CRISP-DM Research Articles

Clustering of Eligibility and Characteristics of Smart Indonesia Card Recipients for College using Agglomerative Hierarchical Clustering

Since 2023, the Smart Indonesia Card or Kartu Indonesia Pintar-Kuliah (KIP-K) has two schemes. Consisting of KIP-K Scheme 1, where scholarship recipients are exempted from tuition fees and receive living allowances during the KIP-K scholarship period and KIP-K Scheme 2, where scholarship recipients are only exempted from tuition fees without receiving living allowances. Lack of information about the characteristics of prospective KIP-K scholarship recipients is a problem in itself. Many universities/institutions are not on target in providing this scholarship. This study aims to obtain clusters of recipients of KIP-K Scheme 1, KIP-K Scheme 2, and Not Eligible who are received and to obtain the characteristics of recipients of KIP-K Scheme 1, KIP-K Scheme 2, and Not Eligible who are received. This research method uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach consisting of the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment and uses a combined method of Agglomerative Hierarchical Clustering with Average Linkage and uses the Calinski-Harabasz Index to test the validity of the cluster. The results of the study showed that the best cluster to explore was in 10 clusters with a Calinski-Harabasz Index value of 34.88236681148884. It was concluded that the feasibility of Scheme 1 KIP-K in cluster 8, 9 and 10.

Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space

There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.

Computer vision solution for uniform adherence in gastronomy schools: An artificial intelligence case study

This research study presents an innovative application of computer vision technology in culinary education to ensure consistent student uniform adherence, crucial to accomplishing hygiene, safety, and professionalism standards. The proposed approach utilizes the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to generate a computer vision application prototype to identify specific culinary uniforms components, such as chef's jackets, aprons, hats, and pants. The development process using LandingLens, a code-free platform, involves several stages: business and data understanding, preparation, modeling, evaluation, and deployment. The final model was training with 77 images, and the application deployment was tested using 38 images. Results demonstrate the potential of artificial intelligence to enhance operational efficiency and uphold professional standards in culinary education. Integrating computer vision addresses the challenges associated with manual monitoring and opens opportunities for broader adoption of technology in culinary pedagogy and training.

Supporting SME companies in mapping out AI potential: a Finnish AI development case

AbstractProducts and services relying upon Artificial Intelligence (AI) have moved from mere concepts to reality. However, challenges still exist in applying AI technologies to traditional industrial and service enterprises. Two central problems are a proper understanding of the opportunities AI could bring to the business processes and making the business logic and data sources transparent to AI experts. As small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, this paper studies how to support SMEs in understanding the potential of AI in their business and how to prepare their data and requirements for a possible AI project. For this purpose, we first proposed the Cross-Industry Standard Process for Data Mining (CRISP-DM) an industry-proven way to apply AI solutions. The weight was in early business and data understanding. Then, we performed data visualization and developed some machine learning methods for 11 SMEs in South-western Finland as case studies to get more ideas for improving their business using AI. Two surveys probed the possible changes in AI practises of companies.

Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction

As smart manufacturing expands, businesses see the importance of digital transformation, especially for small and medium-sized enterprises (SMEs). Unlike larger companies, SMEs face greater challenges when undergoing digital transformation due to technological questions. However, recent advancements in high-performance computing and reduced hardware costs have made deep learning-based digital transformation more financially feasible for SMEs. While previous research utilized machine learning for product quality prediction, there remains a lack of comprehensive in adaptive quality prediction specifically designed for SMEs. This study presents a systematic framework utilizing various machine learning methods and validates research cases using CRISP-DM (Cross-Industry Standard Process for Data Mining). The first step involves applying XGBoost(eXtreme Gradient Boosting)for feature selection, the second step utilizes GRU for parameter prediction. Finally, in the third step, SVM (Support Vector Machine) is employed for quality classification. The integrated framework achieves high accuracy, with R2reaching 90 % for predicted parameters and nearly 95 % for classification indicators. Moreover, this research addresses the research gap in quality prediction and adaptability and provide an effective digital transformation solution for SMEs without substantial investment. The proposed research framework can be applied SMEs of other domains, such as the machining and traditional manufacturing industry.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare.

