Abstract:This study aims to analyze in depth the implementation of islamic business ethics in the sale and purchase of classic Vespa motorcycles at Garage Scooter krangkeng, Indramayu Regency, trough the perspective of Sharia Economic…
nomic Law. As part of sociological phenomenon and contemporary automotive creative economy, transactions of used motorcycles with high historical value are often faced with technical information asymmetry between business actors and consumers. This study uses a qualitative-descriptive method with a normative-empirical case study approach. Primary data were collected directly from the field through participatory observation and in-depth interviews with Garage Scooter owners and consumers, while secondary data were obtaied through participatory observastion and-depth interviews with Garage Scooter owners and consumers, national regulitions. The result of the study indicate thet transaction practices at Garage Scooter Kangkeng are carried out through a direct sale and purchase scheme (bal’ musawamah) and an indent system for ordering modification restorations. Business actors have implemened basic islamic business ethics values such as honesty (shiddiq0, trustworthiness, transparancy, and reponsibility for product qualit. However, crucial loopholes are still found in the form of weak black and white doumentation (written contracts), which opens up the potential fo uncertainty (gharar) and manipulation of minor information (tadlis)related to detalled machine specifacitions and the accuracy of restoration times. Viewed form Shari Economic La, the validity of The pillar and conditions of commodity objects requires strengthening after-sales technical tansparancy through written instruments (al-kitabah) to uphold the principle of mutual consent (an-tardhin) and the right to choose (khiyar al-‘aib) in order to achieve tru benefit (maslahah).
Abstract:This study aims to analyze the influence of packing machine automation on operator productivity in the facial tissue industry by considering the role of moderation, technical competence and operator workload perception.…
The method used is Systematic Literature Review (SLR) by reviewing scientific articles published in the 2020–2026 range from reputable databases such as Scopus. The selection process was carried out using the PRISMA approach through the identification, screening, eligibility, and inclusion stages, resulting in a number of articles relevant to the topics of industrial automation, labor productivity, technical competence, and workload. The results of the study show that the implementation of packing machine automation in general has a positive impact on increasing operator productivity through time efficiency, quality consistency, and reduction of manual errors. Nevertheless, the effectiveness of automation is highly dependent on the level of technical competence of the operator, especially in the operation, maintenance and troubleshooting of the machine. In addition, workload perception has also been shown to moderate the relationship, where automation can lower physical workloads but potentially increase mental workloads due to the demands of automated system supervision. Other findings suggest that an imbalance between automation levels and human resource readiness can hinder productivity optimization. Conceptually, this study confirms that the relationship between packing machine automation and operator productivity is not linear, but is influenced by individual and psychological factors. The practical implications of this study are the importance of technical competency-based training as well as adaptive workload management in supporting the successful implementation of automation in the manufacturing industry. This research contributes to the development of an integrative model that connects technology, people, and operational performance in the context of the tissue processing industry.
Abstract:The fast food industry continues to grow as the public's need for practical products, including Kamumu Kimpul chips, increases. However, small business actors such as UD. Sona in Gunungsitoli Idanoi faces obstacles in production…
oduction due to the use of traditional methods, limited equipment, and lack of optimal management. This condition has an impact on delays in meeting demand, increasing production costs, and declining competitiveness. Therefore, a production optimization strategy is needed to improve product capacity, efficiency, and quality. This study aims to analyze the production optimization strategy implemented by UD. Sona, formulate a capacity building strategy, as well as identify obstacles and solutions that can be done. The results of the research are expected to be practically useful for business actors in production management, as well as make a theoretical contribution to the development of production management science in MSMEs. The research method uses a descriptive qualitative approach. Data was obtained through interviews with owners, employees, and customers, supplemented by observation and documentation. Data analysis is carried out through reduction, presentation, and inductive conclusions, so as to be able to describe the real conditions of the business and develop the right optimization strategy. The results of the study show that UD. Sona has made efforts such as setting up production flows and scheduling planning. However, limited machinery, unstable supply of raw materials, and marketing that has not been maximized are still obstacles. Consumers rate the product as good quality, but its availability has not been consistent. Suggested strategies include the implementation of lean production, improvement of inventory management, and the use of production technology
Abstract:Production optimization is a key to increasing efficiency and profitability, especially in businesses such as UD. Tahu Nias in Hiligodu Ombolata Village, Gunungsitoli City, which operates in the tofu production sector. This…
his research is motivated by the production challenges faced by the company, such as limited equipment, late raw material supplies, and an unskilled workforce, which impact the inefficiency of the production process and decrease the level of profitability. The method used in this study is a qualitative descriptive approach with data collection techniques through interviews, observation, and documentation. Informants consisted of the owner, production employees, and other support staff. The results of the study indicate that the production process at UD. Tahu Nias still has manual stages with limited use of machines, especially only at the soybean milling stage. The main obstacles in optimizing production include late raw material supplies, the lack of technology and training for employees. However, the company has made several efforts such as strategic raw material management and efficient division of labor among employees. The conclusion of this study is that production optimization at UD. Nias tofu can be improved through the use of advanced equipment in more modern production facilities, employee skills development through training, and improved production and distribution planning. By implementing these strategies, the company has the potential to sustainably increase operational efficiency and profitability.
