Publikasi Universitas Mercu Buana
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Decision Support System for Video Editing Staff Recruitment Using a Combination of Entropy and Simple Additive Weighting Methods
No full textThe recruitment process for video editing staff is a strategic stage in ensuring the quality of professional and competitive content production. However, candidate assessment often faces challenges of subjectivity and inaccuracy in decision-making when evaluators rely solely on intuitive judgment without a measured approach. This study aims to develop a decision support system based on Multi-Criteria Decision Making (MCDM) by integrating the Entropy method for objective determination of criteria weights and the Simple Additive Weighting (SAW) method in calculating the preference values of alternatives. Five evaluation criteria are used in the selection process, namely Editing, Creativity, Experience, Discipline, and Teamwork, with the final weights obtained through the Entropy method being 0.2867, 0.2248, 0.2573, 0.0685, and 0.1626. The study results show that the SAW method is capable of processing candidate evaluation scores comprehensively based on these weights, producing final scores that indicate the best candidates, namely Eko Firmansyah (0.986), Indra Mahendra (0.9699), and Candra Wijaya (0.9662) as the three candidates with the highest eligibility. This study demonstrates that the integration of the Entropy–SAW method is effective in creating a selection mechanism that is objective, transparent, and scientifically accountable, thus making a significant contribution to decision-making in the field of human resource managemen
The impact of the inclination angle of perforated screen facade on daylight performance in the tropics
No full textDaylighting is one of the fundamental aspects of green building principles. Utilizing daylighting in a building offers numerous benefits, including energy efficiency, enhanced comfort, improved workplace productivity, better health, and increased economic value. However, buildings with glazed facades can experience excessive illuminance, uneven daylight distribution, and glare without proper shading devices. Perforated screen facade (PSF) is one of the shading devices widely used in buildings with glass facades. PSF minimizes direct solar radiation and enhances daylighting performance while preserving outdoor views. This study focused on one design variable of PSF, the inclination angle, which had not been widely explored in previous research within the context of a tropical climate. The research aimed to evaluate the impact of the PSF inclination angle on daylight performance. The research method was experimental, using radiance-based simulation as a tool. The daylight availability and visual comfort of office buildings with vertical PSF were compared with inclined PSF. The daylight performance metrics analyzed included mean illuminance, useful daylight illuminance, and spatial disturbing glare. The results indicated that implementing an inclined PSF resulted in mean illuminance ranging from 1065 to 1105 lx, useful daylight illuminance between 95.08% and 95.55%, and spatial disturbing glare between 5.1% and 6.5%. Increasing the PSF inclination angle raises the mean illuminance and spatial disturbing glare and reduces the useful daylight illuminance. PSF can be applied with an inclination angle to buildings in the tropics, providing broader possibilities for facade design exploration
3D numerical investigation of roadway bridge response under hydrodynamic forces and local scour in stiff clay and sand foundations
No full textBridges are critical components of transportation networks but are highly vulnerable to failure during extreme flood events, particularly due to hydrodynamic forces and local scour. This study quantitatively evaluates the effects of flood velocity and scour depth on bridge pier displacement for two representative soil conditions: very stiff clay (Ground Type B) and medium-dense sand (Ground Type C). A 3D finite-element model incorporating non-linear p–y springs was developed in CSI Bridge to represent soil–structure interaction (SSI). A total of 192 simulations were performed across flood velocities of 2–16 m/s and scour depths ranging from 0DF to 2DF. The results show that pier displacement increases systematically with both velocity and scour, with medium-dense sand exhibiting up to 30% higher displacement than very stiff clay at severe flood conditions (0.07 m vs. 0.06 m). These findings highlight the importance of soil stiffness in governing pier response under extreme hydrodynamic loading. While the study does not address debris impact, flow directionality or additional hydraulic parameters, the outcomes provide valuable insight for improving foundation design and incorporating SSI considerations into flood-resilient bridge engineering
An ultra-broadband microstrip antenna using a triple dumbbell-shaped defected ground structure
No full textMicrostrip antennas are widely used in modern communication systems due to their compact size and low profile. However, they typically suffer from narrow bandwidth, limiting their performance in advanced wireless applications. This study addresses this limitation by employing a triple dumbbell-shaped defected ground structure (DGS). The antenna is designed to operate at 3.5 GHz using a Rogers RT5880 substrate, and its performance was analyzed through simulations in HFSS 15.0 software. Without the DGS, the antenna exhibits a fractional bandwidth (FBW) of only 1.71%, operating from 3.47 GHz to 3.53 GHz. Incorporating the triple dumbbell-shaped DGS in the ground layer increases the FBW significantly to 53.6%, extending the operating frequency range from 2.39 GHz to 4.14 GHz. This improvement was achieved through the careful optimization of DGS parameters. The simulated gain at 3.5 GHz is 5.13 dBi. The proposed design demonstrates superior performance compared to conventional techniques such as split-ring resonators (SRR) and Butler matrix (BM) configurations. Simulation and measurement results show excellent agreement, validating the design. The achieved ultra-wideband performance benefits 5G and next-generation systems, offering greater frequency tolerance, diverse signal support, increased capacity, and reliable operation, making the antenna a promising candidate for future wireless applications.
