Analisis dan Mitigasi Kerentanan DDoS pada Infrastuktur Jaringan dengan Teknik Hierarchical Clustering dan Firewall IPTables

Network Security infrastructure including routers and server devices, which are connected directly to the global internet has become an important issue along with the increase in internet communications in maintaining the confidentiality, integrity and availability of digital communications. The most challenging problem is the network infrastructure for exploiting a monoculture of routers and servers and detecting Distributed Denial-of-Service (DDoS) attacks. This research aims to combine analysis and mitigation techniques with Hierarchical Clustering single linkage, complete linkage, average linkage and ward linkage as well as IPTables firewall filtering mitigation measures, to analyze DDoS logging data on NIDS suricata, with low, medium and high severity levels exploited from the network public. Clusteirng single linkage deployments produces cluster 3 with a DDoS logging intensity level of high severity, on the TCP Sync Flood protocol type. Cluster 3 shows high severity for the source IP address. The complete linkage clustering technique also provides significant results with a large number of potential DDoS logging, found in cluster 1 and cluster 2. The results of the average linkage distribution show a group with a low average severity level for DDoS. The Ward linkage clustering produces a more uniform group of attributes for each n_clusters 1 to cluster 6. Implementation of mitigation techniques with IPSet and firewall scripting IP Tables provides positive results in reducing the workload of router and vServer devices when facing DDoS attacks. After convergence, the running status resulted in the workload of vCPU resources experiencing a decrease in the percentage of vCPU vR1 by 10%, vCPU vR2 by 9% and memory by 11%.Keamanan infrastruktur jaringan termasuk perangkat router dan server, yang terhubung langsung ke global internet telah menjadi masalah penting seiring dengan meningkatnya komunikasi internet dalam menjaga kerahasiaan, integritas dan ketersediaan komunikasi digital. Masalah paling krusial merupakan infrastruktur jaringan untuk monokultur perangkat router dan server yang diekspolitasi dan mendeteksi serangan Distributed Denial-of-Service (DDoS). Penelitian ini bertujuan menggabungkan teknik analisis dan mitigasi dengan Hierarchical Clustering single linkage, complete linkage, average linkage dan ward linkage serta tindakan mitigasi filtering firewall IPTables, untuk menganalisis data logging DDoS pada suricata NIDS, dengan severity level low, medium dan high yang dieksploitasi dari jaringan public. Pengelompokan penyebaran single linkage menghasilkan cluster 3 dengan tingkat intensitas logging DDoS dengan severity high, pada tipe protocol TCP Sync Flood. Cluster 3 menghasilkan severity high source IP address. Clustering complete linkage menghasilkan potensi high logging DDoS, terdapat pada cluster 1 dan cluster 2. Hasil penyebaran average linkage menunjukkan kelompok dengan severity level average low untuk DDoS. Teknik Ward linkage menghasilkan kelompok yang lebih seragam pada atribut pada setiap n_clusters 1 sampai cluster 6. Implementasi teknik mitigasi dengan IPSet dan firewall scripting IP Tables memberikan hasil positif dalam mengurangi beban kerja perangkat router dan vServer saat menghadapi serangan DDoS. Setelah konvergensi status running menghasilkan beban kerja dari sumber daya vCPU mengalami penuruan persentasi vCPU vR1 10%, vCPU vR2 9% dan memory 11%.

Sistem Deteksi Rambu Lalu Lintas Berbasis You Only Look Once (Yolov8) Menggunakan Rasberry Pi

