This study aimed to analyze the impact of strategic management, specifically marketing and creative-economy-based development strategies, on the competitiveness of culinary enterprises in Makassar. A quantitative research design was employed, using a survey to collect primary data from a representative sample selected through purposive sampling. The data were analyzed using Partial Least Squares Structural Equation Modeling to evaluate structural relationships and test the research hypotheses. The results indicated that creative economy-based marketing strategies had a significant positive effect on competitiveness. Furthermore, development strategies based on the creative economy were found to contribute even more significantly to enterprises' ability to strengthen their competitive position in the local market. These findings emphasize the necessity of adopting innovative and adaptive business models to thrive amid market dynamics. This study provides a solid foundation for enterprise actors and policymakers to enhance creative promotion and strategic development, ensuring long-term business sustainability and competitive advantage.
Early detection of heart disease is essential for supporting timely clinical intervention, improving treatment outcomes, and enhancing the quality of patient care. This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—combined with two feature selection methods, Chi-Square and Recursive Feature Elimination (RFE), using the UCI Heart Disease dataset. Six modeling scenarios were evaluated based on accuracy, precision, recall, and F1-score. The experimental results demonstrate that the Random Forest model achieved the best overall performance, with an accuracy of 85.2% and a recall of 97.0%, indicating a strong capability to identify patients with potential heart disease. To enhance model transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed as an Explainable AI (XAI) technique and integrated into a web-based decision support system to provide intuitive explanations of prediction outcomes. The proposed system is intended to serve as an initial clinical decision-support tool and is not designed to replace diagnosis or clinical judgment by healthcare professionals.
This study investigates the application of transfer learning and Explainable Artificial Intelligence (XAI) for multi-class skin disease classification. The dataset was obtained from the Kaggle Skin Diseases Image Dataset and consists of 29,153 original images spanning 10 skin disease classes. To reduce the bias introduced by class imbalance, the dataset was balanced through directed undersampling, resulting in 12,000 images, with 1,200 images per class. Three pretrained convolutional neural network (CNN) architectures—EfficientNetB0, ResNet50, and DenseNet201—were implemented and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results demonstrate that DenseNet201 achieved the highest classification performance, with an accuracy of 0.8779, precision of 0.8751, recall of 0.8748, and F1-score of 0.8745, outperforming ResNet50 (accuracy: 0.8629) and EfficientNetB0 (accuracy: 0.8269). Model interpretability was investigated using Grad-CAM, SHAP, and LIME. Grad-CAM highlighted that the models primarily focused on the central and peripheral regions of skin lesions during prediction. SHAP identified the dominant contribution of lesion regions and pigmentation patterns to the classification process, while LIME emphasized the importance of local superpixels associated with lesion boundaries, color, and texture in supporting the model's predictions. The findings indicate that combining transfer learning with Explainable AI provides a promising foundation for developing clinical decision support systems for dermatological image classification. Future research should incorporate external dataset validation, more robust class balancing strategies, and clinical interpretation by dermatology experts to facilitate the deployment of such systems in real-world healthcare settings.
Studies of Makassar-Bugis Islam generally explain the relationship between Islam and ancestral cosmology through the lens of syncretism. However, this approach has not yet explained how individuals reconcile these two traditions in their religious experience. This article aims to examine how the Sufi teachings of Syekh Yusuf al-Makassari bridge the tension between Makassar-Bugis ancestral cosmology and Islamic doctrine, using a framework referred to in this study as “Local Psychoanalysis.” The method employed is qualitative with a hermeneutic approach, specifically a side-by-side reading of selected episodes from the epic La Galigo (manuscript NBG 188) and Sinrilik Riwayat Tuanta Salamaka alongside three treatises by Sheikh Yusuf: Zubdat al-Asrar, Sirr al-Asrar, and An-Nafhat al-Sailaniyah. Lacanian psychoanalytic concepts are explored in dialogue with three local categories: Ininnawa, Sumangeq, and Pemali. The results of the study reveal a recurring three-stage pattern in the analyzed corpus, which was subsequently formulated as Ininnawa, Wangkang, and Sompeq. These findings suggest that the teachings of Manyeʾrea Syekh Yusuf function analogously to the concept of the sinthome in Lacanian thought that is, a knot that sustains the relationship between local commitments and Islam without dissolving one into the other. The contribution of this study is methodological in nature, offering Ininnawa, Manyeʾrea, and Sompeq as alternative interpretive categories for the study of the Islamization of the Indonesian Archipelago, which are expected to complement the framework of syncretism that has been used in similar studies to date
Tempering baja adalah suatu proses dimana baja sebelumnya keras atau normal biasanya dipanaskan pada temperatur dibawah temperatur kritis dan didinginkan dengan laju pendinginan tertentu, terutama untuk menambah keuletan (ductility) dan ketangguhan (toughness), tetapi juga untuk menambah grain size of matrix. Baja di temper dengan pemanasan kembali sesudah dikeraskan untuk mencapai nilai spesifik sifat mekanik dan juga untuk relieve quenching stress dan untuk menjamin dimensi yang stabil. Penelitian ini bertujuan untuk menentukan sifat mekanik dan struktur mikro pisau tradisional yang terbuat dari bering bekas, setelah melalui proses tempering 3500 C dan ditahan selama 60 menit. Pada penelitian ini menggunakan Universal Tensile Test 100 kN, dan Mikroskop optik metalografi.Adapun hasil dari penelitian ini adalah: (1) kekuatan tarik media pendingin Udara 1.500,172 N/mm2, Air 691.288 N/mm2, Oli 598.791 N/mm2, Air garam 633.394 N/mm2, Air Batang Pisang 126,524 N/mm2, dan metode patahannya yaitu patah getas. (2) Struktur mikro yang muncul yaitu, Ferrit, Perlit, Sementit, dan Martensit. Dari hasil analisa, dapat ditarik suatu kesimpulan bahwa kegetasan serta keuletan baja, dipengaruhi oleh struktur mikro yang muncul karena adanya perlakuan yang diberikan sebelumnya. Tempering is a heat-treatment process in which previously hardened or normalized steel is reheated to a temperature below the critical range and then cooled at a controlled rate. The primary purpose is to improve ductility and toughness, while also stabilizing the microstructure and relieving residual stresses induced by quenching to ensure dimensional stability. This study aims to evaluate the mechanical properties and microstructure of traditional knives manufactured from used bearing steel after tempering at 350°C with a holding time of 60 minutes. Mechanical testing was carried out using a 100 kN Universal Tensile Testing Machine, and the microstructure was examined using an optical metallographic microscope. The tensile strength results for each cooling medium were: air cooling 1,500.172 N/mm², water 691.288 N/mm², oil 598.791 N/mm², salt water 633.394 N/mm², and banana-stem water 126.524 N/mm². The observed fracture mode was predominantly brittle fracture. Microstructural observations revealed the presence of ferrite, pearlite, cementite, and martensite. The analysis indicates that the brittleness and ductility of the steel are strongly influenced by the resulting microstructure formed due to the applied heat-treatment conditions.