Real-time deep neural network-based waste detection and classification using a camera sensor
No full textWaste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management
Utilization of teak wood powder waste as eco-friendly filler in HRS-WC asphalt: a comparative analysis of dry and wet Marshall mix methods
No full textWith the increasing demand for road durability driven by rapid economic development, innovative and sustainable approaches are essential to improve the strength and service life of road pavements. This study investigates the use of teak wood powder waste (TWPW) as a cost-effective and environmentally friendly filler material in Hot Rolled Sheet – Wearing Course (HRS-WC) asphalt mixtures. Utilizing bio-waste not only supports circular economic principles but also offers economic benefits by reducing the reliance on conventional and more expensive fillers. The research evaluates various TWPW concentrations (0%, 0.3%, 0.6%, and 0.9%) and their effects on key Marshall test parameters, including stability, flow, Marshall Quotient (MQ), Voids in Mineral Aggregate (VMA), Voids in Mix (VIM), and Voids Filled with Asphalt (VFA). Samples were prepared using both dry and wet methods in accordance with Bina Marga (2018) specifications. The results indicate that the optimum filler content was 0.9% for the dry method (stability: 1042.68 kg) and 0.6% for the wet method (stability: 1161.14 kg). SEM analysis confirmed that filler dispersion significantly influences the internal structure and porosity of the mixture. At 0.3% and 0.6%, the filler was more evenly distributed, leading to improved compaction and mechanical performance. Conversely, agglomeration at 0.9% increased voids and reduced compaction quality. This study demonstrates that TWPW can serve as a viable low-cost filler alternative, maintaining pavement performance while reducing material costs and environmental impact. The findings support the adoption of sustainable waste utilization practices in road construction
Perbandingan Algoritma CART Dan AdaBoost Pada Klasifikasi Demensia
No full textDemensia merupakan gangguan kesehatan ditandai dengan penurunan daya ingat, kemampuan kognitif, dan perilaku yang mengganggu aktivitas pada kehidupan sehari-hari. Masyarakat kurang mendapatkan informasi mengenai deteksi dini demensia yang disebabkan terbatasnya fasilitas kesehatan. Klasifikasi menggunakan data mining dapat membantu deteksi dini demensia. Penelitian ini bertujuan membandingkan algoritma CART dan AdaBoost untuk melihat metode yang paling efektif digunakan pada klasifikasi demensia. Pembagian data dilakukan menggunakan metode percentage split dan k-fold cross-validation. Percentage split membagi data menjadi dua bagian dengan 70% data pelatihan dan 30% data pengujian. K-fold cross-validation mengelompokkan data dengan 1 kelompok data menjadi data pengujian dan 9 kelompok data lainnya menjadi data pengujian yang dilakukan berulang pada setiap kelompok data sebanyak 10 kali. ADASYN digunakan untuk menyeimbangkan data pada setiap kelas. Hasil evaluasi kinerja pada kedua algoritma menunjukkan AdaBoost menggunakan ADASYN dan k-fold cross-validation memiliki nilai tertinggi untuk akurasi, presisi, recall, f1-score, dan ROC-AUC masing-masing sebesar 92.52%, 92.11%, 92.52%, 91.46%, dan 96.85%. Hasil ini menunjukkan bahwa algoritma AdaBoost sangat baik dalam memprediksi seluruh demensia dengan benar, mempertahankan keseimbangan antara presisi dan recall, dan membedakan tiga kelas demensia. Hasil penelitian menunjukkan keunggulan pendekatan ensemble learning dalam menangani variasi data dan meningkatkan stabilitas model klasifikasi demensia. Penelitian ini menunjukkan bahwa AdaBoost memiliki performa yang sangat baik dibandingkan CART pada klasifikasi demensia
An Examination of the Fe₃O₄ nanomaterial impact in conjunction with Magnetorheological Elastomer material
