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The Indonesian Archipelagic Sea Lane II (ALKI II) constitutes a strategic route stretching from the Sulawesi Sea to the Indian Ocean with trade values reaching USD 1.5 million daily and traversed by more than 36,773 vessels annually. The complexity of maritime security threats in this region has increasingly intensified following the relocation of the capital city to East Kalimantan, thereby demanding an effective decision support system to assist Indonesian Warship Commanders in making operational decisions that are rapid, accurate, and in accordance with applicable legal provisions. This research employs a qualitative approach through in-depth interviews with four maritime expert sources, with data analysis using NVivo 12 software. Analysis results identified maritime operational complexity and the urgent need for a comprehensive decision support system. These findings subsequently served as guidance in designing a Decision Support System (DSS) with a tab-based interface that integrates international legal frameworks such as UNCLOS 1982, Law No. 3/2025 on TNI, and related national regulations to generate structured action recommendations along with their legal foundations. Research findings demonstrate that the developed DSS successfully integrates operational, legal, and situational aspects to support decision-making that is rapid, accurate, and legally compliant. This system has proven to enhance the effectiveness of Indonesian Warship Commanders in addressing the complexity of maritime security (KAMLA) challenges in ALKI II through the provision of legally-based action recommendations from national and international frameworks accessible in realtime during operational situations.  

The selection process for accepting new employees is one of the important stages in a company to ensure that the candidates accepted have qualifications that suit the company's needs. At WINMART, the selection process is still carried out manually, so it is less efficient and prone to errors. Therefore, a system is needed that can assist in more objective and efficient decision making. This Decision Support System (DSS) is designed to assist the selection process for recruiting new employees using the Simple Additive Weighting (SAW) method, which can assess several relevant criteria, such as work experience, education, skills and competency tests. This system was built on a web basis, so it can be easily accessed by parties involved in the selection process, such as HRD and managers. The SAW method was chosen because of its ability to convert various subjective criteria into more objective numerical scores, so that selection results can be more transparent and accountable. By using this system, it is hoped that it can increase efficiency, accuracy and transparency in the new employee selection process at WINMART, as well as facilitate decision making in selecting candidates who best suit the desired criteria.

Rice plants (Oryza Sativa L.) are the main staple food commodity in Indonesia, as most of the Indonesian population relies on rice as their primary food. One of the causes of low rice production in Indonesia is that farmers generally cultivate rice improperly, such as in land preparation or land selection. Land suitability in rice cultivation greatly affects crop productivity. A process that can support decisions regarding rice land suitability is the development of a Decision Support System (DSS) website using a combination of the Simple Additive Weighting (SAW) method and the Technique for Order Performance of Similarity to Ideal Solution (TOPSIS). This combination is performed by taking the average (µ) of the final results from the SAW and TOPSIS methods. The final scores of each method are calculated separately, and then the average (µ) of these two results is taken to obtain the final ranking of the alternatives. The data used to determine the suitability of rice land is based on five criteria: soil type, soil pH, rainfall, temperature, irrigation and water supply. The alternative data used in the study includes six alternatives: Sungai Kunjang, Sambutan, Samarinda Utara, Palaran, Loa Janan Ilir, and Samarinda Seberang. The aim of this research is to provide information on alternative solutions to farmers or farmer groups in determining rice land suitability. The results of the combination of the SAW and TOPSIS methods show that the alternative with the highest final score is Samarinda Utara (A3), with a final score of 0.7337. Meanwhile, the alternative with the lowest final score is Sambutan (A2), with a final score of 0.4402.

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.

The rapid growth of short-form social media platforms has increased the complexity of decision-making during the digital content planning stage. Content creators are required to evaluate the feasibility of content ideas and determine platform suitability prior to production, while most existing tools primarily focus on post-publication analytics. This study aims to design an Artificial Intelligence (AI)-enabled Decision Support System (DSS) to evaluate digital content ideas in the pre-production stage. Adopting a Design Science Research approach, the study develops a conceptual design artifact that integrates intrinsic content idea characteristics with cognitive and affective response modeling grounded in the Stimulus–Organism–Response (S-O-R) framework, alongside platform affordance mapping. The proposed artifact operationalizes a reflective evaluation mechanism that generates platform recommendation scores and idea enhancement suggestions without claiming deterministic or predictive performance modeling. Evaluation was conducted qualitatively through practitioner assessment to examine perceived usefulness, clarity of recommendations, and decision support contribution. The findings indicate that the developed artifact provides a structured reflective framework for early-stage content evaluation. Theoretically, this study extends the application of the S-O-R framework by operationalizing it as a design logic for a pre-production DSS artifact. Practically, the proposed system has the potential to support more systematic decision-making prior to content production.

