoMT. Kaedah yang dicadangkan ini termasuk mekanisme pemilihan ciri penyesuaian, seni bina cekap sumber dan keupayaan pembelajaran mesin bersama model pembelajaran ansembel. Ujian melalui eksperimen menggunakan dataset CIC-MalMem-2022, yang terdiri dari pelupusan memori berlabel daripada proses tidak merbahaya dan pelbagai Malware, menunjukkan bahawa kajian yang dicadangkan mengatasi model Hibrid semasa, juga menurunkan kos pengiraan, mencapai ketepatan pengesanan 99.59%. Kajian ini menyumbang kepada penyelesaian keselamatan berskala dan cekap yang disesuaikan dengan kekurangan peranti IoMT, menangani cabaran kritikal dalam keselamatan siber penjagaan kesihatan

Analisa Kelayakan Capital Budgeting Jaringan Backbone Kabel Serat Optik Palapa Ring Studi Kasus : Palapa Ring Barat

Dalam rangka masyarakat Indonesia yang modern dan berbasis informasi, pemerintah bekerjasama dengan beberapa perusahaan telekomunikasi swasta menggelar mega-proyek pembangunan jaringan infrastruktur telekomunikasi berupa jaringan backbone kabel serat optik berkecepatan tinggi yang dinamakan Palapa Ring. Tujuan Palapa Ring antara lain untuk mengurangi kesenjangan digital antara Indonesia Bagian Barat & Indonesia Bagian Timur serta menyediakan akses telekomunikasi bagi masyarakat dengan tujuan pemerataan akses informasi untuk meningkatkan kesejahteraan dan mengurangi kemiskinan. Dalam perancangan jaringan ekstensi, parameter diatas ditambah lagi dengan proyeksi kapasitas jaringan yang dibutuhkan untuk beberapa tahun kedepan. Landing Stations ini terdiri dari 12 Kota Pantai beserta analisa penempatannya yang tidak semuanya sama dengan rekomendasi KMI. Untuk proyeksi kebutuhan kapasitas, didapatkan angka kebutuhan kapasitas untuk masing-masing Landing Stations sampai tahun 2033. Penelitian  ini  bertujuan  untuk  menganalisa kelayakan  dari  rencana investasi yang akan dilaksanakan PT. XXX. Rencana investasi ini berupa pembangunan proyek Palapa Ring Barat dengan total investasi sebesar Rp. 1,000,000,000,000 dengan tingkat bunga sebesar 18% & 30%. Dengan alat analisis Payback Period, Discounted Payback Period, Net Present Value, dan Internal Rate Of Return. Tiga alat analisis tersebut dipakai juga oleh PT. XXX untuk mengukur layak atau tidaknya proyek tersebut. Dari hasil analisis dan rencana proyek Palapa Ring Barat diperoleh Payback Period (PP) 3 tahun 1 bulan dan Discounted Payback Period 4 tahun 5 bulan  dari target PT. XXX yaitu 15 tahun, Net Present Value (NPV) Rp. 1,392,644,795,000 dari target yang di tentukan PT. XXX yang hasilnya positif, Internal Rate Of Return (IRR) 35 % dari 18 % & 30 % yang di targetkan oleh PT. XXX. Dan juga didapatkan hasil Subsidi KPBU dari pemerintah Rp. 1,490,772,000,000 dengan rincian simulasi pembayaran selama 15 Tahun dengan Interest 0 % sebesar Rp. 99,384,800,000 / Tahun

Classification Of Tea Plantation Using Orthomosaics Stitching Maps From Aerial Images Based CNN

In Indonesia, Tea is an important economic crop that is widely grown, and in many countries, accurate mapping of tea plantations is essential for the operation, management, and monitoring of the growth and development of the tea industry. We propose a classification of tea plantations using orthomosaics from aerial images based on the Convolutional Neural Network (CNN) which identifies the condition of the tea plantations with the parameters observed, namely the condition of the tea leaves, estimated yields achieved, and monitoring of treeless areas caused by tree death. In this study, we took a sample of 20 hectares. We classify images based on maps generated by drones in previous studies. Image segmentation is performed to maintain image objects, while an enhanced CNN model is used to extract deep image features. To get complete results, this study uses UAV (Unmanned Aerial Vehicle) imagery as the basis for the map, which is then combined or stacked into one image. The results of the images that are used as maps undergo image classification, where the information contained in the map is mapped and divided according to its type. The area of ​​the tea plantations sampled is 20 ha, and the threshold for the image captured by the UAV is 5% of the total area captured, which is around 1 ha. If the image created by the UAV has an error of more than 5%, then the image does not meet the classification requirements. We determine this margin of error based on the performance of the drone camera capture when capturing Fig. 2, and the resolution used is 4096 x 2160 for each image captured by the drone. We conclude that the proposed method for mapping tea plantations using ultra-high resolution remote sensing imagery is effective and has great potential for mapping tea plantations in areas such as the development of drone aerial photography methods for tea plantations based on image classification for forecasting. tea plantations Image stitching can be used to improve the monitoring of tea plantations and predict harvest time using a classification process. The tea garden map has 5 types of information categorized by harvest time, medium leaf tea, milled tea, tea, and old tea. The success of image recognition shows the error matrix data by testing 123 random points spread over the map, of which 113 random points were identified with an average accuracy of 91.87%, this value is of course very good and exceeds the specified threshold of 75%. When using this method, an error occurs that the colors of similar pixels cannot be distinguished, resulting in an incorrect detection. In addition, the image stitching method using the orthomosaics method has succeeded in performing image stitching and can be well applied to classification using the CNN approach

