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Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

Traditional arts in Indonesia face a growing threat not from physical displacement, but from algorithmic distortion. This study examines how social media algorithms shape and distort public perception of Indonesian traditional arts — specifically jaranan and barongan — through the lens of Van Dijck's (2013) popularity principle: the principle that content distribution is determined by interaction volume rather than cultural value. Employing qualitative thematic content analysis, this study analyzes comments on five viral social media videos sourced from YouTube and TikTok, each depicting riots or tension at traditional art performances. Findings demonstrate that the popularity principle operates consistently and cumulatively across all five samples: algorithmically provocative titles, emotional polarization in comment sections, and micro-behavioral signals such as watch duration and replay collectively function as interaction signals that drive the platform to distribute riot content far more widely than culturally substantive footage. The consequences extend beyond perception: event organizers suffer long-term reputational and economic damage, while collective stereotypes — associating jaranan with violence and disorder — become sufficiently entrenched to surface spontaneously in unrelated contexts. A comparative analysis of a Kangen Band concert video reveals that these stereotypes have already achieved the status of cultural reference points. This study argues that strategic resistance is possible: the same algorithmic logic that amplifies negative content can be deployed to circulate culturally rich content, provided that artists, communities, and government commit to producing content designed to generate high-quality interaction. The challenge is to transform social media from a distorting mirror into an instrument of cultural preservation.

This study aims to examine the relationship between Corporate Social Responsibility (CSR) disclosure and profitability with firm value. The research was conducted on companies in the oil, gas, and coal sub-sector listed on the Indonesia Stock Exchange (IDX) during the 2021–2024 period. The independent variables in this study are corporate social responsibility disclosure and profitability, while firm size is employed as a control variable. Firm value is proxied by Price to Book Value (PBV), whereas profitability is measured using Return on Equity (ROE). This study is grounded in Stakeholder Theory and Signaling Theory to explain the relationships among the variables. The sample was determined using purposive sampling, resulting in 29 companies. The data analysis techniques applied include Pearson correlation analysis and multiple linear regression to examine both the simple relationships and the effects of corporate social responsibility disclosure and profitability on firm value. The results indicate that corporate social responsibility disclosure has a negative relationship with firm value, while profitability shows a positive and significant relationship with firm value.

Increasing traffic activity at unsignalized intersections often results in congestion and delays. The intersection of Jl. Imam Bonjol and Jl. Daya Nasional experiences these conditions, making performance evaluation necessary. This research tends to scrutinize the intersection performance under existing conditions and five-year projected conditions and to identify appropriate improvement measures. The analysis was carried out using the 2023 Indonesian Road Capacity Guidelines (PKJI 2023) and PTV VISSIM microsimulation software. Data on intersection geometry, vehicle speed, and traffic volume were collected through CCTV observations on weekdays and weekends. The results show that under existing conditions, the degree of saturation reached 1.170 with an average delay of 33.75 seconds (LOS D). In the five-year projection, the degree of saturation increased to 1.239 with an average delay of 53.69 seconds (LOS E). These findings indicate a decline in intersection performance, highlighting the need for alternative traffic management measures to improve operational performance and service levels.    

The COVID-19 pandemic has increased public interest in investing, especially in the banking sector, which is known for its stability. However, many investors still lack an understanding of fundamental analysis. This study aims to examine the effect of Return on Asset (ROA), Return on Equity (ROE), and Net Profit Margin (NPM) on stock prices of banking companies listed on the Indonesia Stock Exchange during the 2011–2023 period. The research used a quantitative approach with purposive sampling and multiple linear regression analysis using SPSS. The results show that ROA has no significant effect on stock prices. In contrast, ROE has a significant negative effect, while NPM has a significant positive effect on stock prices. These findings indicate that investors tend to consider net profit margins more than asset efficiency, and that high ROE may be perceived as a signal of high leverage risk. This research is expected to provide insights for investors in assessing banking performance before making investment decisions.

In 2024, Indonesia’s food and beverage industry recorded IDR 110.57 trillion in investment and achieved GDP growth above the national average, highlighting its rapid development and importance for the economy. However, this growth does not automatically increase firm value, as internal factors such as solvency, firm growth, and dividend policy may play a crucial role. This study investigates how those factors affect firm value in food and beverage companies listed on IDX during 2021–2024. The research population comprises 84 companies, with purposive sampling resulting in 47 observations from 13 firms over four years. Data were collected from annual reports and analyzed applying multiple linear regression with SPSS 26. The results show that solvency and dividend policy don’t significantly affect firm value, while firm growth has a significant positive impact. Simultaneously, all three variables positively influence firm value with an adjusted R² of 11.8%. The paper enriches the academic discussion by validating the applicability of signaling theory in showing that firm growth acts as a stronger signal compared to solvency or dividend policy in the food and beverage industry, offering useful implications for investors and managers.