Patterns and factors associated with dental service utilization among insured people: a data mining approach

BackgroundInsurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology.MethodsA secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020.ResultsThe analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories.ConclusionsRestorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

Enhancing quality 4.0 and reducing costs in lot-release process with machine learning-based complaint prediction

PurposeThis study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.Design/methodology/approachThis study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.FindingsThe framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.Practical implicationsThe framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.Originality/valueTo the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

STRATEGIC DECISION SUPPORT: DEVELOPING A DASHBOARD FOR MANAGERIAL INSIGHTS IN HIGH-LEVEL TVET STUDENTS' ENROLMENT AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to create a dashboard with information visualisation to assist Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) in managerial decision-making and to critically and hierarchically present data regarding the enrolment of Malaysian Diploma Vocational (MDV) students into high-level Technical Vocational Education and Training (TVET) programs at UMPSA. The Cross Industry Standard Process for Data Mining (CRISP-DM) model was applied in this research followed by dashboard development using Microsoft Power BI. The CRISP-DM model consists of six continuous and connected phases, namely business understanding, data understanding, data preparation, data modelling, data visualisation, and data synchronise and update. The data was obtained from the Undergraduate Records Management Unit, Centre for Academic Management, UMPSA. The database was used in the development of the dashboard to store information in the form of reports and for data visualisation purposes. The findings hope to assist the management of UMPSA in making better decisions and planning the enrolment process of prospective students. The dashboard outlines several initiatives and best practices that should be implemented by the Malaysian Government, the Ministry of Higher Education, and UMPSA through the UMPSA 2021–2025 Strategic Plan to empower the high-level TVET agenda.

ENHANCING LIFELONG LEARNING: A DATA-DRIVEN DASHBOARD APPROACH FOR STRATEGIC DECISION-MAKING AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to develop a dashboard with information visualisation for the lifelong learning (LLL) programs at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) and provide recommendations for UMPSA to improve the strategies and decision-making for future LLL program offerings. This study used the Cross Industry Standard Process for Data Mining (CRISP-DM) model created by the International Business Machines Corporation (IBM) and dashboard development using Microsoft Power BI. The updated database was created by obtaining data from the Institute of Graduate Studies (IPS), UMPSA. Such data was then prepared through the process of selecting, cleaning, constructing, integrating, and formatting data. The dashboard was developed using a database for data visualisation and information in the form of reports. Such information can ensure a better decision-making process and the UMPSA management’s readiness in planning the admission process for future students. However, the data management and data quality at UMPSA must be improved. Future research can expand the present investigation by including data from all categories of LLL students at UMPSA to facilitate a more comprehensive analysis. It will also enable the university management to make accurate decisions about program offerings in the future.

FPAS Model: A Case Study of Flood Prediction and Advisory System

The increasing frequency of flood events worldwide is a significant concern, particularly for the United Nations (UN), as it impacts both economic stability and public safety. In recent decades, floods have caused extensive damage to lives and properties, and projections indicate this trend will continue. Rapid evacuation in the event of a flood relies on effective early warning systems that can predict floods and encourage residents to evacuate high-risk areas. Africa is the third most affected continent by floods, following Asia and Europe. However, the development and implementation of flood forecasting models in Africa are still in the early stages. The continent has only a few hydrological models for flood forecasting, and most of these need improvements to keep pace with changing conditions. The study aimed to enhance flood prediction and advisory systems by leveraging machine learning (ML). The objectives are to develop a predictive model, create dual subsystems for web and Android applications, disseminate predictions and advisories through multiple communication channels, and incorporate persuasive messages to prompt at-risk populations to evacuate. A survey was conducted in Cross River State to evaluate the impact of these persuasive messages and the overall support for the Flood Predictive and Advisory System (FPAS). The study utilized the NiMet dataset, preprocessed the data, and trained the model using SVM, Random Forest, and XGBoost algorithms. Subsequently, both the FPAS web and Android applications were developed and rigorously tested. The research methodology applied in this work was a hybrid approach, combining Object Oriented Analysis and Design Methodology (OOADM) and the Cross Industry Standard Process for Data Mining (CRISP-DM). OOADM was used to develop the mobile app for citizens’ registration and a web app for administrative activities, while CRISP-DM was used to create a data-drive predictive model for the app. These applications facilitated citizen registration and administrative activities, providing feedback via websites, emails, and automated alerts. The survey results revealed that 76.56% of respondents had not experienced persuasive messages before, while 91.15% expressed strong support for the FPAS system incorporating these techniques. Finally, the study successfully developed an advanced flood prediction model and an integrated advisory system, demonstrating significant potential in enhancing disaster preparedness and response through innovative technology and strategic communication.

