Publikasi Universitas Mercu Buana
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PENINGKATAN KAPASITAS TATA KELOLA, PEMBUATAN MEDIA PENGAJARAN DAN APLIKASI BERGERAK UNTUK GURU SANGGAR BELAJAR MUHAMMADIYAH KUALA LUMPUR
No full textPaper ini mendiskusikan pelaksanaan pengabdian masyarakat untuk anak-anak Pekerja Migran Indonesia (PMI), khususnya di sanggar belajar Muhammadiyah Kuala Lumpur. Aktifitas pengabdian meliputi workshop pengelolaan pendidikan non formal, pembuatan media pengajaran menggunakan canva, pembuatan aplikasi bergerak dan bijak menggunakan handphone. Tujuan dari pengabdian ini adalah meingkatkan pemahaman dan kesadadaran dalam pengelolaan lembaga pendidikan serta meningkatkan inovasi pengajaran sehingga proses pembelajaran lebih efektif dan menarik. Dari hasil evaluasi menunjukkan bahwa 100% para guru meraasakan ketiga topik pelatihan dibutuhkan. Untuk dua topik tata kelola dan Canva, 100% peserta memahami dengan baik. Sementara untuk topik pembuatan aplikasi bergerak hanya 60% yang memahami materi yang diberikan. Pengabdian ini sangat diperlukan oleh pengelola dan guru di lembaga pendidikan tersebut serta bermanfaat dalam banyak aspek, baik dari sisi kesadaran tata kelola yang professional maupun memantik kreatifitas para guru
Transformasi Digital UMKM Thrift : Strategi Pemasaran Efektif untuk Meningkatkan Penjualan Pasca Pandemi
Get PDFPenelitian ini bertujuan untuk menganalisis efektivitas strategi pemasaran digital dalam meningkatkan penjualan baju thrift pasca pandemi COVID-19. Metode yang digunakan adalah studi literatur, observasi, dan wawancara mendalam terhadap pelaku usaha dan konsumen. Hasil penelitian menunjukkan bahwa pemanfaatan media sosial, marketplace, dan iklan berbayar secara signifikan mampu meningkatkan visibilitas dan penjualan. Strategi pemasaran digital menjadi lebih optimal jika didukung pemahaman digital marketing funnel, adaptasi konten, dan analisis perilaku konsumen. Namun, pelaku usaha masih menghadapi tantangan seperti persaingan yang ketat, keterbatasan literasi digital, dan isu kepercayaan konsumen. Oleh karena itu, pelatihan berkelanjutan serta dukungan kebijakan pemerintah diperlukan untuk memperkuat daya saing UMKM thrift di era digital
An intelligent approach for detection and classification of security attacks in a Passive Optical Network using Light Gradient Boosting Machine
No full textOver the past decade, Passive Optical Networks (PONs) have emerged as a leading solution for next-generation broadband access, providing high-speed and cost-effective communication. However, PONs face significant security challenges, including data interception, denial-of-service (DoS) attacks, and resource exhaustion caused by malicious Optical Network Units (ONUs). Machine learning (ML), particularly advanced models like Light Gradient Boosting Machine (LightGBM), has proven to be a promising solution for managing complex security issues in PONs. Leveraging its ability to handle imbalanced, high-dimensional datasets, LightGBM was employed in this study to detect and classify malicious ONUs based on bandwidth usage patterns. The model achieved an impressive accuracy of 95.27%, a Matthews Correlation Coefficient (MCC) of 90%, and a precision rate of 93%. While traditional classifiers, such as Naïve Bayes (NB), achieved an accuracy of 88.53%, LightGBM demonstrated superior robustness in addressing class imbalance and enhancing detection accuracy. This work highlights the potential of LightGBM in enhancing PON security and enabling intelligent, resilient broadband networks
Assessment of revetment performance against wave overtopping for mitigating tidal flooding at Lebih Beach
No full textAs one of the largest archipelagic nations, Indonesia faces significant coastal erosion challenges, particularly in Gianyar Regency, Bali, where coastline change rates have reached -11.12 m/year. To combat this issue, the Indonesian government has implemented revetment structures along the coastline, notably at Lebih Beach. This research systematically assesses the current performance of a coastal revetment structure on Lebih Beach, focusing on its ability to withstand modern wave conditions and prevent wave overtopping. The objective is to evaluate the structure’s physical integrity and functionality, especially as wave overtopping has impacted nearby communities and damaged infrastructure. The methodological framework incorporates detailed field surveys to document structural conditions and detect signs of erosion, material degradation, or damage. Topographic and bathymetric data are used to model the coastal and seabed profile, which is essential for simulating wave behavior. Wind, tide, and wave data from CMS-Wave in SMS 10.1 software provide insights into wave height, direction, and energy, helping predict wave impacts on each segment of the coastline. The research area is divided into six segments along the Lebih Beach coastline. Initial evaluations showed that segments 1 through 4 require further analysis due to evident vulnerabilities to wave forces. The reexamination compares the peak elevation of these segments, specifically their ability to withstand wave action at the established elevation of +5.00 m. This comparison allows for an accurate assessment of the structure’s resilience under current environmental pressures and guides recommendations for maintenance or reinforcement where needed. The evaluation results in segments 1, 2, 3, and 4 showed that the revetment still undergoes overtopping. Continuous monitoring and evaluation of coastal protection structures is needed to ensure the integrity of coastal communities and infrastructure in the face of ongoing environmental changes
