Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.
Determining the location of a strategic warehouse is a crucial decision in supply chain management as it directly affects distribution efficiency, logistics costs, and service levels. This problem is multi-criteria and complex, requiring an approach that can accommodate differences in the importance of criteria as well as variations in performance among alternatives objectively. This study aims to develop a Decision Support System to determine a strategic warehouse location by combining the Weights by Envelope and Slope (WENSLO) weighting method and the Ranking of Alternatives with Weights of Criterion (RAWEC) ranking method. The WENSLO method is used to generate criteria weights based on the nonlinear strength of each criterion, while the RAWEC method is applied to calculate the final values and determine the ranking of warehouse location alternatives. A case study was conducted on eleven alternative locations with the main criteria including location cost, accessibility, safety, distribution travel time, and proximity to suppliers. The study results showed that Location TR obtained the highest final score of 0.9673 and was designated as the top priority warehouse location, followed by Location RD with a score of 0.6235 and Location HO with a score of 0.338, while Location QC had the lowest score of −0.975. These findings demonstrate that the combination of the WENSLO and RAWEC methods can produce rankings that are objective, consistent, and easy to interpret, making them a reliable decision-support tool for determining strategic warehouse locations and potentially applicable to other logistics and distribution problems.
Krisis air bersih merupakan tantangan global yang juga dialami di Indonesia. Salah satu upaya strategis yang dapat dilakukan adalah menanamkan kesadaran hemat air sejak dini. Program Pengabdian Kepada Masyarakat ini bertujuan meningkatkan pengetahuan dan kesadaran siswa sekolah dasar tentang pentingnya menghemat air melalui pendekatan cerita (storytelling) dan media interaktif. Kegiatan dilaksanakan di SD Negeri 1 Jatimulyo, Kec. Jati Agung, Kabupaten Lampung Selatan, melibatkan 120 siswa kelas IV dan V serta 6 guru pendamping. Metode pelaksanaan meliputi penyusunan materi cerita tematik, pemutaran video animasi, kuis interaktif, pemasangan poster digital di area strategis sekolah, dan tantangan “7 Hari Hemat Air” untuk siswa. Hasil evaluasi menunjukkan peningkatan rata-rata pemahaman siswa sebesar 33% berdasarkan perbandingan skor pre-test dan post-test. Observasi lapangan dan wawancara guru menunjukkan antusiasme siswa yang tinggi, serta perubahan sikap positif terhadap penggunaan air di sekolah. Program ini terbukti efektif membentuk pemahaman, sikap peduli, dan kebiasaan hemat air yang berkelanjutan pada siswa sekolah dasar.
This research explores the connection between symbolic language and how emotions are portrayed in Taylor Swift’s selected songs: The Prophecy and The Smallest Man Who Ever Lived. Based on a descriptive qualitative approach, this investigation aims to explore how rhetorical devices, including metaphors, personification, irony, and hyperbole, serve as methods for conveying complex emotional states. The results suggest that within "The Prophecy," Swift utilizes metaphors, religious references, and reiteration to illustrate feelings of hopelessness, powerlessness, and acceptance of destiny. In contrast, "The Smallest Man Who Ever Lived" depends on the use of irony, overstatement, and explicit descriptions to convey sentiments of treachery, fury, and personal agency. These patterns of figurative speech demonstrate a shift from a state of weakness to one of resilience, indicating that Swift's songwriting leverages symbolic communication not simply to outline feelings but also to represent the development of emotional change in itself. The research determines that the integration of the psychological study of emotions and the linguistic study of figurative language allows for a more profound comprehension of how current songwriters generate significance and emotional impact through innovative lyrical composition.
This study aims to test and analyze the effect of Debt to Equity Ratio (DER) and Return on Equity (ROE) on the Value of Mining Sector Companies listed on the Indonesia Stock Exchange for the 2019-2021 period. The population in this study were 29 mining sector companies listed on the Indonesia Stock Exchange for the 2019-2021 period. The sample used in this study was a purposive sampling method totaling 33 companies after being multiplied by 3 years of research. The data analysis method in this study was panel data regression analysis with the help of Eviews Software version 10. Based on the results of this study, it shows that partially Debt to Equity Ratio (DER) has a negative and significant effect on the Value of Mining Sector Companies listed on the Indonesia Stock Exchange for the 2019-2021 period. Return on Equity (ROE) partially has a positive and significant effect on the Value of Mining Sector Companies listed on the Indonesia Stock Exchange for the 2019-2021 period. The results of the study also show that simultaneously the Debt to Equity Ratio (DER) and Return on Equity (ROE) have a significant effect on the Company Value of the mining sector listed on the Indonesia Stock Exchange for the 2019-2021 period.
