Technique For Order Preference By Similarity To Ideal Solution Research Articles

IDEAL LOCATION SELECTION FOR CONTACTLESS PARCEL PICK-UP POINTS

The post-Covid era has witnessed the adoption of various new habits in our daily lives, particularly in relation to the ubiquitous e-commerce platforms that have become essential for urban populations. The surge in e-commerce activities and the intensified volume in delivery of packages during the pandemic sparked innovative ideas. This study explores one such creative concept: parcel pick-up points. We conducted a pioneering research endeavor to determine the optimal locations for these pick-up points in Istanbul, Turkey. Our methodology employed a novel hybrid approach, combining the Spherical Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation of criteria importance was facilitated by a literature review and experienced high-level managers in the cargo industry, who determined the criteria weights using the Spherical Fuzzy AHP method. Subsequently, the TOPSIS method was employed to identify the most ideal locations, leading to the selection of Kadikoy, Umraniye, and Atasehir. This study provides valuable insights into the selection of the optimal locations for parcel pick-up points in Istanbul, Turkey, which can inform policymakers, e-commerce companies, and logistics stakeholders. The proposed hybrid approach demonstrates the integration of modern smart technologies with fuzzy decision-making techniques, offering a robust framework for decision support in the field of e-commerce logistics. Future research can further explore the implementation and effectiveness of these pick-up points to enhance the efficiency and convenience of last-mile deliveries in urban areas.

A Decision Support System Based on Hybrid Approach With Copras And Interval Type-2 Fuzzy Topsis For Evaluation Of Renewable Energy Alternatives

Renewable energy (RE) is a vital source for the sustainable development of society and economy. It plays a significant role in meeting energy requirements of both developed and developing countries. Moreover, renewable energy creats multiple benefits such as environmental improvement, increases fuel diversity, reduction of energy price, volality effect on their economy, national economic security, and increases in economic productivity. Selection of the most appropriate RE alternatives for any country can provide guidelines to planners of regional, national and global energy systems. The issue of ranking renewable energy sources involves many conflicting criteria and is a complicated problem since it needs to simultaneously incorporate technical, economic, cost, social-political, and environmental criteria. In this study, an integrated multi criteria decision making (MCDM) aproach consisting of interval type-2 fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Compressed Proportional Assessment (COPRAS) method is conducted to prioritize RE alternatives in order to direct planning of the national RE investments. A real case application for Turkey has been presented via expert evaluations to demonstrate applicability of the proposed methodology.

Evaluation of sustainable cold chain suppliers using a combined multi-criteria group decision-making framework under fuzzy ZE-numbers

Health 4.0 actively focuses on cold supply chains (CSCs) due to digital transformation and technological advancements in refrigeration. With the ill effects of climate change and greenhouse gas emissions, countries globally seek sustainable supply chain management. In this context, integrating sustainability with CSCs becomes crucial. Health sectors actively consider CSC for their activities. In this context, to gain potential suppliers for healthcare management, the present work focuses on grading suppliers from the CSC with sustainability factors. Previous related studies on sustainable cold chain supplier selection (CSSS) (i) have not modeled uncertainty effectively, and (ii) have yet to use any approach under fuzzy Z-numbers information. Therefore, this paper developed an integrated framework, including the Logarithm Methodology of Additive Weights (LMAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods based on extended fuzzy Z-numbers. The practicality of the framework is exemplified via a case example within the Indian health sector, and results indicate that job creation, value price, and productivity via renewable resources are the most critical factors. Moreover, regarding the findings, the economic dimension is the foremost pillar for effective selection. Sensitivity and comparison analyses are performed to understand the efficacy of the integrated framework as well. The proposed framework could contribute to the relevant literature due to its capacity to cope with complex decision-making problems and find many application areas in engineering.