Optimizing intrusion detection with data balancing and feature selection techniques
No full textThe rapid growth of IoT devices has brought significant security challenges, particularly in detecting various types of attacks within heterogeneous network environments. This study explores the effectiveness of data balancing techniques, including Random Undersampling (RUS), Cost-Sensitive Learning (CSL), Synthetic Minority Oversampling Technique (SMOTE), and Randomized Combination Sampling (RCS). Feature selection methods, namely correlation (threshold 0.8) and mutual information (top 15 features), were employed to optimize feature sets. The Decision Tree (DT) and Linear Discriminant Analysis (LDA) classifiers were used to evaluate the performance of balanced datasets. The evaluation metrics included accuracy, precision, recall, F1-score, G-mean, and ROC curves. The results revealed that SMOTE and RCS outperformed other balancing methods, with SMOTE achieving the highest accuracy (98.7%) and RCS demonstrating robust G-mean values across both feature selection techniques. DT consistently showed better performance compared to LDA across all metrics, while feature selection significantly improved the classification results, particularly under mutual information criteria. However, the analysis highlighted limitations of LDA in handling imbalanced datasets and high-dimensional features. This study concludes that a combination of advanced data balancing and effective feature selection significantly enhances the accuracy of intrusion detection in IoT networks. Future work will focus on integrating real-time detection systems and exploring hybrid models to further improve the detection of complex attacks in dynamic IoT environments.
Optimizing PSO for classification: comparison of Naïve Bayes and C4.5 for osteoporosis prediction
No full textOsteoporosis is a medical disease marked by a reduction in bone density, which significantly increases the risk of fractures. Osteoporosis patients do not always exhibit symptoms and because current diagnostic techniques have limitations, early detection is frequently needed. The osteoporosis dataset consists of 1.958 records each containing 15 regular attributes and 1 special attribute as the label. The attribute represented as “1” for the presence of osteoporosis and “0” for its absence. The primary objective is to predict an individual’s risk of developing osteoporosis, including age, gender, bone density, lifestyle factor, medical history, and nutritional intake of calcium and vitamin D. To achieve this, Naïve Bayes and C4.5 has been employed. PSO is employed to identify the most relevant features, thereby optimizing the efficiency and accuracy of the classification models. The initial step in data preprocessing involved handling missing values to ensure data integrity. After implementing PSO, Naïve bayes improved from 82,65% to 83,67%, while C4.5 exhibited an even greater increase, rising from 91,07% to 96,17%. PSO significantly optimizes model, with the most improvement in C4.5. PSO proves to be a valuable tool for feature selection. Age and Hormonal Change emerged as important for both models. Furthermore, Physical Activity and Calcium Intake, which despite having varying levels of influence, were consistently considered relevant. By focusing on these significant attributes, enables us more effectively monitor and recognize early signs of osteoporosis. Identifying individuals at high risk, more effective early detection and intervention, improving the potential for timely management and prevention
Design and Concept Selection of a Modular EV Conversion Kit for Automatic Motorcycles with the Bahana Nusantara Hijau Theme for Mobility in Indonesia
No full textUrban air pollution, primarily driven by emissions from conventional motorcycles, remains a critical environmental and public health issue in Indonesia. Although the government has introduced various incentive programs, the nationwide adoption of electric motorcycles (EMs) is still limited due to challenges such as inadequate battery range, long charging times, and high initial investment. This research introduces a modular electric vehicle (EV) conversion kit engineered for existing internal combustion engine motorcycles (ICEm), offering a hybrid propulsion system that enables seamless switching between electric drive and fuel-based operation. The development process employs Ulrich and Eppinger’s product development methodology, systematically progressing through concept generation, concept selection, and 3D modeling stages. A total of 72 design alternatives were generated using black-box and transparent-box modeling, internal-external searches, and morphological analysis. Through classification trees and multivoting techniques, the options were refined to 12 concepts and evaluated using a weighted scoring matrix. Concept code 34 was selected based on criteria such as performance, manufacturability, cost-effectiveness, and user ergonomics. The final 3D model, created using Autodesk Inventor 2025, integrates a culturally inspired aesthetic—Bahana Nusantara Hijau—to reinforce local identity. This modular kit offers a viable, low-barrier solution to accelerate EM adoption and promote sustainable mobility in Indonesia
