Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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When songs provide artistic value: a historical functionalism study in 1970s Indonesian popular music
No full textThe development of popular music in Indonesia as a country with rich cultural plurality, is interesting to study. The dominant study of the dangdut genre has the potential to overlook the artistic richness contained in its various musical genres. The setting of the 1970s as a historical period of global modern society that gave birth to many countercultural phenomena and leaps in communication technology in developing countries, including Indonesia, has guided the focus of our research. This research aims to answer the question of how the cluster of artistic values of 1970s Indonesian popular music in its popular songs was able to make an inspirational contribution to the development of Indonesian popular music thereafter. The choice of the conceptual foundation of art historical functionalism has led us to use an interdisciplinary research design that includes a musicological approach to analyze the form of the song, a cultural studies approach to interpret the artistic content of the music text, and a critical discourse analysis to examine the contradictions of the music discourse. From the results of the combined analysis, we found that the artistic value of 1970s Indonesian popular music stands on the foundation of artistic form supported by the three forms of songs as musical compositions, and the aesthetics of song lyrics. In addition, it was found that the cluster of artistic value that includes aesthetic value, emotional response, aesthetic value, cognitive knowledge, and historical value in 1970s Indonesian popular songs also offers artistic novelty as well as a distinctive, advanced expression impact. The findings of this study have provided additional knowledge of popular music, with an emphasis on the functionalization of the artistic value of a song in its historical setting. The findings also contribute to the need for the study of Indonesian popular music with its rich artistic content to develop an interdisciplinary approach
Strategic Chess Algorithm-Based PI Controller Optimization for Load Frequency Control in Two-Area Hybrid Photovoltaic–Thermal Power Systems
Full text linkMaintaining frequency stability in hybrid renewable-integrated power systems remains a critical challenge due to the inherent variability and uncertainty of photovoltaic–thermal (PV–T) energy sources. Traditional proportional–integral (PI) controllers, optimized using conventional metaheuristic algorithms such as the Whale Optimization Algorithm (WOA), Firefly Algorithm (FA), and Salp Swarm Algorithm (SSA), often suffer from limitations including slow convergence, premature convergence to local optima, and reduced robustness under severe load disturbances. The research contribution is the development and systematic evaluation of a chess algorithm (CA)-based PI controller tuning approach for enhancing load frequency control (LFC) in hybrid PV–T systems. Unlike population-based methods, the CA employs chess-inspired strategic decision-making processes, which improve the search efficiency and the ability to escape local optima in high-dimensional optimization problems. In this study, the proposed CA-based optimization method is applied to a two-area hybrid PV–T power system, where the system is subject to various operating conditions, including solar radiation fluctuations and step load perturbations. The tuning of PI controller parameters is performed using the integral of time-weighted absolute error (ITAE) as the objective function. Simulation results demonstrate that the CA-optimized PI controller achieves superior performance in minimizing overshoot, undershoot, and settling time when compared with controllers optimized by WOA, FA, and SSA. Specifically, the CA approach achieves faster stabilization and lower frequency deviations, highlighting its potential for real-time implementation and enhanced grid reliability. Future work will explore the scalability of the proposed method to multi-area power systems and evaluate its computational efficiency through hardware-in-the-loop validation
Utilizing Short-Time Fourier Transform for the Diagnosis of Rotor Bar Faults in Induction Motors Under Direct Torque Control
Full text linkIndustrial applications rely heavily on induction motors (IMs). Even though any IM problem can seriously impair operation, rotor bar failures (RBFs) are among the toughest to identify because of their detection challenges. RBFs in IMs can significantly impact performance, leading to reduced efficiency, increased vibrations, and potential IM failure. This research provides a thorough analysis of diagnosing these issues by detecting RBFs and evaluating their severity using three sophisticated signal processing techniques (Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT)). The three techniques (FFT, DWT, and STFT) are used in this work to assess the stator currents. An accurate mathematical model of the IM under RBFs serves as the basis for the simulation. The robustness of Direct Torque Control (DTC) is assessed by examining the IM's behavior in both normal and malfunctioning situations. Although the results show that DTC successfully preserves motor stability even when there are flaws, the current analysis offers some significant variation. The findings show that when it comes to identifying RBFs in IMs and determining their severity, the STFT performs better than FFT and DWT. The suggested method maintains low estimation errors and strong performance under various operating situations while providing high failure detection accuracy and the ability to discriminate between RBFs