Best graduate selection is crucial for academic achievement and contributes to the accreditation value of the institution. Instituto Profissional de Canossa (IPDC) is a higher education institution founded by the Canossian Sisters in Timor-Leste. To improve the effectiveness of assessment and decision-making processes, an information system is needed to assist in selecting the best graduates based on multiple criteria. This research develops a web-based Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method. MAUT is a multi-criteria decision-making method that evaluates alternatives based on their utility scores across several criteria. The study uses four main criteria: attendance, academic performance, ethics, and discipline. The system is implemented as a web application for universal access. The MAUT calculation results provide valid and accurate recommendations for the best graduates. System testing showed that the application successfully ranked candidates based on defined weights and criteria, providing objective and consistent selection results.

Penelitian ini bertujuan untuk mengevaluasi Sistem Surat Keterangan Pendamping Ijazah (SKPI) di Universitas Semarang melalui audit dengan menggunakan kerangka kerja COBIT 2019, dengan fokus pada domain Deliver, Service and Support (DSS) dan Monitor, Evaluate and Assess (MEA). SKPI berfungsi sebagai dokumen resmi yang memberikan informasi tambahan mengenai kompetensi lulusan di luar nilai akademik (ijazah), sehingga penting untuk memastikan kualitas dan relevansinya dengan kebutuhan industri. Metodologi yang digunakan dalam penelitian ini meliputi pengumpulan data primer melalui observasi, wawancara, dan kuesioner, serta data sekunder dari literatur terkait. Hasil penelitian menunjukkan bahwa tingkat kapabilitas pada sub-domain DSS dan MEA berada pada level 4 yang dilabeli sebagai terkelola, dengan nilai rata-rata masing-masing 3,73 untuk DSS dan 3,85 untuk MEA. Meskipun demikian, terdapat sejumlah rekomendasi untuk meningkatkan nilai Maturity Level sistem, dengan GAP masing-masing sebesar 1,07 untuk DSS dan 1,04 untuk MEA. Rekomendasi yang disampaikan meliputi peningkatan kompetensi petugas teknis, pengembangan aplikasi mobile, dan sosialisasi prosedur penyajian SKPI secara digital. Dengan adanya rekomendasi tersebut, diharapkan dapat memberikan masukan positif dalam pengelolaan SKPI di Universitas Semarang dan meningkatkan daya saing lulusan di pasar kerja.Kata Kunci: Audit Sistem, Maturity Level, Rekomendasi, Deliver, Service and Support (DSS), Monitor, Evaluate and Assess (MEA)

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.

The extract of Carica papaya L. leaves contains phytochemical compounds such as flavonoids, alkaloids, phenolics, and saponins that play a role in biological activities, including antioxidants, anti-inflammatory, analgesic, and modulation of the nervous system. This study aims to evaluate the effects of fresh papaya leaf extract on the central nervous system (CNS) and autonomic nervous system based on varying extract concentrations. The study used test animals with treatment groups consisting of negative control, positive control, and extracts at concentrations of 4%, 8%, and 16%. Parameters observed included parasympathomimetic (PSM), parasympatholytic (PSL), sympathomimetic (SM), sympatholytic (SL), analeptic effects (ANA), CNS stimulation (SSSP), CNS depression (DSSP), and muscle relaxation (RO). The results showed that the 16% concentration produced the highest PSM activity (46.82%) and dominant CNS stimulation (38.59%), while DSSP and RO decreased at higher concentrations. This phenomenon indicates that increasing the extract concentration does not always enhance CNS depression, but rather leads to stimulation. These findings support the potential of papaya leaf extract as a neuroprotective and multifunctional agent. Further research is needed to confirm its molecular mechanisms and clinical effectiveness as a phytotherapy candidate.