Implementasi Metode Six sigma DMAIC untuk Mengurangi Defect Produk Vaksin Kering Beku di Perusahaan Vaksin Hewan
No full textPerusahaan Vaksin Hewan ialah sebuah perusahaan yang bergerak di bidang kesehatan hewan dan memproduksi vaksin hewan dalam rangka memenuhi permintaan para peternak yang ada di dalam dan luar negeri. Salah satu produk yang diproduksi perusahaan ini adalah vaksin kering beku. Dalam proses produksinya masih terdapat beberapa permasalahan kualitas yang terjadi seperti non vacuum, volume, basah, kristal dan lain-lain. Oleh sebab itu, dilakukanlah penelitian untuk menangani permasalahan kualitas tersebut menggunakan metode Six sigma DMAIC yang diharapkan dapat mengurangi defect pada produk vaksin kering beku tersebut. Hasil dari penelitian ini menunjukkan bahwa defect produk yang dominan terjadi adalah defect basah yang memiliki persentase dengan bobot 51%.Setelah itu, dilakukan analisis menggunakan fishbone diagram dan FMEA yang menunjukkan bahwa mode kegagalan dengan skor RPN tertinggi adalah pekerja yang kurang berkompeten.Oleh karena itu, perbaikan proses dilakukan dengan training terhadap karyawan di area produksi dengan rentang waktu 3 bulan sekali dan atau disesuaikandengan kepentingan serta kebutuhan perusahaan. Penerapan dari perbaikan kualitas ini menghasilkan penurunan nilai DPMO yang awalnya 317,38 menjadi 202,53 dan menaikkan nilai sigma yang awalnya 4,92 menjadi 5,0
UPS With Half-Bridge Converter Based On Type-2 Fuzzy Logic Controller
No full textElectricity is one of the most important needs. There are many electronic devices that support and facilitate human. Electrical disturbances are an inseparable part of the existence of electrical energy and we often encounter power outages with very fast and sudden time lags. The sudden disconnection of the power source can cause various losses, one of which is damage to the electronic equipment used or the loss of important data. Losses resulting from an abrupt power source failure can be effectively managed and reduced by incorporating a UPS (Uninterruptible Power Supply) unit between the power source and electronic devices. The converter and method used in this journal is the Half-Bridge Converter with the Type 2 Fuzzy Logic Controller method which functions to control the output of the Converter to be stable. The PLN source or input will be rectified with the Rectifier and lowered by the Half-Bridge Converter which will later be used to charge the battery. The output of the Half-Bridge Converter will be controlled using the Type 2 Fuzzy Logic Controller method. When the PLN source is still there, the battery will be in a charging position, and after the PLN source is lost or extinguished, the static switch will change the load source from the PLN source to a battery source. The output from the battery will be forwarded to the inverter and directly to the load. In this journal, the results of each simulation carried out are appropriate and close to the value of the plan. From the tests that carried out with Fuzzy Type-2, the average voltage value was 13.802V with an average error value of 0.055%, which is close to the planning and the error obtained is relatively small
Perancangan Return to Home Robot Pada Sistem Indoor Menggunakan Radio Frequency Identification dan Line Follower
No full textLine follower robot adalah salah satu robot yang dapat diimplementasikan pada proses distribusi barang. Dengan menambahkan Tag dan reader RFID membantu line follower robot bekerja secara optimal menjadi salah satu robot yang dapat diimplementasikan menjadi robot indoor yang dapat diterapkan untuk warehouse robot. Return to home robot merupakan sebuah robot sistem indoor berbasis line follower yang di lengkapi dengan RFID. Line Follower Robot akan menggunakan 2 buah modul reader RFID. Selain menggunakan sensor infrared untuk mendeteksi line, peniliti juga memanfaatkan sinyal dari modul reader RFID yang berada di bagian bawah robot untuk mendeteksi Tag RFID yang berada di percabangan jalan yang bertujuan untuk membantu robot melewati jalur yang benar. Sedangkan, modul reader RFID pada bagian atas robot akan digunakan untuk menerima perintah dari Tag RFID yang digunakan oleh operator menuju pos yang telah ditentukan. Berdasarkan hasil pengujian, sinyal dari Tag RFID yang berada pada percabangan jalan dapat membantu robot memilih jalur yang benar dan Tag RFID dapat digunakan untuk memberikan perintah robot menuju pos yang telah di tentukan pengguna. Pengaruh tegangan terhadap kecepatan motor DC menggunakan nilai Inputan minimum 140, dengan nilai maksimum yang digunakan 225. Tegangan output minimum yang digunakan untuk menggerakan robot sebesar 3,4V. Sensor infrared sebagai masukkan control untuk pengendali aktuator robot atau roda robot, memberikan Nilai biner = 0 saat sensor mendeteksi line berwarna putih dan Nilai biner = 1 saat sensor mendeteksi line berwarna hitam. Kata Kunci— Warehouse robot, Line follower, RFID, Sensor infrare