A TOPSIS-based framework for construction projects’ portfolio selection in the public sector

PurposeThis paper proposes selecting a construction project portfolio in the context of a public institution, which makes it possible to assess quantitative and qualitative criteria, thereby meeting the needs of the institution and the existing constraints.Design/methodology/approachThe research design follows a framework using technique for order preference by similarity to ideal solution (TOPSIS) associated with integer linear programming.FindingsThe method involves a flow of assessments allowing criteria and weights to be elicited where outcomes are based on the experts' intra-criteria assessment of alternatives and decision-makers' inter-criteria assessment. This is of utmost interest to public organizations, where selections must result in benefits and lower costs, integrating the experts' technical and management perspectives.Social implicationsPublic institutions are characterized by having limited financial and personnel resources for project development despite having a high demand for requests not associated with profits, making it essential to have a framework that enables using multiple criteria to better evaluate the benefits related to these decisions.Originality/valueThe main contributions of this article are: (1) the proposition of a framework for selecting construction project portfolios considering the organization's strategic needs; (2) identifying quantitative and qualitative assessment criteria for project selection; (3) integrating TOPSIS with an optimization process for selecting the construction project portfolios and (4) providing a structured decision process for selecting the portfolio that best represents the interests of the institution within its limited resources and personnel.

Optimization of plate-fin heat exchanger performance for heat dissipation of thermoelectric cooler

A plate-fin heat sink is usually used for the hot side of thermoelectric coolers. The plate-fin heat sink should be optimized to improve its heat dissipation capability and eventually elevate the thermoelectric cooler performance. In this paper, a heat dissipation model of the thermoelectric cooler hot side with a plate-fin sink is established, to explore the performances of the plate-fin sink with two types of fins (rectangular and V-type), two convective conditions (lateral and vertical) and three arrangements (single-row, double-row aligned, and double-row staggered). The results show that under vertical jet conditions, double-row aligned rectangular fins have the best heat dissipation capability for the thermoelectric cooler hot side. In order to optimize the fin geometry and maximize its overall performance, a multi-objective optimization of the fins was performed using a machine learning approach based on artificial neural networks (ANN) and non-dominated sorting genetic algorithm (NSGA-ІІ). The corresponding Pareto front is obtained and the technique for order preference by similarity to ideal solution (TOPSIS) method is applied to take the point. Compared with the base case at the same mass flow rate, the fins designed by this method resulted in a 23.3 % reduction in the mean temperature of the hot side.

Optimization of Flow Channels in a PEM Fuel Cell Based on a Multiobjective Evaluation

Optimization of Flow Channels in a PEM Fuel Cell Based on a Multiobjective Evaluation

Investigation for the optimum selection of hybrid AA2024 – ceramic particulate alloy composite materials prepared through stir casting using the hybrid AHP-TOPSIS MCDM technique

ABSTRACT In most of the manufacturing industries, the selection of optimal material among the finite available alternatives becomes more difficult with the more required performance criteria to satisfy certain objectives. In such scenarios, Multi-Criteria-Decision-Making (MCDM) techniques, such as hybrid AHP (Analytical hierarchy process) – TOPSIS (Technique for Order Preferences by Similarity to Ideal Solution) and others assist in quantitative decision-making by accounting for qualitative human judgments. The present work illustrates the application of a hybrid AHP-TOPSIS technique to the ranking of alloy composites. The properties defining data such as physical, mechanical, thermal, thermo-mechanical, etc. are weighted using the AHP technique; thereafter, relative weights are used to rank the alloy composite compositions by the TOPSIS method. The compositions comprise the AA2024 alloy as the matrix phase and the reinforcing phase consists of silicon carbide, silicon nitride and graphite particulates. The ranking analysis orders are consistent with subjective analysis.

Optimization of the integrated green-gray-blue system to deal with urban flood under multi-objective decision-making.

