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Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics

The selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

This study aims to investigate the effects of tamarind leaf extract (Tamarindus indica L.) on neuropharmacological activity in mice using calculated percentages of responses based on the parameters PSM, SSSP, DSSP, SL, RO, SM, PSL, and ANA. This research was conducted as a laboratory experiment using a completely randomized design (CRD) with three treatment concentrations: 1%, 2%, and 4%. Observations were performed to assess the percentage of activity produced by each sample concentration, followed by descriptive–quantitative analysis to determine the dose–response pattern. The results showed that tamarind leaf extract produced varying responses across concentrations. The SSSP, ANA, and RO effects demonstrated increased activity at the 2% concentration, whereas other parameters (PSM, PSL, SM, SL, and DSSP) showed decreased activity. Interestingly, the SSSP parameter exhibited a positive dose–response pattern with the highest activity of 55.84% at 2%. Overall, the effectiveness of tamarind leaf extract depends on the concentration level. The 2% concentration appears to be the optimal dose for several neuropharmacological effects, while the 4% concentration was most effective only for SSSP. These findings highlight the importance of multi-concentration testing to determine effective dosing of natural products for biological applications and the need for further investigation.

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.

The selection process for prospective cadets at a maritime college is routinely carried out every year. In the current system, the processing and calculation of prospective cadet scores uses Microsoft Excel tools, but this takes a long time and can lead to errors in the calculation of scores and the recapitulation of selection results. In addition, unintegrated data can lead to data duplication, resulting in inaccurate selection results that can be detrimental to prospective cadets. A fast, precise, and accurate integrated decision support system is needed to support the selection process for prospective cadets. The author conducted a study on the prospective cadet admission system at UNIMAR AMNI Semarang using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to assist in the selection of qualified prospective cadets who meet the established criteria. With this decision support system using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method, it can be used to solve the selection problem for prospective cadets because it has a short calculation time, is simple, transparent, and has high flexibility. This system will be built using the Codeigniter Framework and a MySQL database.

N the rapidly evolving digital era, decision-making has become a critical aspect across various fields, including education, where choices such as selecting an Islamic boarding school (pondok pesantren) are often influenced by complex and subjective factors. This study addresses the dilemma faced by Diyah, a junior high school student, in determining the best reasons for choosing Pondok Pesantren Darularafah Raya, highlighting the limitations of manual, personal-based processes that fail to systematically consider measurable criteria like educational quality, learning environment, facilities, discipline, and instilled religious values. The advancement of information technology provides a solution through Decision Support Systems (DSS), utilizing the ORESTE (Organization, Rangement Et Synthèse De Données Relationnelles) method, which effectively processes ordinal data to produce objective rankings based on subjective yet structured preferences. Unlike other methods such as SAW or AHP that rely on numerical data, ORESTE emphasizes relative preference weights, making it suitable for individual decision-making contexts like educational choices. The novelty of this research lies in applying ORESTE in a DSS focused on analyzing an individual's best reasons for selecting a pesantren, aiming to reduce subjective bias and enhance rationality. The primary objective is to develop a DSS using the ORESTE method to analyze and determine Diyah's optimal reasons for choosing the pesantren. Through this, the system is expected to accelerate evaluation processes, improve objectivity, and identify dominant factors influencing educational decisions. Findings from the implementation demonstrate accurate rankings that prioritize key criteria, leading to more efficient and data-driven outcomes. Implications include aiding students, schools, and educators in understanding influential factors, fostering objective assessment systems, and serving as a reference for future studies integrating MCDM methods with computer-based systems in personalized educational decision-making.

Lecturers are a key element in improving the quality of education in higher education. Effective assessment of lecturer performance is essential for evaluation and improvement of education quality. Instituito Profissional de Canossa (IPDC) evaluates lecturer performance through manual methods involving students, this process often produces inaccurate data and slows down decision making. For this reason, a decision support system is needed that can accommodate all the criteria needed in decision making using the promethee method to assess lecturer performance. Promethee was chosen in this research because of its ability to consider data characteristics and provide more accurate results. This DSS will consider three assessment criteria, including education, community service and research. The results of the DSS implementation with the Promethee method can provide recommendations for lecturer performance rankings that help the quality assurance team in identifying outstanding lecturers.

Choosing a strategic business location is a key factor in building a successful enterprise, especially in Lautem Municipality, Timor-Leste. This area presents various opportunities for businesses such as grocery shops, small restaurants, repair workshops, and agricultural ventures. However, entrepreneurs often face challenges like poor road infrastructure, limited purchasing power, and inadequate transport access. This study developed a web-based Decision Support System (DSS) using the ELECTRE method to help business owners select the most suitable location. The system evaluates multiple location options based on important criteria such as population density, transportation access, and economic activity. By using this approach, entrepreneurs can reduce the risk of choosing an unprofitable location and improve their chances of success. The results show that implementing the ELECTRE method in a DSS is effective for identifying optimal business sites. These findings are expected to serve as a practical reference for entrepreneurs in Lospalos and nearby areas, while also supporting local economic development in the region.