Shaping central banking for sustainability has become increasingly relevant as climate change and the pursuit of sustainable development challenge the conventional scope of monetary policy. Green monetary policy reflects efforts to align central banking with environmental and economic objectives, yet the scholarly literature on this issue remains fragmented. This study employs a bibliometric approach using R Studio (Bibliometrix) to analyze publications indexed in Scopus from 2015 to 2025. The dataset comprises more than 1,200 documents with an annual growth rate of nearly 12%, signaling the rapid expansion of research in this field. Bibliometric techniques, including citation mapping, co-authorship analysis, and keyword co-occurrence, are applied to identify influential authors, sources, and thematic clusters. The results indicate a steady increase in international collaboration and a consolidation of research themes, reflecting the growing importance of sustainability in central banking discourse. This study is expected to contribute by providing a structured overview of the intellectual landscape of green monetary policy, clarifying its links with sustainable development and climate change, and offering guidance for future research and policy innovation in sustainable central banking.

This study aims to analyze the effect of profitability, leverage, and liquidity on firm value in food and beverage sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. Profitability is measured by Return On Assets (ROA), leverage by Debt to Equity Ratio (DER), and liquidity by Current Ratio (CR), while firm value is proxied by Price to Book Value (PBV). The study employs a quantitative approach using multiple linear regression analysis. The sample consists of 25 companies selected through purposive sampling, with a total of 125 secondary data observations obtained from annual financial statements. The results indicate that, partially, profitability, financial risk, and liquidity have a positive and significant effect on firm value. Simultaneously, the three independent variables also significantly affect firm value, with an adjusted R² of 43.4%, meaning that 56.6% of the variation in firm value is explained by other factors outside the model. These findings support agency theory and signaling theory, which suggest that strong financial performance, optimal debt management, and adequate liquidity provide positive signals to investors, thereby enhancing firm value.

The 17+8 demands voiced by the public reflect the accumulation of disillusionment with various government policies and violations committed, especially by the House of Representatives (DPR). Policies that are considered unfair and detrimental to the public trigger a strong push to carry out reforms that are more democratic and oriented towards the interests of the people. Various factors such as human rights violations, lack of transparency and accountability, economic inequality, and polemics related to official allowances are the main triggers for the birth of these demands. The demands of 17+8 not only contain burdens or criticisms, but also contain short-term and long-term goals that can be the basis for improving governance. This momentum should be seen as a strategic opportunity to strengthen democracy, improve state-citizen relations, and restore public trust. If these demands are taken seriously, the government has the potential to reduce the legitimacy crisis and improve the quality of public services. On the contrary, ignoring such demands can deepen public distrust and worsen political stability. Thus, the demands of 17+8 are an important signal for the need for structural change and a real commitment to realizing a fairer, more transparent, and responsive government.

This study evaluates the impact of TATO, ROA, DER, stock returns, and firm size on company value (PBV) for 28 infrastructure companies listed on the Indonesia Stock Exchange (IDX) during 2021–2023. The background to this research is the crucial role of the infrastructure sector amid government budget dynamics that affect corporate performance and investor perception. The results show that ROA, DER, and stock returns have a significant positive effect on company value. This indicates that high profitability, optimal debt management, and good stock returns send positive signals to the market. Conversely, TATO was found to have a significant negative effect, reflecting that inefficiencies in asset management can reduce investor confidence. Meanwhile, firm size had no significant impact on company value. This study recommends that investors use ROA, DER, and stock return as key indicators in decision-making. At the same time, companies are advised to optimise profitability and debt management to enhance their value in the eyes of investors.

At the company PT. PLN (Persero) ULTG Madiun carried out many tests relay, one of which is intertrip testing relay distance. Intertrip testing relay distance is a test carried out between 2 (two) relay distance conducting bays in main substations that face each other. Intertrip testing relay distance must be done if replacement occursrelay, replacement of teleprotection equipment, changes to protection schemes, and changes to logic circuitsrelay which is related to the send or receive signal from the opposite substation. However, this test requires synchronization between two intertrip test tools relay distance at each substation. In this final project, it will be designed Time Syncronization As A Tool For Intertrip Relay Distance Testing, to make things easier for PT employees. PLN (Persero) ULTG Madiun when synchronizing two intertrip test equipment relay distance. All process control of this tool uses ESP32 Devkit V1. Meanwhile for displaying setting time for testing can be seen on the web assisted by GPS NEO-6M. The results obtained when testing the Dolopo by Ponorogo R and S phase distance intertrip relay were carried out at 12.00.00, experiencing a delay time of 0.0553 seconds when the PMT tripped. Meanwhile, the Dolopo by Ponorogo T phase distance intertrip relay test was carried out on 12.09.00 and experienced a delay of 0.0529 seconds when the PMT tripped. From two intertrip relay distance tests, the trip time delay is no more than 0.1 second.