The integrated green-gray-blue (IGGB) system is considered to be a new way of stormwater management, and a comprehensive evaluation of the green-gray-blue infrastructure layout mode under different return periods is the key to the implementation decision-making of stormwater management. In this study, a blue-green synergism evaluation model is established to optimize the layout of blue-green infrastructure. An evaluation framework combining the evaluation indicator system and the hydrology model is established. Stormwater storage, peak flow reduction, and life cycle cost are selected as evaluation indicators. On this basis, seven optimal scenarios, including green, blue, gray, green-blue, green-gray, blue-gray, and green-gray-blue, are established. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to analyze these seven scenarios under different return periods. The results indicate that (1) when the drainage infrastructures are arranged in combination, the peak flow reduction is significantly improved compared to that of a single drainage. (2) TOPSIS results show that green-gray and blue-gray perform better when the cost weight is 0-0.35, and green-gray-blue performs best when the cost weight is 0.35-1. (3) The integrated green-gray-blue system has obvious synergistic effects. This study can provide support for planning department workers for the urban stormwater management strategy.

Analysis of the integrated performance of hybrid fiber-reinforced polymer composite used for thermal protection based on a dual-scale ablation model

In order to cope with more extreme aerothermodynamic environment, a dual-layer hybrid fiber-reinforced polymer composite (HFRP) was proposed as a thermal protection material. A dual-scale ablation model was developed to evaluate the performance of this composite, including a meso‑scale heat transfer model and a macro-scale ablation model, and the two models were effectively linked. The models were validated have good reliability. The meso‑scale thermal properties and macro-scale ablation performance of dual-layer HFRP were compared with those of traditional carbon fiber-reinforced polymer composite (CFRP). The effect of thickness the of HFRP on the performance of dual-layer HFRP was also investigated. Finally, the integrated ablation resistance-insulation-lightweight design of the composite was comprehensively evaluated by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show that the new composite proposed in this paper maintains good ablation resistance and light weight while the thermal insulation performance had significantly improved, especially when the thickness of the HFRP is 50 %, the comprehensive performance is optimal, and the warp yarn hybrid fiber-reinforced polymer composite (WaHFRP) has better performance. Dual-layer HFRP had obvious advantages to CFRP, and can be used as a reference in the design of thermal protection materials.

Evaluation of PPP-ABS investment environment based on combined weighting of level difference maximization and TOPSIS method.

Asset-backed securitization (ABS) is currently used to refinance public-private partnership (PPP) projects in the infrastructure field. To stimulate the investors' enthusiasm, this study evaluated the investment environment of PPP projects asset-backed securitization (PPP-ABS). Firstly, we established a PPP-ABS investment environment evaluation indicator system based on the literature review and the practice of PPP-ABS. Then, the optimal weights of each indicator were determined by the combined weighting of level difference maximization method, where the subjective weights were determined by the AHP method, and the objective weights were determined by the entropy method. Finally, we evaluated the PPP-ABS investment environment from 2015 to 2022 with the technique for order preference by similarity to ideal solution (TOPSIS) method. The final valuation results are consistent with the actual situation. The results showed that the PPP-ABS investment environment exhibits a stable and upward trend. Under the overall guidance of the government, the approval process, information disclosure and supervisory systems have continued to improve, the number of ABS products issued has continued to grow, and the overall market risk is controllable. However, some problems still need to be solved and improved, including inadequate accounting and tax systems, insufficient liquidity in the secondary market, and the recovery of economic development in the post-COVID-19 era. This study fills the research gap in PPP-ABS. It proves the rationality and feasibility of PPP-ABS and is expected to provide a reference for investors' decision-making and promote the sustainable and healthy development of PPP-ABS.