Abstract:In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing…
prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Abstract:Artificial Intelligence has become a transformative technology in the field of financial technology, leveraging advanced algorithms and machine learning to identify risks and make informed decisions. However, its widespread…
ead adoption presents new challenges related to ethical use, data privacy, security concerns, potential bias, and discrimination. This study aims to explore the benefits of AI-based risk management in Fintech while highlighting associated challenges and providing recommendations. This research utilises the systematic review methodology to analyse existing literature and identify important patterns, gaps, and areas for further investigation. The study utilised data gathered from the Scopus database to obtain credible scholarly materials. Research data was collected from a variety of countries including the United States, China, European nations, and other Asian countries in order to develop a comprehensive understanding of AI-based risk management on a global scale. The findings highlight the crucial role of ethical considerations in implementing AI-based risk management systems to ensure fairness, transparency, and accountability. Moreover, the fintech industry needs to establish strong data protection measures and address issues related to bias and discrimination in order to instil trust and uphold public confidence in AI-based risk management. Future research should emphasise assessing the effectiveness of different algorithms and approaches while also examining potential regulatory frameworks and legal implications associated with AI-based risk management strategies.
Abstract:Penggunaan dompet digital yang terus meningkat menghasilkan banyak ulasan pengguna yang dapat dimanfaatkan untuk mengevaluasi kualitas layanan. Penelitian ini bertujuan meningkatkan akurasi klasifikasi sentimen pengguna…
dompet digital menggunakan metode Stacking Ensemble Machine Learning yang mengombinasikan Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), dan AdaBoost dengan Logistic Regression sebagai meta-learner. Data ulasan diproses melalui tahapan text preprocessing meliputi case folding, cleaning, tokenizing, stopword removal, stemming, dan pembobotan fitur menggunakan TF-IDF. Penyeimbangan data dilakukan dengan SMOTE, sedangkan evaluasi model menggunakan 5-Fold Cross-Validation. Hasil penelitian menunjukkan bahwa model Stacking Ensemble memperoleh akurasi rata-rata 80,55%, lebih tinggi dibandingkan algoritma dasar. Evaluasi menggunakan Confusion Matrix, Classification Report, dan ROC Curve juga menunjukkan peningkatan nilai precision, recall, F1-score, dan kemampuan diskriminasi model. Hasil ini menunjukkan bahwa pendekatan Stacking Ensemble Machine Learning efektif untuk meningkatkan akurasi klasifikasi sentimen pengguna dompet digital serta mendukung evaluasi kualitas layanan berbasis opini pengguna.
The rapid growth of digital wallet usage has generated a large volume of user reviews that can be utilized to evaluate service quality. This study aims to improve the accuracy of digital wallet user sentiment classification using a Stacking Ensemble Machine Learning approach that combines Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and AdaBoost with Logistic Regression as the meta-learner. User reviews were processed through text preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, stemming, and TF-IDF feature weighting. Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance, while model performance was evaluated using 5-Fold Cross-Validation. The experimental results show that the proposed Stacking Ensemble model achieved an average accuracy of 80.55%, outperforming the individual base learners. Furthermore, evaluations based on the Confusion Matrix, Classification Report, and Receiver Operating Characteristic (ROC) Curve demonstrated improvements in precision, recall, F1-score, and the model's discriminative capability. These findings indicate that the proposed Stacking Ensemble Machine Learning approach is effective in improving the accuracy of digital wallet user sentiment classification and can serve as a reliable tool for supporting service quality evaluation based on user opinions.
Abstract:One of the steps in the tyre manufacturing process is the Tyre Building Machine. The tread is the last component to be fitted to the tyre carcass after all other components have been installed. When measuring the length…
of the pre-adjusted tread, errors or variations often occur in the measurements taken by the measuring device due to its lack of accuracy. This prevents operators from using the tyre tread and can result in a large amount of scrap and wasted time during the tyre manufacturing process. To overcome this problem, a redesigned tread length measurement system with a higher level of accuracy was created to minimise tread length fluctuations during tyre manufacturing. The results of the tread length measurement procedure using the ENC-1-1-24-N type rotary encoder can function according to the program created. This is validated by the tread length measurement data. The results of the HMI display design and the software created can operate according to the desired instructions. To change the tread length, the operator only needs to enter the number on the HMI panel.
Abstract:This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications,…
applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.
Abstract:This study aims to assess the impact of the use of modern Alsintan on rice farming. rice farming in Maradekaya Village, Bajeng District, Gowa Regency. The informant retrieval technique in this study used the purposive sampling…
mpling method, where informants were determined intentionally. The level of adoption of modern agricultural machinery in Maradekaya Village is quite high, including the use of 4-wheel tractors, rice transplanting machines, and rice harvesting machines, which certainly greatly help the lives of farmers. The impact of using modern agricultural machinery brings many changes to the activities of rice farmers, such as a reduction in time and labor, a reduction in capital, and an increase in productivity that is more profitable than before using agricultural machinery technology. Farmers can more easily divide their time for other work besides farming with modern agricultural machinery. In addition, modern agricultural machine tools (Alsintan) make rice farmers' work time more efficient than before the existence of this technology in Maradekaya Village.