Autonomous vehicle (AV) is projected to become a part of land transportation in the New Capital City of Nusantara (IKN). . This opens up opportunities for related research to be conducted prior to actual implementation.. One of the capabilities of AV is recognizing different types of traffic signs. Therefore, this study aims to design a traffic sign detection system as an insight and support for the implementation of autonomous vehicles in IKN Nusantara. To achieve this, a total of 11,157 images containing 30 types of traffic signs were collected as the primary dataset along the roads of the Special Region of Yogyakarta.  Variations of the dataset were also added in the form of noise, blur, and dark. During the model training, hyper-parameter configurations such as learning rate, epoch, and image size were performed. In this study, the You Only Look Once v8 method is used. The results of testing with daytime data showed an accuracy of 80%, recall of 83%, and precision of 96%. In contrast, tests with night data showed 93% precision, 70% recall, and 67% accuracy. This test works well for cars moving at speeds below than 40 km/h because of the Raspberry Pi hardware\u27s computing speed constraintsKendaraaan autonomous vehicle (AV) direncanakan mewarnai alat transportasi darat di Ibu Kota Nusantara (IKN). Hal ini membuka kemungkinan penelitian–penelitian terkait sebelum implementasi benar–benar dilakukan. Salah satu kemampuan yang dimiliki oleh AV adalah mengenali jenis dari rambu lalu lintas. Untuk itu, penelitian ini bertujuan untuk merancang sistem deteksi rambu lalu lintas sebagai wawasan dan dukungan penerapan autonomous vehicle di IKN Nusantara. Untuk itu, sebanyak 11.157 citra yang berisi 30 jenis rambu lalu lintas sebagai dataset primer telah diambil di sepanjang jalan Daerah Istimewa Yogyakarta. Ditambahkan juga variasi dataset berupa penambahan noise, blur, dan dark. Pada training model dilakukan konfigurasi hyper-parameter berupa learning rate, epoch, dan ukuran citra. Penelitian ini menggunakan algoritma You Only Look Once v8. Pengujian menggunakan data siang menghasilkan precision 96%, recall 83%, dan accuracy 80%. Sedangkan, pengujian menggunakan data malam menghasilkan precision 93%, recall 70%, dan accuracy 67%. Pengujian ini efektif untuk kendaraan bergerak dengan kecepatan di bawah 40 km/jam karena keterbatasan kecepatan komputasi perangkat keras Raspberry P

Pengembangan Sistem Iot Untuk Pemantauan Berat, Tinggi, Suhu, Dan Denyut Jantung Dengan Rekomendasi Kesehatan

Health facilities such as community health centers, clinics, and hospitals play a crucial role in providing basic health check-up services, including measuring weight, height, body temperature, and heart rate. This examination is important as an initial step in early detection of disease and routine monitoring of patient health conditions. This study aims to develop an Internet of Things (IoT)-based health monitoring system that can automatically measure these body parameters, display data in real-time, print test results via a thermal printer, and provide simple health recommendations. This study uses the Research and Development (R&D) method with a modified Borg and Gall model approach. The system trial was conducted at the UI student dormitory in May 2025, involving 38 student respondents as samples. The results of the study showed that the developed system was able to perform measurements with high accuracy and fast response time. In addition, the features of printing results and providing health recommendations have been shown to increase service efficiency and user satisfaction. This system has the potential to support remote health services (telemedicine) and increase public awareness of the importance of independent and regular health monitoringFasilitas kesehatan seperti puskesmas, klinik, dan rumah sakit memiliki peran krusial dalam menyediakan layanan pemeriksaan kesehatan dasar, termasuk pengukuran berat badan, tinggi badan, suhu tubuh, dan denyut jantung. Pemeriksaan ini penting sebagai langkah awal dalam deteksi dini penyakit serta pemantauan rutin kondisi kesehatan pasien. Penelitian ini bertujuan untuk mengembangkan sistem pemantauan kesehatan berbasis Internet of Things (IoT) yang dapat secara otomatis mengukur parameter tubuh tersebut, menampilkan data secara real-time, mencetak hasil pemeriksaan melalui printer thermal, serta memberikan rekomendasi kesehatan sederhana. Penelitian ini menggunakan metode Research and Development (R&D) dengan pendekatan model Borg and Gall yang dimodifikasi. Uji coba sistem dilakukan di Asrama mahasiswa UI pada bulan Mei 2025, dengan melibatkan 38 responden mahasiswa sebagai sampel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu melakukan pengukuran dengan akurasi yang tinggi dan waktu respon cepat. Selain itu, fitur pencetakan hasil dan pemberian rekomendasi kesehatan terbukti meningkatkan efisiensi pelayanan dan kepuasan pengguna. Sistem ini berpotensi mendukung layanan kesehatan jarak jauh (telemedicine) dan meningkatkan kesadaran masyarakat terhadap pentingnya pemantauan kesehatan secara mandiri dan berkala