Analisa Kelayakan Capital Budgeting Jaringan Backbone Kabel Serat Optik Palapa Ring Studi Kasus : Palapa Ring Barat

Dalam rangka masyarakat Indonesia yang modern dan berbasis informasi, pemerintah bekerjasama dengan beberapa perusahaan telekomunikasi swasta menggelar mega-proyek pembangunan jaringan infrastruktur telekomunikasi berupa jaringan backbone kabel serat optik berkecepatan tinggi yang dinamakan Palapa Ring. Tujuan Palapa Ring antara lain untuk mengurangi kesenjangan digital antara Indonesia Bagian Barat &amp; Indonesia Bagian Timur serta menyediakan akses telekomunikasi bagi masyarakat dengan tujuan pemerataan akses informasi untuk meningkatkan kesejahteraan dan mengurangi kemiskinan. Dalam perancangan jaringan ekstensi, parameter diatas ditambah lagi dengan proyeksi kapasitas jaringan yang dibutuhkan untuk beberapa tahun kedepan. Landing Stations ini terdiri dari 12 Kota Pantai beserta analisa penempatannya yang tidak semuanya sama dengan rekomendasi KMI. Untuk proyeksi kebutuhan kapasitas, didapatkan angka kebutuhan kapasitas untuk masing-masing Landing Stations sampai tahun 2033. Penelitian  ini  bertujuan  untuk  menganalisa kelayakan  dari  rencana investasi yang akan dilaksanakan PT. XXX. Rencana investasi ini berupa pembangunan proyek Palapa Ring Barat dengan total investasi sebesar Rp. 1,000,000,000,000 dengan tingkat bunga sebesar 18% &amp; 30%. Dengan alat analisis Payback Period, Discounted Payback Period, Net Present Value, dan Internal Rate Of Return. Tiga alat analisis tersebut dipakai juga oleh PT. XXX untuk mengukur layak atau tidaknya proyek tersebut. Dari hasil analisis dan rencana proyek Palapa Ring Barat diperoleh Payback Period (PP) 3 tahun 1 bulan dan Discounted Payback Period 4 tahun 5 bulan  dari target PT. XXX yaitu 15 tahun, Net Present Value (NPV) Rp. 1,392,644,795,000 dari target yang di tentukan PT. XXX yang hasilnya positif, Internal Rate Of Return (IRR) 35 % dari 18 % &amp; 30 % yang di targetkan oleh PT. XXX. Dan juga didapatkan hasil Subsidi KPBU dari pemerintah Rp. 1,490,772,000,000 dengan rincian simulasi pembayaran selama 15 Tahun dengan Interest 0 % sebesar Rp. 99,384,800,000 / Tahun.</jats:p

Penerapan Simulasi Emulated Virtual Environment – New Generation (Eve-NG) (Studi Kasus Di SMKN 1 Buah Dua Sumedang – SMK PK)

Pemahaman &amp; Keterampilan  penguasaan di teknologi telekomunikasi khususnya dibidang jaringan komputer merupakan tujuan siswa setelah lulus dari SMK TKJ. Untuk meningkatkan tingkat keterampilan diperlukannya jam terbang yang luas serta pengalaman baik implementasi di lapangan kerja. Dalam proses pembelajaran perangkat keras yang digunakan berupa peralatan jaringan yang harganya relatif mahal. Untuk mengatasi masalah ini dengan menggunakan software simulasi jaringan yaitu "Eve-NG. Pada penulisan ini khususnya guru/siswa di SMKN 1 Buah Dua Sumedang Jurusan TKJ akan mendapatkan penjelasan mengenai simulasi pelatihan oleh tim Pendamping SMK Pusat Keunggulan dari Institut Teknologi Telkom Jakarta tentang aplikasi EVE-NG meliputi perangkat Router Mikrotik &amp; Switch Cisco.</jats:p

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security.</jats:p

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security

Enhancement of images compression using channel attention and post-filtering based on deep autoencoder

Image compression has recently become a crucial research topic, especially in data transformation and storage processes. Conventional methods have long been used to significantly reduce image file sizes; however, they are insufficient to meet current transmission and storage needs due to various issues often encountered in the received image size and quality. Currently, deep learning-based approaches using autoencoders are considered one of the best solutions. This research aims to explore the potential of artificial neural networks to achieve more optimal data compression with better image reconstruction results by leveraging hyperparameters in the compression process and channel attention. Furthermore, it introduces a novel image compression architecture utilizing convolutional autoencoders to replace traditional transformation roles and the use of post-filtering to enhance the quality of the reconstructed images. Experimental results using two datasets, CLIC for training and KODAK for testing, demonstrate that this method outperforms existing conventional methods and some previous studies. Finally, with an average PSNR improvement of 34% and an MS-SSIM improvement of 8%, the model in this research significantly enhances the rate distortion (RD) performance compared to previous approaches