A 15N-Tracing Study to Explore the Coupling Effects of Biochar and Nitrogen Fertilizer on Tomato Growth, Yield, Nitrogen Uptake and Utilization, and the Rhizosphere Soil Environment under Root-Divide Alternative Irrigation

To investigate the coupling effects of biochar and nitrogen fertilizer on tomato growth, nitrogen uptake and utilization (NUU), and the soil environment, a pot experiment was conducted using 15N-tracing technology from March to July 2021 and from September 2021 to January 2022. Three biochar application rates (B0, B1, and B2; 0, 3, and 6 t/hm2, respectively) and three nitrogen levels (N1, N2, and N3; 150, 300, and 450 kg/hm2, respectively) were set up. The results show that the growth, yield, rate of 15N uptake, nitrogen derived from soil (Ndfs), total nitrogen (TN), 15N utilization, and recovery rate of tomatoes were improved under biochar application, but nitrogen derived from fertilizer (Ndff) gradually decreased. A Pearson correlation analysis showed that the 15N uptake, Ndfs, TN, rhizosphere soil organic matter, soil organic carbon, and TN were significantly positively correlated with the yield and lycopene content of tomatoes. The comprehensive benefit to the tomatoes was evaluated based on combination weighting with the help of the technique for order preference by similarity to ideal solution (TOPSIS). This indicates that the best planting mode was the B2N2 treatment, with a biochar rate of 6 t/hm2 and nitrogen levels of 300 kg/hm2, under the alternative partial root-zone irrigation.

Multi-criteria decision analysis for supporting the selection of subclinical mastitis screening tests to use in large- and small-scale dairy farms in Türkiye.

The production of high-quality and safe milk is closely associated with the udder health of dairy cows. While there are many mastitis diagnostic tests/methods available, choosing the most appropriate diagnostic test for a sustainable udder health control program could be a challenge. This study was aimed at selecting tests for the screening of subclinical mastitis on small- and large-scale dairy farms in Türkiye, using multi-criteria decision-making methods. An integrated approach employing the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) together was used to select subclinical mastitis screening tests for on-farm use. While the AHP determines the weights of the evaluation criteria, the TOPSIS provides a final ranking. Nine different subclinical mastitis screening (SCM) methods (DeLaval somatic cell counter, PortaSCC test, California mastitis test (CMT), rapid culture, portable/hand-held electrical conductivity meter, infrared thermography, leukocyte esterase strip test, milk pH, UdderCheck test) were analyzed on the basis of five selection criteria (the market availability of the test, the diagnostic accuracy of the test, the cost of the test, the cow-side use of the test, and the practicality of the test). The selection criteria were determined based on literature review and stakeholder input. The weighting of the criteria with the AHP was based on the pairwise comparison of the criteria by stakeholders. The criteria were weighted from 1 to 9 according to their relative importance as follows: "1: equally important," "3: moderately important," "5: strongly important," "7: very strongly important," "9: extremely important," and "2, 4, 6, 8: intermediate values." Final ranking of SCM tests with the TOPSIS was based on the stakeholder evaluations of fulfillment of the criteria by the alternatives. The most appropriate screening test for both large- and small-scale dairy farms was determined to be the CMT. The CMT is a very useful, easy to perform, and low-cost tool for detecting subclinical mastitis. Being a major element of udder health control programs, the CMT, if regularly used on dairy farms in Türkiye, would enable the culling of chronically infected animals and the reduction of mastitis-associated economic losses. Furthermore, regular CMTs would contribute to reducing milk SCC and improving milk quality. In conclusion, multi-criteria decision-making methods not only provide a systematic approach that may assist both veterinarians and farmers in deciding on the best choice among the different tests available for the screening of subclinical mastitis but also offer potential benefits to policymakers, researchers, and other industry stakeholders.

Estimating Japanese bank performance: Stochastic entropic analysis on the basis of ideal solutions

We propose a stochastic entropic analysis based on ideal solutions and apply it to evaluate the performance of Japanese banks. Not only is the odds ratio of the learning feedback modeled by Beta-distributed priors, but our innovative model also combines the advantages of Data Envelopment Analysis (DEA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in performance evaluation. The information gains in terms of contextual variables are predicted using Machine Learning methods. Our findings demonstrate that our proposed method generates more comprehensive performance scores compared to DEA and TOPSIS. Additionally, we find that total liabilities, total assets, and total deposits, in that order, are the three most relevant inputs for understanding the outputs. Operating revenue, total income, and total loans are the output variables most pertinent to comprehending input behavior. We argue that while mutual information seems important in explaining efficiency variations, continuous improvement in the production process plays an even more crucial role in enhancing the performance of Japanese banks.