Implementasi Algoritma Genetika untuk Pengaturan Gizi Remaja

Adolescence is an important moment in human life marked by growth, emotional, and psychosocial. During adolescence, a healthy diet becomes very crucial to support adolescent development and prevent future health problems. Therefore, this study aims to implement the Genetic Algorithm for Teenager Nutrition Management in an expert system. This system is designed to provide recommendations for food menus that are in accordance with the nutritional needs of a teenager, which include protein, carbohydrates, energy/calories, and fat. Genetic algorithms are used so that the food recommendation process can be in accordance with the daily nutritional needs of teenagers. The variables in this study are age, gender, weight, height, and physical activity. Meanwhile, the results of this system are in the form of total energy or calorie needs, protein, fat, and carbohydrates included in the daily food menu according to the nutritional needs of teenagers. This system can provide daily food menu recommendations in the form of a menu list using a genetic algorithm. The best fitness value is 0.0098 at a generation size of 100 and 670 dataset of foodMasa remaja merupakan momen yang penting dalam kehidupan manusia yang ditandai oleh pertumbuhan, emosional, dan psikososial. Pada masa ini, pola makan yang sehat menjadi sangat krusial untuk mendukung perkembangan remaja dan mencegah masalah kesehatan di masa depan. Oleh karena itu, penelitian ini bertujuan untuk mengimplementasikan algoritma genetika untuk pengaturan gizi  remaja pada sistem pakar. Sistem ini dirancang untuk memberikan rekomendasi menu makanan yang sesuai dengan kebutuhan gizi seorang remaja, yang mencakup protein, karbohidrat, energi/kalori, dan lemak. Algoritma genetika digunakan supaya proses rekomendasi makanan dapat sesuai dengan kebutuhan gizi harian remaja. Variabel dalam penelitian ini adalah umur, jenis kelamin, berat badan, tinggi badan, dan aktivitas fisik. Sedangkan, hasil dari sistem ini berupa total kebutuhan energi atau kalori, protein, lemak, dan karbohidrat yang disertakan pada menu makanan harian sesuai dengan kebutuhan gizi remaja. Sistem ini mampu memberikan hasil rekomendasi menu makanan harian dalam bentuk daftar menu dengan menggunakan algoritma genetika. Hasil nilai fitness terbaik sebesar 0,0098 pada ukuran generasi 100 dan 670 dataset makanan.

Applying the Analytical Hierarchy Process to Select Strategic Locations for Culinary MSMEs in Tomohon City

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in Indonesia\u27s economy, making up over 99% of business units. In Tomohon City, located in North Sulawesi, Indonesia, MSMEs, especially in the culinary sector, have faced challenges due to the COVID-19 pandemic, which led to significant revenue losses and business closures. However, recovery efforts are underway, prompting the development of a web-based system to aid culinary businesses in selecting optimal locations. Utilizing the Analytic Hierarchy Process (AHP) method, the system evaluates potential sites based on five criteria: completeness of infrastructure, population density, competition, community incomes, and business capital. Each criterion is assessed using sub-criteria such as very good to very inadequate, providing tailored recommendations for Downtown District, Dotu Tololui Tua Statue, Kakaskasen Highway Area, and Beriman Terminal. Among these criteria, infrastructure completeness (40.4%) is prioritized most, followed by population density (26.2%), competition (18.1%), community incomes (10.4%), and business capital (4.9%). The sensitivity analysis indicates that changes in criteria weights significantly impact the rankings of alternatives, with infrastructure and community incomes causing minor changes but preserving the top-ranked options, while population density and business capital lead to more substantial shifts, including changes in the top position

Integrasi Sensor Pintar dengan Sistem IoT untuk Memantau Kadar Gas Nitrogen pada Reaktor Biogas