The Hartley transform provides a real-valued alternative to the classical Fourier transform, offering structural advantages for the analysis of real-valued signals. This paper presents a systematic study of the continuous Hartley transform, including its definition, inversion formula, Plancherel identity, and core operational properties such as shifting, modulation, and convolution. The analytical framework is developed in parallel with the classical Fourier theory to highlight structural similarities and distinctions between the two transforms. Furthermore, we establish a Hartley-type Heisenberg uncertainty principle using two complementary approaches: a direct method based on intrinsic properties of the Hartley kernel, and a Fourier-based method that exploits the algebraic relationship between the Hartley and Fourier transforms. These results provide a unified and rigorous foundation for understanding uncertainty relations within real-valued transform frameworks, and they demonstrate the continued relevance of the Hartley transform in harmonic analysis, integral transforms, and modern signal processing.

This study alims to alnallyze the effect of talste alnd promotion on consumer purchalsing decisions alt McDonalld's Talmaln Pinalng Sidoalrjo. McDonalld's, als one of the lealding bralnds in the industry, hals succeeded in crealting mouth-waltering signalture flalvors alnd developing smalrt promotionall straltegies to influence consumer purchalsing decisions. This resealrch wals conducted alt McDonalld's Talmaln Pinalng Sidoalrjo involving 100 respondents als al resealrch salmple. The resealrch method used is qualntitaltive using multiple linealr regression alnallysis techniques. Daltal wals obtalined through al questionnalire consisting of questions albout talste, promotions alnd purchalsing decisions. The results showed thalt the respondents' responses to the talste of McDonalld's Talmaln Pinalng Sidoalrjo were in the sufficient caltegory, while the responses to promotions were allso in the sufficient caltegory. However, the response to the purchalse decision is in the very good caltegory. The test results of the coefficient of determinaltion show thalt talste alnd promotion caln explalin albout 5.5% of the valrialtion in purchalsing decisions. The remalining 94.5% caln be explalined by other falctors not exalmined in this study. Hypothesis testing allso shows thalt talste hals al positive alnd significalnt influence on purchalsing decisions, while promotion hals no significalnt effect. This study concludes thalt the talste of McDonalld's Talmaln Pinalng Sidoalrjo plalys aln importalnt role in shalping consumer preferences alnd influencing purchalsing decisions. Promotion allso hals aln effect, allthough not staltisticallly significalnt. They need to continue to develop alnd improve their unique flalvors alnd effective promotion straltegies to malintalin their position in the falst food industry alnd meet the expectaltions of consumers who walnt pralcticall alnd quallity food.

This study aims to explore how gold price volatility influences customers’ investment decisions in Islamic banks within the framework of Islamic values. Employing a qualitative phenomenological approach, the research investigates the perceptions, motivations, and strategies of twelve active customers of Bank Syariah Indonesia in Surabaya through in-depth interviews. The findings reveal that gold price volatility does not necessarily reduce investment interest; instead, it stimulates adaptive and reflective behavior grounded in religious commitment and Islamic financial literacy. Investment decisions are shaped by three main factors: rational risk perception, religious conviction in the permissibility of gold as an Islamic instrument, and trust in the integrity of Islamic financial institutions. Thus, gold price volatility is interpreted not only as an economic signal but also as a social and spiritual phenomenon that fosters financial maturity among investors. The study concludes that faith-driven investment behavior contributes to financial resilience and moral stability amid market uncertainty. These insights enrich the field of Islamic behavioral finance by highlighting the integration of economic rationality and spiritual values in investment decision-making.

This study examines the effectiveness of law enforcement regarding violations of Article 287 paragraph (2) of Law Number 22 of 2009 on Road Traffic and Transportation, particularly concerning red-light violations (APILL). Although the legal provisions are clearly formulated, practical implementation reveals a gap between the normative expectations (das Sollen) and actual conditions (das Sein). This research employs a normative juridical method supported by secondary empirical data, analyzed through Lawrence M. Friedman’s legal system theory and Satjipto Rahardjo’s theory of law enforcement. The findings show that the effectiveness of enforcing red-light regulations is influenced by structural aspects of law enforcement agencies, the community’s weak legal culture, and supporting facilities such as the uneven distribution of ETLE systems. The study concludes that the enforcement of Article 287 paragraph (2) remains suboptimal, and therefore requires strengthening ETLE infrastructure, improving officer professionalism, reassessing the proportionality of sanctions, and enhancing public legal education to increase traffic compliance.

