Publikasi Universitas Mercu Buana
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Optimization of CNC Turning Parameters for Surface Roughness of Brass 36000 Using the Taguchi Method
No full textBrass is widely used in industrial applications due to its excellent machinability and durability, making it well suited for CNC turning operations. Although numerous studies have investigated the optimization of turning parameters, variations in machine tools and cutting conditions often lead to differing conclusions. This study aims to optimize surface roughness in the CNC turning of Brass 36000 using the Taguchi method. An L9 orthogonal array was employed to evaluate the effects of spindle speed, feed rate, depth of cut, and coolant type. Experimental data were analyzed using signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) to identify the most influential parameters and optimal cutting conditions. The results indicate that feed rate is the dominant factor affecting surface roughness, contributing 95.54% of the total variation, followed by spindle speed (1.88%), depth of cut (0.33%), and coolant type (0.18%). The optimal machining parameters were determined as a spindle speed of 1700 rpm, feed rate of 0.1 mm/rev, depth of cut of 1.0 mm, and the use of synthetic coolant (GT41), resulting in a minimum surface roughness of 0.67 µm. These findings demonstrate that precise control of feed rate is critical for achieving improved surface quality in CNC turning of brass
Perbandingan Algoritma K-Means dan Hierarchical Clustering dalam Pengelompokan Prestasi Akademik Siswa
No full textPengelompokan prestasi akademik merupakan salah satu strategi yang dapat membantu guru dan pihak sekolah dalam melakukan intervensi pembelajaran, seperti pemberian bimbingan tambahan atau penentuan strategi pengajaran yang lebih tepat. Penelitian ini bertujuan untuk membandingkan kinerja algoritma K-Means dan Hierarchical Clustering dalam mengelompokkan prestasi akademik siswa. Dataset yang digunakan terdiri dari 1.000 data siswa dengan tiga atribut nilai, yaitu Matematika, Membaca, dan Menulis, yang diperoleh dari sumber data publik. Tahapan penelitian meliputi proses preprocessing, normalisasi data menggunakan StandardScaler, penerapan algoritma clustering, serta visualisasi hasil menggunakan scatter plot dua dimensi dan dendrogram. Evaluasi kinerja model dilakukan menggunakan Silhouette Score untuk menilai kualitas pemisahan cluster. Hasil penelitian menunjukkan bahwa algoritma K-Means memperoleh skor Silhouette sebesar 0,406, sedangkan Hierarchical Clustering memperoleh skor 0,374. Nilai tersebut mengindikasikan bahwa K-Means menghasilkan struktur pengelompokan yang lebih baik dan lebih jelas dalam membedakan tingkat prestasi siswa menjadi tiga kategori: rendah, sedang, dan tinggi. Dengan demikian, K-Means dinilai lebih sesuai untuk analisis pengelompokan prestasi akademik pada dataset ini
Understanding the Dual Impact of Job Conflict and Work Stress on Organizational Performance Outcomes
No full textObjectives: Organizational changes in both internal and external environments can significantly affect employee productivity, particularly when human resources are unable to adapt effectively. This often results in workplace conflict and elevated job stress levels. This study aims to examine the impact of work conflict and job stress on employee performance at PT. Scudetto Prima Transportasi.Methodology: The research employs a descriptive-verificative method with a quantitative approach, utilizing non-probability sampling techniques on the entire employee population (75 individuals). Data were analyzed using SPSS Statistics 20.Findings: Furthermore, partial tests reveal that both independent variables independently exert a negative and significant influence on performance. These findings underscore the critical importance of effective conflict and stress management strategies in enhancing organizational productivity and employee effectiveness.Conclusion: The results indicate that work conflict and job stress have a simultaneous negative and significant effect on employee performance, accounting for 17.9% of the variance
Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches
No full textMachine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision.
Analysis of Service Quality and Coverage Based on LoRaWAN Network: A Case Study of Air Quality Monitoring in Grand Depok City
No full textSmart living is an evolution of the Internet of Things (IoT). The concept of smart living itself refers to the ability to control and connect everything in the surrounding environment to the Internet. The research method used in this study is observational, focusing on the range of LoRa usage with parameters including Receive Signal Strength Indicator (RSSI) and Signal to Noise Ratio (SNR). The study results show that the average RSSI is -94.6 dBm at 50 meters, -105.4 dBm at 100 meters, -106.8 dBm at 150 meters, -111 dBm at 200 meters, -106.6 dBm at 250 meters, and -108.8 dBm at 300 meters. Meanwhile, the highest SNR value was recorded at 50 meters with 11 dB, while the lowest was at 300 meters with -8 dB. The highest error rate was observed at 250 meters, with an error value of 88%. Lack of error value indicates that as the distance between the transmitter and receiver increases, the signal's strength and quality tend to decrease, ultimately affecting communication reliability. Noise becomes more dominant at greater distances than the received signal
Penerapan Aplikasi Rekomendasi Konten Akun Instagram Photographer Menggunakan Collaborative Filtering
No full textPerkembangan media sosial, khususnya Instagram, telah membuka peluang besar bagi individu dan komunitas untuk membagikan konten visual. Tantangan yang muncul adalah bagaimana menyajikan konten yang sesuai dengan preferensi audiens. Penelitian ini bertujuan untuk mengimplementasikan sistem rekomendasi konten berbasis metode Item-Based Collaborative filtering pada akun Instagram “Photographer Dadakan”. Sistem ini menganalisis interaksi pengguna berupa likes dan komentar terhadap konten sebelumnya, kemudian menghitung kemiripan antar konten menggunakan Cosine Similarity. Prediksi minat pengguna dilakukan dengan metode Weighted Sum, dan rekomendasi utama ditentukan melalui perhitungan Global Score. Hasil penelitian menunjukkan bahwa sistem dapat memberikan rekomendasi konten yang lebih relevan sehingga mendukung pengelolaan konten berbasis data pada platform media sosial visual
Real-time Unmanned Surface Robot (USR) for river quality monitoring systemm
No full textA real-time Unmanned Surface Robot (USR) for river water quality monitoring system is a technology that employs a small autonomous boat outfitted with sensors and other monitoring equipment to gather and transmit data on various water quality parameters like pH, temperature and total dissolved solids sensors in rivers and other bodies of water. The USR can traverse the river, gather information or data at specific points or designated locations, as well as continuously monitor a specific stretch of river at all times. The data or information was sent in real time to a central monitoring station, where it was analyzed and used to identify potential water quality problems. Initially, the USR was designed using SolidWorks software, and its structural performance was the main focus of the investigation and examination of the design. This USR was then created and manufactured. The entire USR system could help detect and mitigate pollution and other environmental problems, as well as offer useful information for managing water resources. Next, to determine the overall performance of the USR, five experiments and autopilot accuracy tests were performed. Finally, this study also verified and validated the accuracy of water quality monitoring sensors.