Performance appraisement of supplier selection in construction company with Fuzzy AHP, Fuzzy TOPSIS, and DEA: A case study based approach

In the present marketing environment, choosing the right suppliers is very difficult for any construction company. Current supplier selection models in the construction industry often suffer from limitations such as incomplete criteria coverage, inadequate handling of uncertainties, and oversimplification of decision-making, leading to sub-optimal supplier choices and project risks. This paper aims in selecting the best suppliers among the different M-Sand environment suppliers. In this study 13 qualitative criterions are selected by the expert team. For handling the attributes, uncertainties, vagueness associated with supplier selection problems the Fuzzy Delphi, Fuzzy Analytical hierarchal Process (AHP) and Fuzzy Technique for order preference by similarity to ideal solution (TOPSIS) methods were chosen. In the first phase of this study, Fuzzy Delphi Method is employed to select the 5 significant criterions. These criterions can be used to help the construction company in the direction to choose the right suppliers at the end. During the second phase, one of the significant Multi-criteria Decision Making Method called AHP is employed with extended support of fuzzy logic to evaluate the weightage of each criterion. Further ranking of various alternative suppliers are done by Fuzzy TOPSIS model. The ranking results indicate that A2 is the best supplier followed by A1 and A2. The third phase of this study deals with analyzing both the qualitative and quantitative criteria, hence Data Envelopment Analysis (DEA) is adopted to correlate the criteria. This is done to select efficient suppliers. The develop model is demonstrated in the construction industry.

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

In the context of the thriving real estate market in developing countries like Vietnam, understanding consumer preferences and effectively addressing them through a comprehensive multi-criteria decision-making (MCDM) framework is paramount for real estate providers. This study presents a two-stage MCDM model that integrates the Delphi technique and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on Spherical Fuzzy Sets (SFSs). Initially, the SF-Delphi technique validates critical criteria influencing customers' apartment selection in Vietnam. Secondly, the SF-TOPSIS method evaluates the top ten apartment providers. To ensure robustness and validity, a comparative analysis compares the results with those from the Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) and Fuzzy TOPSIS (F-TOPSIS) methods. Subsequently, five rank correlation coefficients (Spearman, Kendall, Goodman-Kruskal, Weighted rank measure of correlation, Weighted Similarity) are used to assess the relationships between various TOPSIS techniques applied to apartment suppliers in Vietnam. The correlation coefficients demonstrate strong agreement among the TOPSIS methods, with the smallest coefficient being 0.7778, surpassing the threshold of 0.7. This high level of consistency confirms the efficacy of the proposed TOPSIS approach with different Fuzzy Sets in reliably evaluating customers' preferences for apartment suppliers. Notably, the legal aspect's prominence underscores its critical role in shaping customer choices, emphasizing the significance of considering legal factors in the context of apartment supply and demand in Vietnam. Furthermore, using SFSs makes this approach particularly suited to capture consumer perceptions within the dynamic and uncertain business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).

Evaluating the impact of COVID-19 on the monetary crisis by machine learning

In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.

Consideration of Parents in Choosing Islamic Schools in the Digital Era

In the digital era, formal education is not only about learning at school but also about learning and education outside of school. This condition has more or less directly affected the consideration of parents in choosing a school, especially in the digital era. This research is conducted using quantitative and qualitative analysis methods. The quantitative method is performed by analyzing the potential of the schools chosen by parents, while the qualitative method is to find out the opinions of parents in more depth in choosing schools for their children. TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis is performed which involves three experts in assessing good schools in the digital era. Three types of schools are used as research objects such as Islamic Boarding Schools, Public Schools, and Subsidized Schools. Some interesting findings related to considerations of parents in choosing a school are related to religious education for their children, access to education, and also the reason for the relatively expensive cost of Islamic-based schools. Therefore, parents tend to choose non-boarding public schools.

