Padang State Polytechnics

An integrated two-step co-pyrolysis (ITSC) process was developed to valorize heterogeneous municipal solid waste (MC-MSW) co-processed with Indonesian brown coal (BC), employing unmodified natural mineral catalysts (kaolin, dolomite, zeolite) for vapor-phase upgrading to improve bio-oil properties. The MC-MSW: BC blend (4:1, w/w) underwent pyrolysis at 550 °C; resulting vapors (paraffins, olefins, aromatics) were catalytically upgraded and analyzed using GC–MS and standardized ASTM protocols (ASTM D445 for viscosity, ASTM D4052 for density, ASTM D92 for flash point, ASTM D5865 for calorific value). Kaolin produced the highest liquid yield (44.0 wt

Machine learning methods enable industry professionals in the metallurgy sector to rapidly, accurately, cost-effectively, and environmentally responsibly design the mechanical properties of low-alloy steel to meet specific application needs. However, the success of these methods highly relies on data quality and a substantial amount of data. In this study, we evaluate the use of synthetic data in predicting the mechanical properties of low-alloy steel using six machine learning algorithms. Synthetic data is generated using the Mostly AI synthetic data platform, which includes chemical composition, temperature, and the mechanical properties of low-alloy steel. Each model is trained with three different training dataset combinations: (1) using synthetic data, (2) using only experimental data, and (3) using a combined dataset that integrates synthetic and experimental data. Each model is evaluated with three evaluation metrics: MAE, RMSE, and R-squared. The research findings demonstrate that employing a combined dataset with the Decision Tree (DT) algorithm yields significantly better performance compared to models using only experimental or synthetic data. DT achieves an MAE of 8.87, RMSE of 20.38, and an R-squared of 0.98 for YS prediction. For TS, DT attains an MAE of 9.06, RMSE of 20.96, and an R-squared of 0.97. These findings suggest that utilizing synthetic data in modeling the prediction of low-alloy steel’s mechanical properties through machine learning methods can substantially enhance model performance.

This study implements a low-cost Software Defined Radio (SDR)-based receiver system for the Automatic Identification System (AIS) operating at frequencies of 161.975 MHz and 162.025 MHz using a 44 cm V-dipole antenna with a 90° angle. Spectrum analysis is performed using Airspy to identify AIS signal characteristics in real time within the frequency domain, with measured Signal-to-Noise Ratio (SNR) values of 21.3 dB for AIS 1 and 21.4 dB for AIS 2. The signal decoding process is carried out by AIS-catcher, which handles GMSK demodulation and efficiently extracts AIS message payloads. The decoded data is then visualized using OpenCPN in the form of a digital map to monitor vessel positions and movements in real time. SDRangel is utilized as a supporting platform for signal observation, SDR device configuration, and additional analysis of reception quality. System performance evaluation demonstrates stable capability in receiving and decoding AIS signals with a satisfactory success rate, although it is still affected by interference and propagation conditions. GNU Radio is used in a limited capacity as a signal processing environment for filtering and basic parameter adjustment. The results confirm that low-cost SDR is an effective, flexible, and economical solution for implementing AIS monitoring systems at the research and basic application levels.

The development of digital technology has driven the emergence of various innovations capable of supporting business activities, one of which is Artificial Intelligence (AI). AI technology enables business actors to process data quickly, produce accurate information, and provide recommendations that can be used as a basis for business decision-making. For Micro, Small, and Medium Enterprises (MSMEs), the use of AI is becoming increasingly important because it can help overcome resource limitations, improve operational efficiency, and strengthen competitiveness amidst increasingly dynamic business competition. This study aims to analyze the effect of the use of Artificial Intelligence on business decision-making in MSMEs. This study uses a quantitative approach with a survey method. Data were obtained by distributing questionnaires to MSMEs that have utilized digital technology in their business activities. The sampling technique used purposive sampling with the criteria for respondents being MSME owners or managers who have used Artificial Intelligence-based applications or systems. Data analysis was conducted using descriptive statistics, validity tests, reliability tests, and simple linear regression analysis to examine the effect of AI use on business decision-making. The results show that the use of Artificial Intelligence has a positive and significant effect on business decision-making in MSMEs. The use of AI has been proven to increase the speed of access to data analysis information, the ability to predict market conditions, and the effectiveness of business strategy development. Furthermore, AI helps MSMEs understand consumer needs, identify business opportunities, and reduce the risk of errors in decision-making. AI's ability to provide real-time information also enables businesses to respond to changes in the business environment more quickly and accurately.

As workforce diversity increases in the globalization era, vocational education plays a crucial role in preparing students to be competent and industry-ready, with English serving as a key medium for professional integration. English as a Lingua Franca (ELF) has emerged as a flexible mode of communication, facilitating interaction not only between native and non-native speakers but also among non-native speakers from varied linguistic backgrounds. This study examines the perceptions of Indonesian Vocational Higher Education (VHE) students regarding the role and relevance of ELF in meeting global workplace communication demands. Participants consisted of sixty-nine sixth-semester English majors who had completed two- to three-month internships in various industries. Data were collected through a five-point Likert-scale questionnaire and semi-structured interviews, enabling both quantitative and qualitative insights. Statistical analysis using a t-test, following the Shapiro-Wilk normality test, revealed mean differences ranging from M = 2.95 (Q10) to M = 4.28 (Q14). These findings were supported by interview data, indicating that most participants viewed ELF as more suitable for workplace communication than traditional English as a Foreign Language (EFL) norm. The study highlights the need to align English Language Teaching (ELT) in vocational education with ELF-oriented principles to enhance communicative effectiveness in global professional settings.