Renewable energy is a type of energy derived from natural processes and can be used continuously. The use of new and renewable energyrepresents a societal effort to reduce reliance on fossil fuels. Biogas is one of the newest sustainable energy sources in the home.One such renewable energy source applicable at the household level is biogas, a gas produced by microorganisms through the anaerobic fermentation of organic matter. This study presents an Internet of Things (IoT)-based nitrogen gas monitoring system for determining the quantities of nitrogen gas in biogas. The hardware system for detecting nitrogen gas levels utilizes an MQ-135 sensor and an ESP8266 microcontroller for processing data via the internet or in real-time.  Monitoring results indicate that the nitrogen gas concentration in biogas does not exceed 20,000 ppm, which aligns with the established standard range of 2%–5% nitrogen, or approximately 20,000 ppm. Based on these findings, the biogas is deemed to meet the required quality criteria.Energi terbarukan merupakan salah satu energi yang berasal dari proses alam akan tetapi tetap ada dan dapat digunakan secara terus menerus. Penggunaan energi baru terbarukan merupakan bentuk masyarakat mengurangi penggunaan bahan bakar fosil. Salah satu energi baru terbarukan yang berada pada lingkup rumah tangga yaitu biogas. Biogas adalah gas yang dihasilkan oleh mikroorganisme selama fermentasi anaerobik bahan organik. Penelitian ini mengusulkan metode monitoring gas nitrogen berbasis Internet of Things (IoT) yang mampu mengetahui kadar gas nitrogen yang terkandung dalam biogas. Perancangan perangkat keras sistem untuk mendeteksi kadar gas nitogen digunakan perangkat sensor MQ-135 dan ESP8266 untuk pengolahan data melalui internet atau secara langsung. Hasil monitoring menunjukkan kadar gas nitrogen yang terdapat pada biogas tidak melebihi 20000 ppm. Sesuai dengan kadar standar yang ditetapkan berupa kadar nitrogen sebesar 2% - 5% atau 20.000 ppm. Oleh karena itu, biogas memenuhi kriteria

Implementasi Arsitektur MTCNN pada Kelas Dimensi Piksel Berbeda dan Plotting Multi-Wajah pada Hasil Deteksi

Face detection is a computer vision task to identify and verify a person based on a photo of their face. Face detection and alignment in unconstrained environments are very challenging due to various poses, illumination, and occlusions. The human face is difficult to model because there are many variables that can change, such as facial expression, orientation, lighting conditions, and partial occlusions, such as sunglasses, scarves, masks, and others. Recent studies have shown that deep learning approaches can achieve impressive performance on these two tasks. In this paper, face detection on multi-faces will be carried out as well as mapping one by one the results of the face detection obtained (face crop) for the needs of various systems related to face detection using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) approach. This study aims to implement the MTCNN architecture using TensorFlow and OpenCV, with two main benefits. First, this study is expected to provide a pre-training model that performs optimally and strengthens evidence from previous studies that have examined this model. Second, this model can be used as input for other systems. The input variable is a photo image of a face containing one or more to be processed. This photo image will have various pixel dimensions to represent different resolutions. The output variable produced is in the form of coordinates of the detected face location or in the form of landmarks of key facial points, such as the position of the eyes, the corner of the nose, and the mouth. The results of the study showed an average score on various pixel dimensions in the dataset, with an accuracy of 93%, a precision of 95%, a recall of 96%, an F1-score of 95%, and an ROC-AUC of 90.89%.Deteksi wajah merupakan tugas computer vision untuk mengidentifikasi dan memverifikasi seseorang berdasarkan foto wajah mereka. Deteksi dan penyelarasan wajah di lingkungan yang tidak dibatasi sangat menantang karena berbagai pose, iluminasi, dan oklusi. Wajah manusia sulit untuk dimodelkan karena ada banyak variabel yang dapat berubah, misalnya ekspresi wajah, orientasi, kondisi pencahayaan, dan oklusi parsial, seperti kacamata hitam, syal, topeng, dan lainnya.. Studi terbaru menunjukkan bahwa pendekatan deep learning (pembelajaran yang mendalam) dapat mencapai kinerja yang mengesankan pada dua tugas ini. Pada penelitian ini akan dilakukan pendeteksian wajah pada multi-wajah sekaligus memetakan satu persatu hasil deteksi wajah yang didapat (face crop) untuk kebutuhan berbagai sistem yang berkaitan dengan pendeteksian wajah dengan menggunakan pendekatan Multi-Task Cascaded Convolutional Neural Network  (MTCNN). Penelitian ini bertujuan untuk mengimplementasikan arsitektur MTCNN menggunakan TensorFlow dan OpenCV, dengan dua manfaat utama. Pertama, penelitian ini diharapkan dapat menyediakan model pra-pelatihan yang berkinerja optimal serta memperkuat bukti dari penelitian-penelitian sebelumnya yang telah meneliti model ini. Kedua, model ini dapat digunakan sebagai input bagi sistem lain. Variabel input berupa gambar foto wajah yang berisi satu atau lebih untuk diproses. Gambar foto wajah ini akan memiliki berbagai dimensi piksel untuk mewakili resolusi yang berbeda. Variabel output yang dihasilkan berupa koordinat lokasi wajah terdeteksi ataupun berupa landmark titik-titik kunci wajah, seperti posisi mata, sudut hidung, dan mulut. Hasil penelitian menunjukkan skor rata-rata pada berbagai dimensi piksel dalam dataset, dengan akurasi sebesar 93%, presisi 95%, recall 96%, F1-score 95%, dan ROC-AUC 90,89%.  