Optimizing breast cancer classification using SMOTE, Boruta, and XGBoost
Full text linkBreast cancer remains one of the leading causes of death among women worldwide. This study aims to develop a clinical data-based breast cancer classification framework by integrating the Synthetic Minority Oversampling Technique (SMOTE), the Boruta feature selection algorithm, and the XGBoost classifier. The proposed approach is tested using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, consisting of 569 samples and 30 numerical features. SMOTE addresses class imbalance, Boruta selects the most relevant diagnostic features, and XGBoost is the main classification algorithm due to its tabular and imbalanced data robustness. Model validation is conducted through Repeated Stratified K-Fold Cross Validation with 30 repetitions to ensure statistical stability. The resulting model achieves excellent classification performance, with an average accuracy of 0.9608 ± 0.0274, precision of 0.9465 ± 0.0481, Recall of 0.9512 ± 0.0524, and F1-score of 0.9475 ± 0.0374. The ROC-AUC value reaches 0.9926 ± 0.0094, the PR-AUC is 0.9906 ± 0.0113, and the Matthews Correlation Coefficient (MCC) is 0.9179 ± 0.0575, indicating a well-balanced model. Clinically, this model can aid early diagnosis by effectively reducing irrelevant diagnostic attributes, retaining only 10 key features without compromising accuracy, thereby offering a lightweight yet reliable diagnostic tool. However, limitations include the relatively small dataset and the absence of hyperparameter tuning. Future research should explore larger datasets, advanced ensemble methods, and interpretability techniques such as SHAP or LIME to improve clinical transparency and adoption
Developing an integrated brand activation strategy framework for platform-dependent creative studios based on the kemsgraphics case
Full text linkDigital creative enterprises face significant strategic risk from platform dependency, as reliance on a single marketplace can lead to unstable revenue when algorithmic changes reduce visibility. This research addresses that risk by designing and empirically testing a brand activation strategy to elevate brand awareness for Kemsgraphics, a virtual studio highly dependent on Fiverr. The strategy was developed using the Brand Activation Process (Discovery, Strategic Development, Creative Development, Delivery, and Evaluation) as the primary framework, enriched by the incorporation of the Steps of Effective Communication Development for strategic planning and Design Thinking principles for creative solution development. The impact of the strategy was tested using a quasi-experimental, time-series design with mixed-method analysis, collecting daily performance data and qualitative insights from five digital platforms. The intervention produced an 890% increase in TikTok follower growth and substantial improvements in reach, engagement, and profile visits across all platforms during a five-day period. This research demonstrates how an integrated, theory-based brand activation strategy can help remote creative businesses build brand resilience and reduce platform risk, providing a practical blueprint for managing digital brand presence in the creative sector
Content analysis of quick sampler functionality in logic pro x: an investigative study of configuration features
No full textThis study investigates the configuration and functional capabilities of the Quick Sampler feature within Logic Pro X, focusing on its application in audio sample processing for music composition. Utilizing a qualitative content analysis approach based on Krippendorff’s methodology, the research systematically examines key components of Quick Sampler, including Synth Mode, Mod Matrix, and Mapping Section, to evaluate their roles in enhancing workflow efficiency and creative flexibility. Data were gathered from direct interface analysis, supported by documentation and user experience reports. The findings reveal that Quick Sampler’s intuitive drag-and-drop interface combined with flexible sample modes simplifies the sampling process, while the Mod Matrix facilitates complex modulation routing that expands sound design possibilities. Although the study is descriptive and qualitative, it highlights Quick Sampler’s potential to streamline compositional processes within digital audio workstations. Future research is recommended to quantitatively measure user efficiency and to compare Quick Sampler’s performance with other DAWs, as well as to explore its optimization for specific music genres
Visual self-presentation through unique costumes: a case study of the digital persona of a ‘crackers man’ on Instagram
