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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.

Bangunan yang berada di daerah rawan gempa dengan tanah lunak cenderung memiliki tingkat kerentanan yang lebih tinggi. Dalam merancang struktur bangunan bertingkat, beban gempa menjadi salah satu parameter utama yang sangat berpengaruh. Kekuatan sebuah bangunan ditentukan oleh elemen-elemen struktur yang menopangnya dan kolom merupakan elemen struktur yang memiliki peran penting dalam konstruksi. Jika kolom mengalami kegagalan, bangunan yang ditopangnya berpotensi runtuh. Oleh karena itu, rendahnya ketahanan konstruksi di wilayah ini tetap menjadi permasalahan yang sedang diteliti saat ini. Tujuan penelitian ini untuk mengetahui seberapa jauh pengaruh variasi dimensi kolom terhadap struktur bangunan terhadap gempa, sehingga diperoleh ukuran dimensi kolom yang paling optimal. Pada penelitian ini, objek yang digunakan adalah bangunan 8 lantai yang terdiri dari lantai 1-3 merupakan kantor dan lantai 4-8 merupakan hunian. Pada perencanaan ulang bangunan ini berpedoman pada persyaratan beton struktural (SNI 2847:2019), perencanaan ketahanan gempa (SNI 1726:2019), dan beban minimum untuk perancangan bangunan gedung dan struktur lain (SNI 1727:2020), dengan pemodelan menggunakan software ETABS V.22. Redesain bangunan ini untuk mengetahui ukuran dimensi kolom yang paling optimal. Hasil penelitian perubahan dimensi kolom terhadap kekuatan struktur bangunan yakni dengan memperbesar dimensi kolom, kapasitas penampang bertambah yang dapat meningkatkan ketahanan kolom terhadap beban gempa. Selain itu, dimensi yang lebih besar juga membantu mengurangi tegangan aksial pada kolom, sehingga menurunkan risiko kegagalan akibat tegangan berlebih atau kelelahan material. Semakin besar dimensi kolom yang digunakan maka semakin kecil nilai simpangan yang dihasilkan. Artinya dengan dimensi kolom yang lebih kecil memiliki resiko yang lebih tinggi, sebab nilai simpangan lebih dekat dengan batas yang diijinkan. Akan tetapi hasil analisis menunjukkan bahwa dimensi kolom dengan berbagai variasi masih aman dari pengaruh delta izin.

This research is motivated by the dynamics of the layer chicken egg supply in Tabanan Regency. The purpose of this study is to analyze the factors that have the effect on the supply of layer chicken eggs, particularly the policy of banning the use of Antibiotic Growth Promoters (AGP), the number of layer chicken breeders, and the selling price of layer chicken eggs. The research method used is quantitative analysis with a multiple linear regression approach, using primary data obtained through questionnaires distributed to 101 layer chicken farmers in Tabanan Regency and analyzed with the assistance of EViews 12 software. The results show that the policy variable of banning the use of AGP has a negative and significant effect on the supply of layer chicken eggs. In contrast, the number of layer chicken breeders and the selling price of layer chicken eggs have a positive and significant effect on the supply. The coefficient of determination (R²) value of 0,316 indicates that the three independent variables are able to explain 31,6% of the variation in the supply of layer chicken eggs, while the remaining variation is affected by other factors outside the research model.

This study aims to analyze ticket purchasing behavior at Mawar Tour & Travel Pekalongan. The issue raised in this research is the decline in direct ticket purchases due to the rise of competing digital platforms, which has made it more challenging for traditional travel agencies to retain customers. The research method used is quantitative, employing multiple linear regression analysis through SPSS software to evaluate various factors influencing customer decisions. The results show that service quality and price perception significantly affect purchase intention, while booking convenience does not directly influence it but is fully mediated by trust, highlighting the importance of building strong customer relationships. These findings suggest that Mawar Tour & Travel should enhance service quality, strengthen customer trust, and adopt competitive pricing strategies to improve customer satisfaction and increase sales. This approach would enable the agency to stay competitive in the rapidly changing digital marketplace and better meet the needs of its customers.