Analisis Sentimen dan Pemodelan Topik Percakapan Twitter dalam Pemilihan Presiden Indonesia 2024

The 2024 Indonesian presidential election has garnered significant attention owing to the increasing influence of social media on public perceptions of political leaders and information exchange. This study aimed to analyse the online discourse surrounding three prominent Indonesian presidential candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Utilising sentiment analysis, frequency analysis, and the Latent Dirichlet Allocation (LDA) algorithm, this study examines conversations on social media platforms, focusing on word frequency, sentiment, subjects, and entities that emerge in the discussions. The findings reveal that Prabowo Subianto is the most frequently mentioned candidate, followed by Anies Baswedan, and Ganjar Pranowo. Sentiment analysis indicated a predominantly neutral to slightly positive sentiment across conversations. The LDA topic analysis uncovered distinct campaign focuses for each candidate, with Anies emphasising local issues and debates, Prabowo concentrating on national discourse and defense policy, and Ganjar discussing national concerns, justice, and progress. Named Entity Recognition (NER) highlights the prominence of entities such as "Indonesia”, "Prabowo”, and "Pilpres2024" in online discussions. This study underscores the crucial role of social media in shaping public opinion and provides valuable insights into the online narratives of presidential candidates. These findings contribute to a deeper understanding of the dynamics of digital elections and offer guidance for political analysts and academics in navigating the evolving landscape of online political discourse in Indonesia.Pemilihan Presiden Indonesia 2024 telah menarik perhatian yang signifikan karena meningkatnya pengaruh media sosial terhadap persepsi publik tentang pemimpin politik dan pertukaran informasi. Penelitian ini bertujuan untuk menganalisis wacana online seputar tiga kandidat presiden Indonesia yang terkemuka: Anies Baswedan, Prabowo Subianto, dan Ganjar Pranowo. Dengan menggunakan analisis sentimen, analisis frekuensi, dan algoritma Latent Dirichlet Allocation (LDA), penelitian ini meneliti percakapan di platform media sosial, dengan fokus pada frekuensi kata, sentimen, subjek, dan entitas yang muncul dalam diskusi. Temuan menunjukkan bahwa Prabowo Subianto adalah kandidat yang paling sering disebut, diikuti oleh Anies Baswedan dan Ganjar Pranowo. Analisis sentimen menunjukkan sentimen yang didominasi netral hingga sedikit positif di seluruh percakapan. Analisis topik LDA mengungkap fokus kampanye yang berbeda untuk setiap kandidat, dengan Anies menekankan pada isu-isu lokal dan perdebatan, Prabowo berkonsentrasi pada wacana nasional dan kebijakan pertahanan, dan Ganjar mendiskusikan masalah-masalah nasional, keadilan, dan kemajuan. Named Entity Recognition (NER) menyoroti penonjolan entitas seperti "Indonesia", "Prabowo", dan "Pilpres2024" dalam diskusi online. Studi ini menggarisbawahi peran penting media sosial dalam membentuk opini publik dan memberikan wawasan yang berharga tentang narasi online seputar calon presiden. Temuan-temuan ini berkontribusi pada pemahaman yang lebih dalam tentang dinamika pemilihan umum digital dan memberikan panduan bagi para analis politik dan akademisi dalam menavigasi lanskap wacana politik online yang terus berkembang di Indonesia