No full textMagnetorheological elastomer (MRE) is an advanced material class that can be used for vibration damping. This material possesses the ability to reduce vibration disturbances through adjustment of its mechanical properties in response to a magnetic field applied from an external source. The objective of this study is to ascertain the effect of incorporating Magnetite (Fe₃O₄) nanomaterials into MRE. It is expected that this new material will be more sensitive to magnetic fields in damping vibrations, which would be a significant improvement. MRE is composed of carbonyl iron powder (CIP), silicone oil, and silicone rubber, with weight proportions of 30%, 5%, and 65%, correspondingly. The addition of magnetite nanomaterials to MRE occurred at weight ratios of 0.5%, 1%, 1.5%, and 2%. Observations of this new material included elemental composition analysis and viscoelastic testing of various mixture formulations in the laboratory. From this research, it can be concluded that an MRE containing Fe₃O₄ nanomaterials has been created. For the attenuation of vibrations within the 1–100 Hz frequency range. MRE-2 (MRE with 0.5% Fe₃O₄ added) is the best choice as the primary material, as it exhibited the highest tan delta value and strong damping performance at an intermediate frequency. MRE-1 sample was used as a base material mixture without added Magnetite also an excellent choice, offering high stiffness and good damping capability at low frequencies. It is shown by the results of this experiment that the effectiveness of MRE in reducing vibration can be increased by adding Magnetite, even in the limited mid-frequency range of 0 to 100 Hz
An effective and efficient vehicle detection using ER-EMA-YOLOv10n
No full textVehicle detection plays a key role in automating traffic analysis, a field that continues to advance rapidly. Vision-based systems identify vehicle types and sizes, but achieving high accuracy and efficiency remains a challenge. Reliable real-world deployment requires optimized models that balance performance and computational cost. YOLOv10n, the most efficient version of the YOLO family, offers a solid foundation for lightweight feature extraction. To improve its detection performance, this study proposes an enhanced version of YOLOv10n by incorporating a scale-aware attention mechanism. We proposed the Expanded Refinement Efficient Multi-Scale Attention (ER-EMA) module, which enhances feature encoding by capturing vehicle characteristics across multiple receptive fields. ER-EMA consists of two core components: the Expanded Converted Inverted Block (ECIB) and the Convolutional Refinement Block (CRB). These components use diverse convolutional kernels to extract and refine multi-frequency spatial features. Integrating ER-EMA into the YOLOv10n framework produces a more compact and accurate detection model. Experimental results show that the proposed model increases mAP@50 by 1%, while reducing the number of parameters by 0.1M and computation by 0.1 GFLOPS on the Vehicle-COCO dataset. On the UA-DETRAC benchmark, it achieves a 4% improvement in mAP@50:95, with a reduction of 0.2M in parameters and 0.4 GFLOPS in computational efficiency—outperforming the original YOLOv10n and prior methods in both performance and computational efficiency
Land cover changes, built-up and vegetation density, and the Urban Heat Island (UHI) phenomenon in Pekanbaru City
No full textPekanbaru city has a high population growth rate and is currently experiencing rapid urbanization, which is driving urban expansion. Urban development alters land cover patterns and reduces environmental quality. The development of residential areas and infrastructure reduces vegetation, affecting Land Surface Temperature (LST) and contributing to the emergence of the Urban Heat Island (UHI) phenomenon. This study aims to analyze changes in land cover, examine the correlation between LST and the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI), and then investigate the UHI phenomenon in Pekanbaru City. The research method is quantitative, using data from Pekanbaru City, an administrative map, and Landsat 8 OLI/TIRS imagery, which were spatially analyzed in ARGIS and QGIS. The novelty is the use of guided classification and maximum likelihood algorithms for land cover classification, which revealed significant changes over the five years from 2018 to 2023 in Pekanbaru City. Over 5 years, land cover in the city of Pekanbaru changed, with water bodies increasing by 23%, palm areas increasing by 5%, built-up areas increasing by 34%, and vegetation increasing by 10%, while bare land decreased by 57%. There are significant changes in built-up and vegetation density. The correlation between land surface temperature and built-up density is positive; however, it is negatively correlated with vegetation density. There is an urban heat island phenomenon in Pekanbaru City, characterized by surface temperatures exceeding the UHI threshold.