This study aims to analyze the interest of residents of Semarang City in using the Trans Jateng service and to identify the factors that influence this interest. The background of this study is the low proportion of residents switching to public transportation despite the availability of the Trans Jateng service. This research adopts a quantitative approach, utilizing a survey method to collect data, which is then processed using SPSS software. The results of the study indicate that factors such as service quality (including service, comfort, and safety), fare perception, and stop access significantly influence the interest of residents in using Trans Jateng. These findings are valuable for understanding the factors that drive the adoption of public transportation in urban areas. The novelty of this study lies in its specific focus on Semarang City and the integration of resident interest variables with the relatively new Trans Jateng service in the region. The study’s implications offer practical recommendations for public transport operators to improve fare policies, enhance promotional strategies, and optimize modal integration to encourage greater use of public transportation.

Management of household waste in Final Disposal Sites (TPA) faces a serious problem, where most of the waste accumulates and is difficult to decompose due to its complex nature. This condition substantially inhibits natural decomposition processes and limits the effectiveness of recycling efforts. Pre-processing operations, such as sorting and crushing, which are still dominated by manual methods, are proven to be inefficient, high-risk, and require large allocations of land resources and manpower. Therefore, automated technological innovation is needed to facilitate the efficient separation of organic components from inorganic materials (packaging). This research was conducted to determine the design and structural strength analysis of a hammer mill type depackaging machine, carried out using Solidworks software. Structural analysis simulation utilizes Finite Element Analysis (FEA) to determine the structural strength of the machine. The specifications of the hammer mill type depackaging machine include a capacity of 3000 kg/hour, a hammer mill input power of 12 KW, and a rotational speed of 2500 rpm with a torque of 34.54 Nm. Meanwhile, the screw conveyor input power is 0.75 KW and the rotational speed is 20 rpm. The FEA simulation analysis results for the hammer mill type depackaging machine showed that the maximum Von Mises stress value recorded is 3,022×10^7   N⁄m^2 , the maximum displacement value measured is very minimal, namely 2,793×10^(-1)  mm, and the Factor of Safety (FOS) obtained is 8.3. This FOS value significantly exceeds the required minimum safety limit (>3), confirming that the machine design has optimal reliability, fatigue resistance, and structural integrity for operation under intensive working conditions at the TPA. The conclusion of this study indicates that the engineering design of this hammer mill type depackaging machine is safe and meets structural technical requirements to proceed to the implementation phase, potentially becoming a sustainable technological solution in improving the efficiency of waste pre-processing.

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.

Green accounting procedures have been adopted by numerous companies in response to the growing global focus on environmental responsibility. Nonetheless, monetary instability is still a major obstacle that can reduce productivity in Indonesia's manufacturing sector. The purpose of this research is to analyze industrial businesses listed on the Indonesia Stock Exchange from 2019 to 2023 and see how green accounting, financial crisis, and earnings management affect financial performance. The population in this study consists of 68 industrial sector companies, with a sample of 7 companies selected through purposive sampling based on 4 criteria. We used EViews software and Moderated Regression Analysis (MRA) for a quantitative approach. First, financial distress has a significant impact on financial performance. Second, green accounting has a significant positive effect on financial performance. Third, earnings management does not moderate the relationship between financial distress and financial performance. Fourth, earnings management does not moderate the relationship between green accounting and financial performance. With an Adjusted R-Square value of 79.73%, the study model has a high level of explanatory power. It may be used to explain the majority of the variation in financial performance. This shows that the constructed model is applicable and fits the empirical data well. Transparent reporting and real sustainability initiatives are still vital for improving company results, according to these results, as profits management methods do not change the impact of environmental and financial variables, which are important drivers of performance.