No full textThis study examines how a “Crackers Manâ€, an Instagram content creator and crackers maker, publicly known through his Instagram account  @jeniinugraha, utilizes distinctive costumes for visual self-representation and shaping a digital persona, which transcends the conventional perception of his profession. This study aims to analyze the strategies and impacts of this unconventional approach. A qualitative case study method was employed, utilizing in-depth content analysis of @jeniinugraha’s Instagram profile, semi-structured interviews with the creator, and a survey of audience perceptions. The main findings of this study suggest that the consistent use of thematic costumes combined with humorous messaging strategies contributes to the visual uniqueness of the content and an approachable digital persona. This study underscores the effectiveness of innovative visual communication for personal branding by micro-entrepreneurs on social media, offering a model for leveraging creativity to build distinction in crowded digital markets
The influence of the value clarification technique learning model on improving elementary school students' music creativity learning outcomes
Full text linkIn learning activities, creativity is an important aspect, where students can improve and develop their creative talents and abilities in thinking creatively. In grade III students of Muhammadiyah Al-Mujahidin Elementary School, Wonosari, there are still some students who are less interested in music art subjects. This study aims to determine the effect of theValue Clarification Technique (VCT) learning model on the musical creativity of grade III students, the efforts made by class teachers to improve musical creativity. This study is a descriptive quantitative study using an experimental method. The research design used is Pre-Experimental Design in the form of a One-Group Pretest-Posttest Design design with a sample of 24 students. Data collection techniques use test and non-test techniques, such as observation, interviews, and documentation. Data is processed using instrument validation and instrument reliability. The results of this study can be seen through the results of the hypothesis test with the Paired Sample t-test technique processed using the IBM SPSS for Windows version 30.0 program, the Sig value (2 Tailed) was obtained at 0.000, which means the Sig value 0.05, which means H0 is rejected and H1 is accepted. So it can be concluded that the Value Clarification Technique (VCT) learning model has an effect on the student’s outcome’s ability to play music creativity of grade III students in Muhammadiyah Al-Mujahidin Elementary School, Wonosari
Stability Analysis of a Fractional-Order Lengyel–Epstein Chemical Reaction Model
Full text linkIn this paper, we stady a mathematical model based on a system of fractional-order differential equations to describe the dynamics of the Lengyel–Epstein chemical reaction, which is well known for exhibiting oscillatory behavior. The use of fractional derivatives allows in chemical processes compared to classical integer-order models. We specifically focus on analyzing the stability of the system’s positive equilibrium point by applying fractional calculus techniques. The stability conditions are derived and discussed in the context of the fractional-order parameters. To validate the theoretical findings, we perform numerical simulations using the Forward Euler method adapted for fractional-order systems. These simulations illustrate the impact of the fractional order on the system’s dynamic behavior and confirm the analytical results regarding equilibrium stability
Optimizing Small-Scale Wind Energy Generation: Site-Specific Wind Speed Analysis and Turbine Placement Strategies
Full text linkWind is an effective renewable power source suitable for localized electricity production when regional environmental factors have substantial impact on system output. The research studies the best wind turbine placement through wind speed variability studies conducted with calibrated anemometers and data loggers that assess site conditions. A data-based assessment method creates the research's main contribution which facilitates the optimization of wind power potential measurement for enhanced energy efficiency. The research methodology includes continuous Vantage Pro2 equipment together with anemometers at different heights for wind speed observation while performing accuracy-based calibration analysis. The research shows that elevating the turbine from seven meters to ten meters leads to a 12 percent growth in the amount of power produced. The power output of wind energy decreases as wind speed changes because of environmental conditions so proper installation locations become essential. Energy performance increases best when selecting sites which feature reliable and elevated wind speeds. This research provides useful knowledge about enhancing decentralized power generation through wind energy but it cannot be easily scaled up to bigger systems. The study demonstrates that specific site assessments together with practical recommendations will enhance the efficiency of small-scale wind energy systems