Comparison Of K-Means and K-Medoids Algorithm for Clustering Data UMKM in Pagar Alam City

The aim of this research is clustering MSME data in Pagar Alam City using the K-Means and K-Medoids algorithms. This research is motivated by the lack of further management of MSME data collection, which can hinder the development and improvement of Pagar Alam City MSMEs. Meanwhile, this data is considered necessary for agencies to develop and improve Pagar Alam City MSMEs. Apart from agencies, this data is also useful for sub-districts, sub-districts and RT/RW to find out what interests, talents and potential the community has in what business fields. MSME data is processed using Rapid Miner and Python, the system development method in this research uses the Cross Industry Standard Process for Data Mining (CRISP-DM) method, where the stages include Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The test method uses the Davies-bouldin index, a DBI value that is close to 0 results in good clustering. The results of this research obtained 3 clusters. In 2020 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 1 and C2= 3 sub-district. In 2022 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 3 and C2= 1 sub-districts. The results of the 2020 sub-district DBI clustering calculation are DBI k-means = 0.134 and k-medoids = 0.523. In 2022 DBI k-means = 0.277 and k-medoids = 0.496. So it can be concluded that the K-Means algorithm in the case of grouping MSMEs in Pagar Alam City has better performance, because the DBI value is close to 0. From the results of the grouping it can help provide an overview for related parties in encouraging or providing assistance to sub-districts that are included in the low cluster.

Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data.

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.

Machine Learning-Based Cardiovascular Disease Detection and Intelligent Healthcare Workflow Automation

Cardiovascular diseases (CVDs) are a major cause of death worldwide, emphasizing the importance of better early detection methods. This study introduces a machine learning model to detect CVDs by analyzing vital sign information. The model, which was trained using a cardiovascular dataset, utilizes three algorithms: Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. It assesses the health status of patients by predicting vital sign values, allowing healthcare providers to receive timely alerts. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a proof-of-concept web application was created with object-oriented design principles and implemented using Python. This application predicts the likelihood of CVD and streamlines the scheduling of doctor appointments, promoting quick medical intervention. Our findings reveal that the Decision Tree classifier performed the best in accurately identifying patients who are at risk based on abnormalities in vital signs. This method can potentially enhance early detection of CVDs and improve the timing of medical care.

Academic performance prediction using Machine Learning algorithms

An excellent secondary school education becomes evident in students’ performance after they graduate or further their education. No matter their career choice, they can genuinely excel if they can identify areas that require them to put in more effort to have an overall excellent performance in school. In Nigeria, several solutions address students' learning needs or the administrative needs of the schools. Still, no systems cater to analysing and monitoring students’ performance, causing failures that can be averted. This dissertation reviews five different machine learning algorithms using data from students in public and private schools in rural and urban Nigeria to identify which algorithm performs best in predicting students’ performance using the Waikato Environment for Knowledge Analysis tool for modelling and the cross-industry standard process for data mining (CRISP-DM) research methodology. The result shows the Decision Tree as the algorithm with the best performance for the dataset. It is recommended that the findings be used to build a system embedded into a school management or learning management software to enable students, parents, and teachers to channel the right resources into areas where it has been predicted that the student will underperform to change the narrative.

Enhanced Freelance Matching: Integrated Data Analysis and Machine Learning Techniques

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.

Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency

This study investigates the precision geolocation of medicinal plants, a critical endeavor bridging ecology, conservation, and pharmaceutical research. By employing machine learning algorithms—gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM)—within the cross-industry standard process for data mining (CRISP-DM) framework, both the accuracy and efficiency of medicinal plant geolocation are enhanced. The assessment employs precision, recall, accuracy, and F1 score performance metrics. Results reveal that SVM and GBM algorithms exhibit superior performance, achieving an accuracy of 97.29%, with SVM showing remarkable computational efficiency. Meanwhile, despite inferior performance, RF remains competitive especially when model interpretability is required. These outcomes highlight the efficacy of SVM and GBM in medicinal plant geolocation and accentuate their potential to advance environmental research, conservation strategies, and pharmaceutical explorations. The study underscores the interdisciplinary significance of accurately geolocating medicinal plants, supporting their conservation for future pharmaceutical innovation and ecological sustainability.

Towards an ELSA Curriculum for Data Scientists

The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow. ELSA should not be seen as an impediment or a superfluous artefact but rather as an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model as a backbone to define a vertical partition expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely ethical and societal, legal and technical rendering knowledge units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss their implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.

Reproducible machine learning research in mental workload classification using EEG.

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.