The usage of blockchain has increased around the world over the years. It has become widely used in various sectors that need transparency, such as accounting, business, and auditing. Blockchains are gaining more popularity after being applied as a system for digital asset ownership, such as cryptocurrency and NFTs (Non-Fungible Tokens). This growing trend of blockchain is followed by the increasing trend of research regarding it in the last decade. Blockchain has the potential to revolutionize the auditing sector and enhance economic accountability due to its decentralized system. Therefore, research regarding blockchain applications in auditing is becoming an important topic. This study adopts a qualitative approach by using datasets retrieved from the Scopus website, from the search result of blockchain auditing, with a total of 1228 articles that were published in the last decade (2015-2025). Furthermore, this study also uses several software programs as data processing tools, such as R Studio, VOSViewer, and Publish or Perish. This study aims to understand the research trend regarding blockchain auditing in the last decade and highlight its implications for the auditing and economic sectors.

The phenomenon of fashion FOMO (fear of missing out) exerts a significant influence on fashion consumption behavior, particularly among the demographic from late adolescence to young adulthood. The fear of missing trends can lead individuals to make impulsive purchases. In contrast, the Minimalist Wardrobe concept promotes a conscious lifestyle that emphasizes functional and sustainable clothing choices. The objective of this study is to examine the influence of Fashion FoMO on purchasing decisions and to assess the role of a Minimalist Wardrobe as a moderating variable. A quantitative approach was employed, utilizing purposive sampling to collect data through surveys administered to respondents residing in Semarang City. The data were analyzed using the Structural Equation Modeling (SEM) method with SmartPLS software. The results show that Fashion FoMO has a significant and positive influence on purchasing decisions for UNIQLO products. Meanwhile, the Minimalist Wardrobe shows an insignificant effect. These findings suggest that purchasing decisions are more strongly driven by emotional pressure from FoMO than by minimalist lifestyle considerations.

Salah satu bentuk pengolahan limbah menjadi energi yang berasal dari limbah padat perkotaan adalah sampah bahan bakar turunan atau Refuse Derived Fuel (RDF). Terdapat berbagai teknologi pengeringan yang biasanya digunakan untuk pengeringan RDF. Contoh penerapannya adalah oil tube rotary dryer sebagai alternatif dari steam tube rotary dryer karena sejumlah alasan utama. Pertama, penggunaan oil engine sebagai material penghantar panas tidak membutuhkan tekanan tinggi seperti pada steam tube rotary dryer sehingga komposisi desain yang tidak serumit steam tube rotary dryer. Penelitian ini dilakukan untuk mengetahui perancangan dan analisis kekuatan struktur mesin oil tube rotary dryer yang dilakukan melalui software Solidworks. Cara kerja mesin dimulai dari pemanasan thermal oil di unit pemanas hingga mencapai suhu operasi yang diinginkan. Oil panas kemudian disirkulasikan secara tertutup ke dalam tube di dalam drum pengering. Pada saat yang sama, RDF dengan kadar air awal 55% dimasukkan melalui inlet housing ke dalam shell. Suhu yang bekerja pada oil untuk memanaskan sekitar 150°C, jumlah total tube 65 buah berdiameter 1,25 inch, dan panjang pipa 10 m yang tersusun di dalam shell. Hasil analisis struktur mesin oil tube rotary dryer berdasarkan simulasi didapatkan stress (von mises) dengan nilai tertinggi 9,371 × 108 N/m^2. Sedangkan nilai terendah 9,628 × 103 N/m^2. Nilai tertinggi dari hasil simulasi tersebut terjadi pada sudut tajam bagian support roller yang tidak terdapat kontak, sehingga hal ini dapat diabaikan karena merupakan stress singularity. Hasil simulasi displacement  dengan  nilai tertinggi 1,026 × 101 N/m^2. Sedangkan nilai terendah 1,000 × 10-30 N/m^2. Nilai tertinggi dari hasil simulasi tersebut merupakan penerapan dari gaya centrifugal yang terjadi pada drum/shell rotary. Hasil simulasi Safety factor yaitu mengacu pada rata-rata probe result adalah 2,3. Sehingga dari hasil simula