The Decision Support Sistem (DSS) for selecting exemplary teachers at SMP Negeri 2 Loli aims to assist the school in determining teachers who deserve to be recognized as exemplary teachers. The evaluation of exemplary teachers involves various criteria such as teaching performance, discipline, creativity, and communication skills with students. However, the selection process conducted manually tends to be subjective and time-consuming. Therefore, a sistem is needed to simplify and accelerate the selection process while producing objective and transparent results. This sistem is developed using the Simple Additive Weighting (SAW) method, which is one of the techniques in multi-criteria decision-making. The SAW method works by assigning weights to each predetermined criterion and calculating the weighted scores for each alternative (teacher). These weighted scores are then summed to determine the ranking order of teachers who meet the criteria as exemplary teacher candidates. The results of this study indicate that the implementation of the SAW method can produce a more structured, objective, and efficient exemplary teacher selection process. The sistem facilitates the school in conducting evaluations and decision-making, while also reducing the potential for errors that may occur during manual selection. Thus, this decision support sistem is expected to make a significant contribution to improving the quality and accuracy of exemplary teacher assessments at SMP Negeri 2 Loli.

Employee performance appraisal is a crucial aspect of human resource management and development, as the evaluation results serve as the basis for managerial decision-making, including promotions, incentives, and career development. However, unstructured assessment sistems often hinder the objectivity, transparency, and accuracy of performance evaluations. This study aims to design and develop a web-based Decision Support Sistem (DSS) to assess the performance of Biller employees at the ULP PLN Kuala office by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS was selected because it effectively addresses multi-criteria decision-making problems by comparing each alternative to both positive and negative ideal solutions. The evaluation criteria include invoice achievement, account return balance, and PAL balance. The sistem was developed using PHP as the programming language and MySQL as the database, ensuring integration and accessibility. The test results indicate that the sistem is capable of providing employee rankings in an objective and measurable manner. Based on the calculation, Tri Widodo achieved the highest score (0.8027), demonstrating the best performance among the evaluated employees. The implementation of this TOPSIS-based DSS makes the performance appraisal process more sistematic, efficient, transparent, and accurate, thereby contributing to the improvement of human resource management quality within the organization.

The verification and validation process for land application data at the Southwest Sumba Regency Land Office has been carried out manually, which often results in delays, data inconsistencies, and the potential for errors in decision-making. These problems impact the low efficiency of public services and the accuracy of managed data. Therefore, this study aims to develop a Decision Support Sistem (DSS) that can assist the verification and validation process for land application data sistematically, quickly, and objectively. This sistem is built using the Simple Additive Weighting (SAW) method, which functions to determine the level of eligibility of an application based on several criteria, namely completeness of documents, land status, legality of ownership, suitability of applicant data, and dispute history. The sistem design process is carried out through several stages, including needs analysis, design, implementation, testing, and evaluation. The results show that the implementation of a SAW-based DSS can increase the efficiency of the verification process by up to 65%, and increase the accuracy of validation results by up to 90% compared to manual methods. This sistem provides automatic decision recommendations, thus assisting officers in assessing the eligibility of land applications in a transparent and consistent manner. Thus, this sistem has the potential to be an effective solution to support the digitalization of public services in the land sector in Southwest Sumba Regency.

Modern Islamic boarding schools not only focus on religious education, but are also required to improve the academic quality of students, including in mathematics subjects which are often challenging due to differences in students' learning styles, motivations, and abilities. This study aims to design and implement a Decision Support Sistem (DSS) based on the VIKOR method to analyze students' mathematical understanding and provide recommendations for more adaptive learning strategies. The study uses a descriptive quantitative approach with data obtained through documentation of students' grades, as well as interviews with mathematics teachers. The VIKOR method is used to evaluate students' understanding based on five main criteria, namely exam scores, report card scores, attendance, attitudes and behavior, and skills, through a process of normalization, weighting, calculation of S, R, and Q scores, and ranking students. The results show that there are five students with the lowest level of mathematical understanding, namely A11, A4, A17, A1, and A12, who have the lowest VIKOR index scores. This low level of understanding is influenced by factors such as learning attitudes, attendance, report card scores, and skills, so they require further attention and guidance from teachers. The application of the VIKOR method in SPK has proven effective in optimizing the analysis of students' mathematics achievements and providing recommendations for more targeted personal learning strategies, thereby helping to improve the quality of education and maximize students' academic potential.