A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds

Best management practices (BMPs) have been extensively employed in effective watershed management for non-point source pollution. The weights of objective functions and the restrictive conditions of combined BMPs are the vital requirements for BMPs allocation. Therefore, it is more beneficial to explore that a spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination. Here is the novel framework based on Soil and Water Assessment Tool (SWAT) and the Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ), which considers multiple objectives in deriving watershed-scale pollution control practices by considering BMPs cost and combined reduction rates of total nitrogen (TN) and total phosphorus (TP). The framework also integrates combined Entropy Weight method (EWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the weights of TN and TP, and considers the attributes of the sub-basin itself, which is more local suitability. Four categories of BMPs, tillage management, nutrient management, vegetative filter strips, and landscape management, were evaluated in the Jing River Basin (JRB) and resulted in reduction rates of 9.77%, 10.53%, 16.40%, and 14.27% averagely, respectively. BMP allocation schemes, derived from multi-objective optimization, are stratified into three financial scenarios. Low-cost scenario, costing up to 2 billion RMB, primarily targets the grain for green program in 28.81% of sub-basins. Medium-cost scenario, between 2 and 6 billion RMB, predominantly utilizes the grain for green in areas with a slope greater than 15°, accounting for 20.00% of sub-basins. High-cost scenario exceeds 6 billion RMB, mainly due to the implementation of multiple combination measures. The three configuration scenarios can provide decision-makers with a trade-off between measure costs and reduction efficiency. Overall, the innovative framework not only facilitates cost-effective implementation but provides a beneficial methodology for selecting cost-effective conservation practices in other regions.

Sistem Pendukung Keputusan Pemilihan Jurusan Menggunakan Metode TOPSIS Berbasis Web Studi Kasus: SMK Negeri 38 Jakarta Pusat

The selection of majors at SMK Negeri 38 Jakarta currently relies on a single opportunity during registration, without considering various factors that might influence students' choices. This underscores the necessity for developing a decision support system that can aid prospective students in selecting majors aligned with their interests, potentials, and characteristics. The aim of this research is to design and implement a decision support system using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as an alternative to facilitate a more informed decision-making process. The research methodology involves data collection from prospective students, including personal interests, the average grades from the first to the fifth semester of junior high school, average school examination scores, registration pathways, and percentiles of grades. This data serves as input for the TOPSIS method to generate recommendations for study focus options that suit the characteristics of each prospective student. The research findings indicate that the implementation of a TOPSIS-based decision support system offers a more accurate possibility for major selection, aligning with the potentials and interests of prospective students. This system allows for data filtering and processing to produce recommendations guiding prospective students in choosing a suitable study focus among three available options: Hospitality, Culinary Arts, and Fashion Design. Conclusively, the decision support system using the TOPSIS method proves to be an effective solution in aiding new prospective students at SMK Negeri 38 Jakarta in selecting majors aligned with their interests and potentials, thereby reducing the tendency for errors in major selection based on a single registration opportunity.

Implementasi Metode Technique For Order Preference By Similarity To Ideal Solution (Topsis) Dalam Penunjang Keputusan Penilaian Kinerja Guru

The purpose of this research is to build a decision support system for evaluating teacher performance using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method at SDN Inpres 09 Bandung. decision support for teacher performance evaluation uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method which can be used by school principals and assessment teams to provide teacher performance assessments. If previously when not using the system in the teacher performance appraisal process it took 1 week, now with the teacher performance appraisal system, the selection time is shorter, namely 15 minutes to 30 minutes. So that routine activities within the scope of SDN Inpres 09 Bandung are not disturbed by the process of evaluating teacher performance, then the process of making reports becomes easier and more systematic.