This study compares two service management models to evaluate the governance of the Wi-Fi network in Dusun Gita Nagari Baru. The main objective is to measure user satisfaction and service quality following the implementation of the COBIT 2019 framework, particularly the DSS02 domain (Manage Service Requests and Incidents). The research employed a mixed methods approach, using historical-comparative document analysis and Likert scale questionnaires distributed to 21 active users. The data were analysed through gap analysis, capability level mapping, and descriptive statistical analysis to identify performance differences between two periods. The results indicate that most indicators in the COBIT 2019 capability model are at Level 4 (Predictable), one indicator reaches Level 5 (Optimising), and another indicator is at Level 3. Indicators directly related to the DSS02 domain, such as ease of reporting, response speed, schedule accuracy, and repair time, demonstrate the most significant improvements. These findings support the hypothesis that implementing COBIT 2019-based governance for DSS02 can enhance user satisfaction and the quality of Wi-Fi network services in rural areas. This study also provides practical recommendations for the sustainable management of digital infrastructure in areas with limited access.

In the era of globalization and digital transformation, organizations strive to maintain competitiveness through optimizing internal factors such as work culture, business process engineering (BPR), and decision support systems (DSS). This qualitative research based on a Systematic Literature Review (SLR) analyzes the relationship between these three using the Denison Organizational Culture Model, Hammer & Champy BPR, and the Resource-Based View (RBV). It finds that work culture mediates the implementation of BPR and DSS to increase efficiency by 30-50%, employee productivity, and sustainable competitive advantage. Case studies such as the Toyota Production System (TPS) confirm this synergy, resulting in reduced costs, cycle times, and improved service quality. This research also emphasizes the importance of a strong work culture in supporting the implementation of new technologies and methodologies, which in turn strengthens the company's market position. Thus, organizations that are able to integrate these three elements will be better prepared to face the challenges of globalization and dynamic changes in their industry, creating a sustainable advantage and being able to survive in a highly competitive market.

The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.

The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.

Student selection for the National Student Sports Olympiad (O2SN), particularly in the karate category, is still largely conducted manually, which may lead to subjectivity and inconsistency in the assessment process. This study aims to design and develop a web-based Decision Support System (DSS) to assist schools in selecting the best students for the O2SN karate competition by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at SMA Negeri 2 Muaro Jambi. The system was developed using the waterfall model, which consists of requirement analysis, system design, implementation, and testing stages. The student evaluation process is based on criteria referring to the official O2SN karate standards, including stance accuracy, techniques, movement transitions, timing and harmony, breathing control, focus (kime), conformity (consistency with ryu-ha/style), strength, speed, and balance. The developed system processes assessment data to generate preference values and student rankings, which are separated by male and female categories. The results indicate that the application of the TOPSIS method is able to support more objective decision-making and improve transparency and efficiency in the O2SN student selection process within the school environment.

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.

The puring plant (Codiaeum variegatum), which has been known as an ornamental plant, apparently has significant therapeutic potential, supported by the presence of secondary metabolites such as flavonoids, tannins, saponins and alkaloids. Various modern studies report the biological activity of croton leaves, including anti-cholesterol, anti-hyperglycemia, antibacterial effects, and their use in biosurfactant formulations. However, studies on how varying extract concentrations influence bioactivity parameters quantitatively are still limited, especially in the context of dose-response relationships involving many biological parameters in one series of research. Therefore, this research was conducted to evaluate eight bioactivity parameters, namely PSM, SSSP, DSSP, SL, RO, SM, PSL, and ANA at croton leaf extract concentrations of 1%, 2%, and 4%. This research uses a laboratory experimental approach by extracting croton leaves through a maceration method using ethanol, then formulated in three concentration levels. Each parameter was analyzed quantitatively to observe the pattern of changes in biological response to variations in the dose administered. The results showed that most of the PSM, SSSP, DSSP, SL, and PSL parameters showed an optimum response at a concentration of 2%, which indicates that there is a most effective dose point before a decrease in activity occurs at the highest concentration. In contrast, several parameters such as RO, SM, and ANA showed a more fluctuating response, even the ANA parameter gave the highest value at a concentration of 4%, indicating that certain activities require higher compound levels to be achieved. In general, the response pattern found was not linear, but instead formed an optimum curve typical of the bioactivity of plant extracts. These findings confirm that determining dosage is very important in the use of croton leaf extract, both for pharmacological purposes and for the formulation of natural ingredient-based products.

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.