A predictive safety and maintenance framework for railway locomotives: integrating HAZOP, FMEA, and IoT-based risk mitigation
No full textSafety and maintenance efficiency are critical challenges in the railway industry, particularly in the use of lifting jacks for locomotive maintenance. This study proposes a predictive maintenance framework that integrates the Hazard and Operability Study (HAZOP), Failure Mode and Effects Analysis (FMEA), and Internet of Things (IoT) technology to detect potential failures in real time. A case study was conducted at a locomotive maintenance depot in Indonesia, where several occupational accidents had been recorded due to lifting jack malfunctions. Based on HAZOP and FMEA analyses components such as stoppers and drive motors were identified as having high Risk Priority Numbers (RPN), each reaching 512, indicating significant failure risks. The proposed IoT system employs HCSR-04 and MPU6050 sensors to accurately monitor the height and inclination of the equipment. Evaluation results show that the system effectively detects anomalies with minimal data deviation and a low data loss rate during a 10-day testing period. The implementation of this system significantly reduces workplace accident risks, improves maintenance efficiency, and supports digital transformation within the industrial environment. These findings demonstrate that the integration of HAZOP, FMEA, and IoT is effective for risk mitigation and can be replicated in other railway components. Moreover, this research opens new avenues for developing AI-based predictive systems and implementing digital twins as part of future smart maintenance strategies
Determination of critical factors and the best alternatives for developing biodiesel from Maggot BSF
No full textThis paper explores the approach for producing biodiesel from Maggot Black Soldier Fly (BSF) as a sustainable renewable energy source in Indonesia. The SWOT and VIKOR techniques determine the most effective strategy for promoting renewable energy in Indonesia. The paper included numerous respondents to ascertain the criteria and assess each option. Environmental consciousness is an important strong component in biodiesel development, with a value of 1.52. A significant drawback in biodiesel production is the elevated investment costs, quantified at 1.48. A notable opportunity in biodiesel development is its potential as an environmentally sustainable energy alternative, scoring 1.32, while a considerable threat is inadequate financial assistance, scoring 1.24. Moreover, applying the VIKOR approach reveals that alternative 6 (Enhancing collaboration among stakeholders) is the most critical option, as expert evaluations indicate, with a value of 0.048. The outcomes of this study require enhancement since additional research is necessary to yield more precise findings that will augment our comprehension of the evolution of renewable energy in Indonesia. Future studies should focus on the ramifications of producing biodiesel from BSF maggots, particularly in terms of energy security and energy autonomy in Indonesia.
Effect of Different Cleanness of the Pre-Coat Surface on Adhesion and Corrosion Performance of A36 Steel with Epoxy Coating
No full textAdhesiveness and protection against corrosion are the main properties of epoxy coating, especially if the coating is applied to materials exposed to harsh environments, such as water containing chloride. However, the effectiveness of this coating also really depends on the painting process, especially the cleanliness of the substrate surface prior to coating application. Choosing the right surface preparation can optimize the coating’s capabilities. This research aims to evaluate the effect of the cleanness of the blasting process on the coating performance on the surface A36 steel. The blasting pressure on garnet blasting is 8 bar utilizing mesh 30-40 garnet, and the spraying angle is 90° with a distance of 30 cm. The spraying time is at least 5 minutes to the desired NACE standard. Painting is done at room temperature using the airless spray method with an angle of 90°, a distance of 25 cm, with a movement speed of 300mm/s with a gap of 24 hours between the first and second layers. The results showed that surface preparation influenced the paint layer thickness, where NACE 4 provided the highest thickness in the first layer. In contrast, NACE 1 yielded the highest thickness in the second layer. The pullout strength test demonstrated that NACE 2 provided the highest pullout strength, followed by NACE 3 and NACE 1, while NACE 4 resulted in the lowest adhesion strength. Likewise, corrosion rate testing showed that surface preparation affects the corrosion rate, with NACE 1 providing lowest corrosion rate