Fenomena #STYOut di X: Analisis Jaringan Sosial

This research paper analyzes the distribution of information related to Shin Tae-yong controversy, as the coach of the Indonesian National Team, on social media, using the Social Network Analysis (SNA) method. The objective is to identify key actors and user interaction patterns. The findings show that accounts such as @idextratime and @kopex__ have significant influence in the #STYOut network. Identification of these influential actors can help communication and marketing strategies, as well as decision making processes. This study is also relevant for sports team management in understanding the impact of public reaction on the coach\u27s reputation. The study findings supports the Two-Step Flow Model of Communication theory, which shows the role of opinion leaders and the rapid spread of information. This study also provides insights into social interactions on social media, implications for sports management, and contributions to communication theory in the digital era.Penelitian ini menganalisis penyebaran informasi terkait kontroversi Shin Tae-yong sebagai pelatih Timnas Indonesia di media sosial menggunakan metode Social Network Analysis (SNA). Tujuannya adalah mengidentifikasi aktor kunci dan pola interaksi pengguna. Temuan menunjukkan akun seperti @idextratime dan @kopex__ memiliki pengaruh signifikan dalam jaringan #STYOut. Identifikasi aktor berpengaruh ini dapat membantu strategi komunikasi dan pemasaran, serta pengambilan keputusan. Penelitian ini juga relevan bagi manajemen tim olahraga dalam memahami dampak reaksi publik terhadap reputasi pelatih. Secara teoritis, penelitian ini mendukung Two-Step Flow Model of Communication, menunjukkan peran pemimpin opini dan cepatnya penyebaran informasi. Kajian ini memberikan wawasan tentang interaksi sosial di media sosial, implikasi bagi manajemen olahraga, dan kontribusi terhadap teori komunikasi di era digita

User Acceptance and Effectiveness of AutoML Systems in Predicting Training Success: A Case Study of the DTS Program

The advancement of digital technologies has transformed workforce demands, emphasizing digital literacy, adaptability, and innovation. Indonesia’s Digital Talent Scholarship (DTS) program, launched in 2018, has trained over 500,000 participants in IT skills. However, completion rates vary significantly (57%–96%), highlighting challenges in participant engagement and program effectiveness. This study integrates the DeLone and McLean Information Systems Success Model and the Technology Acceptance Model (TAM) to evaluate system effectiveness and user acceptance. Variables from the DeLone model—System Quality, Information Quality, and Service Quality—were incorporated into TAM constructs, influencing Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and User Satisfaction, which shape Attitude Toward Use (ATU) and Behavioral Intention (BI).Survey data from 342 DTS administrators were analyzed using Structural Equation Modeling (SEM). Results show PEOU significantly influenced PU (path coefficient = 0.900) and ATU (0.594), while PU and ATU collectively explained 70.3% of BI. High PEOU (0.91) and PU (0.87) scores highlight the importance of system usability and utility. However, low ATU stems from organizational misalignment, as the system’s focus on participant quality contrasts with the DTS priority on participant numbers. Addressing this misalignment, enhancing system features, and improving service quality can boost adoption and foster a digitally skilled workforce aligned with Indonesia’s evolving demands