Penelitian ini bertujuan untuk menganalisis pengaruh good corporate governance terhadap intellectual capital, serta bagaimana dampaknya terhadap kinerja keuangan. Penelitian dilakukan pada perusahaan perbankan yang terdaftar di Bursa Efek Indonesia periode 2021-2024. Data yang digunakan adalah data kuantitatif berupa laporan keuangan dan annual report. Jumlah sampel yang diperoleh sebanyak 188, dengan menggunakan metode purposive sampling. Teknik analisis data yang digunakan adalah regresi linear berganda menggunakan software IBM SPSS 25. Hasil penelitian menunjukkan bahwa dewan komisaris independen, komite audit, kepemilikan institusional berpengaruh positif terhadap intellectual capital. Intellectual capital berpengaruh positif terhadap kinerja keuangan. Hasil penelitian variabel kontrol menunjukkan ukuran perusahaan berpengaruh positif, sedangkan leverage berpengaruh negatif terhadap intellectual capital dan kinerja keuangan.

Penelitian ini bertujuan untuk menganalisis pengaruh literasi keuangan, pengetahuan investasi, dan preferensi risiko terhadap minat investasi reksa dana pada mahasiswa di Yogyakarta. Penelitian ini adalah penelitian kuantitatif, di mana populasi dalam penelitian ini adalah mahasiswa jurusan Manajemen yang termasuk Generasi Z berdomisili di Yogyakarta. Sampel diambil dengan menggunakan teknik purposive dan convenience sampling sebanyak 101 responden. Data dikumpulkan menggunakan kuesioner elektronik. Data diolah dan dianalisis dengan menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan bantuan software SmartPLS versi 4.1.1.2. Hasil penelitian menujukkan bahwa literasi keuangan, pengetahuan investasi, dan preferensi risiko secara parsial berpengaruh positif dan signifikan terhadap minat investasi reksa dana. Tiga faktor yang diteliti dapat menjelaskan 56,4% variasi minat investasi reksa dana, sementara 43,6% lainnya dijelaskan oleh faktor lain yang tidak diteliti dalam penelitian ini. Literasi keuangan menjadi faktor yang paling dominan membentuk minat investasi reksa dana. Hasil ini sesuai dengan dugaan awal, di mana berinvestasi memerlukan tingkat pengetahuan dan kecakapan yang tinggi. Mahasiswa dengan preferensi risiko yang tinggi akan memiliki minat investasi reksa dana yang lebih tinggi karena reksa dana pada umumnya memiliki risiko yang tinggi daripada instrumen investasi lainnya seperti deposito dan obligasi.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

This explanatory research aims to know the effect of self acceptance and emotional regulation to predict psychological well being cancer patient and to find which one is more effective to predict psychological well being cancer patients. The sampling technique used in this research was convenience sampling technique. Subjects in this research is 100 cancer patient at Dr Cipto Mangunkusumo National Central Public Hospital Jakarta. Data collection was carried out through questionnaires, interviews and observation. Self report technique is used in collecting instrumen data. The data analysis technique used is multiple linear regression analysis processed using Statistical Package for the Social Sciences (SPSS) software. The result shows that there is an influence between self acceptance and emotional regulation  in predicting psychological well being where the score of p value F test is 0,000 (p<0,05) and it is found that self acceptance  is more effective in predicting psychological well being compare with emotional regulation  Based on the research results, the findings are as follows: (1) collectively or simultaneously self acceptance and emotional regulation has a positive and significant effect on psychological well being, (2) partially self acceptance has a positive and significant effect on psychological well being; (3) partially emotional regulation has a positive and significant effect on psychological well being.

This study aims to examine and analyze the influence of financial literacy and financial planning on financial management with financial attitude as a mediating variable among production employees of PT. Yih Quan Footwear Indonesia. This research employs a quantitative approach. The population consists of production employees of PT. Yih Quan Footwear Indonesia located in the Batang Integrated Industrial Estate, with a total sample of 112 respondents. Data were collected through an online questionnaire distributed via Google Form. The data analysis technique includes the analysis of the structural model (Inner Model), measurement model (Outer Model), and hypothesis testing. Data processing was carried out using SmartPLS 4 software. The results indicate that financial literacy has no significant effect on financial management, while financial planning has a positive and significant effect on financial management. Furthermore, financial literacy positively and significantly influences financial attitude, whereas financial planning shows no significant effect on financial attitude. Financial attitude has a positive and significant impact on financial management and mediates the relationship between financial literacy and financial management. However, financial attitude does not fully mediate the relationship between financial planning and financial management. These findings highlight the importance of enhancing employees’ financial literacy and attitudes to enable more prudent, structured, and sustainable financial management.