The development of information technology has brought significant changes to the medical device procurement process, particularly within government institutions such as the Health Office. The procurement of appropriate, efficient, and objective medical devices is crucial to supporting optimal medical services, yet the decision-making process is often constrained by limited budgets and the complexity of multiple assessment criteria. This study aims to design and implement a decision support system (DSS) based on the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to provide recommendations for medical device procurement at the Binjai Health Office. The DSS evaluates six main criteria: price, quality, durability, ease of maintenance, medical necessity, and safety level, using procurement data from the 2022–2024 period. The TOPSIS method is applied to calculate the relative closeness of each alternative to the ideal solution, enabling decision-makers to rank medical device options objectively and systematically. The findings show that the DSS successfully prioritizes procurement alternatives, helping stakeholders allocate budgets more effectively and transparently. In addition, the system minimizes subjective bias by integrating quantitative analysis with clearly defined criteria. The system is implemented in a web-based environment with MySQL as the database, ensuring accessibility and scalability for future use. Overall, this research demonstrates that integrating TOPSIS into a decision support system can enhance the efficiency, accuracy, and accountability of medical device procurement in public health agencies. The study is expected to contribute to improving budget management and strengthening the quality of health services through better resource allocation.

Employee performance appraisal is an important process in human resource management that aims to evaluate individual work achievements based on certain criteria set by the organization. This process not only serves to assess the extent to which an employee meets work standards, but also serves as a basis for strategic decision-making, such as job promotions, bonus awards, and career development planning. However, in practice, CV. Surya Perkasa Makassar faces serious obstacles in the form of subjectivity in the assessment process, because the benchmarks used still tend to be based on the likes or dislikes of superiors. This causes the evaluation results to be less objective, inconsistent, and potentially reduce employee work motivation. To overcome these problems, this study aims to develop a decision support system for employee performance appraisal using the Tsukamoto Fuzzy Logic method. This method was chosen because it is able to accommodate uncertainty in the assessment, resulting in more objective, measurable, and consistent decisions. This study uses a Research and Development (R&D) approach with a Black Box Testing method to ensure system functionality. The assessment criteria used include five main aspects, namely work quality, work quantity, discipline, responsibility, and cooperation. Data from these criteria is processed through fuzzification, inference, and defuzzification stages to obtain the final employee performance score. Test results indicate that all system features function as expected. The system is able to prevent data duplication, validate input, and produce accurate final performance scores. The implementation of the Tsukamoto Fuzzy Logic method has proven effective in reducing the level of subjectivity that typically occurs in manual assessments. Therefore, this system can be used as a reliable tool in managerial decision-making, both regarding promotions, bonus awards, and planning employee future career development.

The Electronic Traffic Law Enforcement (E-Tilang) system has been implemented in Jakarta as an innovative solution to address the increasingly complex problems of traffic law enforcement. This research aims to evaluate the effectiveness of the E-Tilang system's implementation by analyzing public compliance levels and measuring the system's maturity using the COBIT 2019 framework. The research method uses a quantitative approach with a survey of 113 Jakarta residents who have experience with the E-Tilang system. Data was collected through a structured questionnaire mapped to the five COBIT 2019 domains: Evaluate, , Plan and Organize (APO); Build, Acquire and Implement (BAI); Deliver, Service and Support (DSS); and Monitor, Evaluate and Assess (MEA)

In the era of increasingly advanced technological development, decision support systems (DSS) play an important role in improving the quality of decision-making in the education sector. The selection staff at SMP Dharma Bakti find it difficult to objectively determine the best academic students because they still use a manual method that is prone to errors. This research aims to design a web-based decision support system that can help selection staff assess and determine their best academic students objectively and quickly. This system is developed using the PHP programming language, MySQL database, LARAVEL 8 framework, and applies the Analytical Hierarchy Process (AHP) method within it. This research produces a system that allows the SMP Dharma Bakti Selection Officers to input alternative data, namely student data, determine the weight of criteria and sub-criteria, and view the ranking results automatically, thus greatly facilitating the SMP Dharma Bakti Selection Officers in determining their best academic students.

The Indonesian Law No. 11 of 2012 concerning the Juvenile Criminal Justice Sistem (SPPA Law) established Diversion and Restorative Justice as core pillars for handling Children in Conflict with the Law (CICL). The Public Prosecutor (JPU) plays a mandatory role in attempting Diversion, as stated in Article 7(1) of the SPPA Law. However, the implementation of Diversion is hindered by judicial limitations, such as restrictions on crimes with a prison sentence of less than seven years and the lack of specific government regulations (PP). These barriers often deny children the opportunity for Diversion, exposing them to the formal justice process and stigmatization. This study analyzes the challenges within the JPU’s authority regarding Diversion and proposes legal reforms to strengthen their role. Using normative legal research and a conceptual approach, the study examines primary and secondary legal materials and suggests the implementation of a Digital Decision Support Sistem (DSS) to guide prosecutorial discretion. The findings reveal that restrictive penal criteria and the absence of a review mechanism for rejected Diversion cases undermine Restorative Justice efforts. The paper argues that legal reforms are necessary to revise penal limitations, develop a detailed regulatory framework, and implement an integrated information sistem to support rehabilitation programs, ensuring the best interests of children and reducing reoffending.

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).