Accurate wind turbine power curve modeling plays a crucial role in performance evaluation, energy yield estimation, and data-driven control strategies. However, actual power curves often exhibit non-linear behavior influenced by atmospheric variability, measurement noise, and SCADA anomalies, making conventional modeling approaches less effective. This study proposes an optimized logistic power curve model whose parameters are tuned using Particle Swarm Optimization (PSO) to improve predictive accuracy. The analysis uses the Wind Turbine SCADA Dataset from Kaggle, which undergoes extensive preprocessing including physical rule filtering, outlier detection with the Interquartile Range (IQR) method, anomaly removal, and smoothing of the power signal. A three-parameter logistic model is selected due to its ability to capture the typical S-shaped relationship between wind speed and power output. PSO is applied to identify optimal model parameters by minimizing the Mean Squared Error (MSE), utilizing 40 particles over 200 iterations. The optimized model achieves strong predictive performance with RMSE of 404.09, MAE of 179.96, and R² of 0.904 on the test set, indicating that more than 90% of the variability in actual power can be explained by wind speed. Residual analysis reveals heteroscedastic patterns and slight overestimation in mid-range wind speeds, yet overall model consistency remains high. Comparative evaluation against Linear Regression, Random Forest, and logistic modeling using curve_fit shows that the Logistic–PSO approach provides the most accurate and stable predictions. These findings demonstrate that combining logistic modeling with PSO offers an effective and robust method for data-driven wind turbine power curve optimization.

The old bridge structure, are subjects that must be monitored in bridge maintenance. Bridge detection supervision is very necessary to determine structural deformation caused by normal operations or environmental impacts such as temperature, humidity and heavy vehicle loads. Monitoring the structure as a whole also needs to be carried out after extreme conditions occur, such as an earthquake. Research on structural dynamics analysis of steel frame bridge conditions to determine the behavior of the structure. Vibration data is read using a wireless sensor network or accelerometer. The vibration mode signal obtained is processed using Fast Fourier Transform (FFT) analysis via MATLAB software, which will produce a graph of the relationship between the frequency domain and the time domain. Through this graph, the dynamic characteristics of the bridge structure can be analyzed, with several variations in the condition of the bridge structure.

The rapid development of Islamic banking in Indonesia faces significant challenges in maintaining liquidity and profitability amidst dynamic capital market conditions. The urgency of this study arises from the need to examine whether traditional financial ratios, such as the Financing to Deposit Ratio (FDR) and Return on Equity (ROE), play a decisive role in influencing investment decisions, which are proxied by the Price to Earning Ratio (PER). The main objective of this research is to empirically test the effect of liquidity and profitability, both partially and simultaneously, on investment decisions in Islamic commercial banks listed on the Indonesia Stock Exchange during the 2021–2025 period. This study adopts an associative design with a quantitative approach, utilizing secondary data from financial reports obtained from the IDX, and analyzed using multiple linear regression on 68 observation samples. The findings reveal that neither liquidity nor profitability significantly influence investment decisions, either partially or simultaneously. These results suggest that investors in the Islamic banking sector tend to prioritize non-financial factors such as sharia compliance, governance, macroeconomic conditions, and ESG trends, rather than conventional financial indicators. In conclusion, this research extends the understanding of the limitations of Signaling Theory in the sharia context and recommends the development of a more holistic investment evaluation model. Future studies are encouraged to incorporate non-financial variables for a more comprehensive analysis.

Importance of gut microbiota during neurodevelopment has increased, as has the potential relationship between the gut microbiota and (ASD). Complex associations between gut microbiota and ASD are explored here, including significant pathways such as immune modulation, neurotransmitter control, and gut-brain axis signaling. Based on available data, individuals with ASD possess distinctive microbial signatures that are characterized by reduced diversity and altered abundance of specific bacterial species. Such modifications could be related to symptoms of the behavioral nature, neuroinflammatory, as well as gastrointestinal. ASD growth as well as severity could be influenced by the composition of microbiome, depending on genetic, nutrition, microbial exposure during the earliest phases, as well as antibiotic use. Additional therapies based on the microbiome that presented the potential to alleviate the symptoms related to ASD include the use of probiotics, prebiotics, diet modification, as well as fecal microbiota transplantation (FMT). Still, establishing the causal associations, standardizing the procedure of handling the patients, as well as solving the problem related to the manipulation of microbiome, are still challenging activities, though. Large-scale, long-term studies need to be the core agenda of subsequent research, so that specific microbial signatures associated with ASD will be clearly defined, as well as tailored therapies that address the microbiome will be developed. Understanding more about the role that the microbiota plays during ASD may open up the diagnosis as well as the therapy based on the entirely new concepts, something that will ultimately benefit the patients who possess the disorder.