Repurchase intention represents a tangible form of consumer satisfaction toward a product or service offered by a brand, which fosters a desire among consumers to make repeat purchases of the same brand. Several factors influence repurchase intention, including brand trust, brand image, and electronic word of mouth (e-WOM). The existence of repurchase intention has significant implications for various industries, particularly the food and beverage (F&B) sector. One of the most recognized F&B franchises in Indonesia is the international brand Pizza Hut. This study aims to analyze the influence of brand trust, brand image, and e-WOM on repurchase intention among consumers of the international Pizza Hut franchise in Denpasar City. The research was conducted on consumers of the international Pizza Hut franchise in Denpasar, with a total of 100 respondents selected using purposive sampling. Data were collected through the distribution of offline questionnaires. The data analysis techniques employed in this study include multiple linear regression, as well as descriptive and inferential analyses using SPSS software. The findings indicate that brand trust, brand image, and e-WOM have a positive and significant effect on repurchase intention. Based on these findings, it is recommended that the international Pizza Hut franchise consider the aspects of brand trust, brand image, and e-WOM as key strategies to enhance consumers’ repurchase intention.

This study aims to analyze the relationship between managerial ownership, institutional ownership, audit committee, and research and development (R&D) expenses on Intellectual Capital Disclosure (ICD) in healthcare sector companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. Intellectual Capital Disclosure is essential as it reflects a company’s ability to manage knowledge, innovation, and human resources that serve as its competitive advantage. This research employs a quantitative approach using the total sampling method, where all healthcare sector companies that meet the criteria are included as samples. Secondary data were obtained from annual reports and analyzed using panel data regression with the assistance of Stata 19 software. Model selection was conducted through Chow, Hausman, and Lagrange Multiplier (LM) tests, with the results indicating that the Random Effect Model (REM) was the most appropriate model to use. The results show that managerial ownership, institutional ownership, and audit committee have negative and insignificant relationships with Intellectual Capital Disclosure. In contrast, research and development activities have a positive and significant relationship with Intellectual Capital Disclosure.

This research examines how cost characteristics influence the relationship between target costing and time-driven activity-based costing (TDABC) approaches and their subsequent impact on corporate competitiveness. Using a descriptive analytical approach, the researcher collected data through a structured questionnaire, which was then processed and analyzed using SPSS software. The study aimed to test the validity of the proposed hypothesis, which suggested that a standalone approach—either target costing or TDABC—fails to sufficiently enhance competitiveness. However, the integration of both approaches proved to be beneficial, allowing companies to improve their competitive positioning by reducing overall costs. The research concludes that combining target costing and TDABC can create synergies that better align with the goal of cost reduction. As a recommendation, companies are encouraged to integrate these cost management approaches to maximize their competitive advantage in the market and optimize cost efficiency.

Social media has evolved into more than just a means of communication; It now plays an important role in shaping the image of the institution, providing recognition, and increasing employee motivation and involvement, especially in public service institutions. This study aims to determine the effect of the use of Instagram @dinkopukmperindagkotaserang accounts on employee self-actualization at UPT Pasar Kota Serang. The method used is a descriptive quantitative approach with survey techniques. The population and sample in this study amounted to 40 employees, which were taken using the total sampling technique. Data analysis was carried out with the help of SPSS software version 26, including validity tests, reliability tests, descriptive statistics, and simple linear regression. The results of the study show that the use of Instagram accounts has a significant influence on employee self-actualization. The determination coefficient value of 0.471 shows that 47.1% of the self-actualization variable is influenced by the use of Instagram accounts. These findings indicate that the better the content management and the higher the employee's involvement with the account, the higher the level of self-actualization achieved in carrying out their role as public servants. This study recommends optimizing institutional social media strategies as part of efforts to improve the quality of human resources and the effectiveness of public services.