Recommendation systems face a major challenge known as the cold-start problem, which occurs when the system lacks sufficient user interaction data. Thus, there is no basis for a recommendation. Existing approaches, such as co-clustering methods and non-personalized popularity models, often struggle to effectively combine heterogeneous user and item data (categorical user attributes and numerical item ratings) or to capture latent group-level preferences. To bridge this gap, we propose a new clustering-popularity-based model that independently groups users and items using two separate algorithms and integrates them through a popularity measure. Users are clustered using K-Modes based on demographic attributes, while items are clustered separately using either K-Means or Fuzzy C-Means (FCM) based on rating patterns. A rating-aware popularity score is then computed within each item cluster. To generate recommendations for new users, we assign them to the appropriate user demographic clusters and suggest items from the most popular clusters. Experiments on the MovieLens 100K dataset show that the FCM-based variant, ClusterPopRec-FCM, consistently outperforms both a K-Means-based variant (ClusterPopRec-KMeans) and the traditional item-based baseline across all cold-start scenarios (extreme, moderate, and non-cold-start scenarios). In the extreme cold-start scenario, ClusterPopRec-FCM achieved Precision@5=54.65 and DCG@5=1.66, which in comparison to the baseline represents percentage increases of 149.7% and 110.1% respectively, with statistical significance < 0.001. These results show the benefit of soft clustering (FCM) in capturing nuanced item relationships and demonstrate the effectiveness of hybrid models that combine demographic clustering with in-cluster popularity scores. This work offer a effective solution for cold-start scenarios and heterogeneity, allowing advancement in recommendation systems research.

Purpose: The purpose of this research is to investigate the effectiveness of data warehousing and the application of Neural Networks methods in analyzing bicycle travel app user data, with a focus on enhancing the annual membership of app users in North America.Design/methodology/approach: This study utilizes a dataset that includes membership and usage data from relevant bicycle travel apps. It involves comparing the performance of different Neural Networks architectures, such as Feedforward Neural Networks, Convolutional Neural Networks (CNN), and other suitable models, to evaluate their effectiveness in predicting user membership.Findings/result: The analysis results demonstrate that the implementation of Neural Networks can improve prediction accuracy, with the most effective model achieving 76.03% accuracy. The research also highlights the importance of preprocessing steps, such as data normalization and transformation, in contributing significantly to model performance. However, challenges such as overfitting were identified, suggesting the need for further testing with model and parameter variations.Originality/value/state of the art: This research provides valuable insights for application developers and policy makers, helping them create data-driven strategies to improve the bicycle travel management information system. It also supports efforts to sustainably grow user membership. The study contributes to the field by exploring the practical application of Neural Networks for data analysis in the context of bicycle travel management, filling a gap in current research on effective predictive models for user membership growth.

Purpose: This research aims to design an IS/IT strategy for PT Wahid Bangun Semesta to propose prioritized information systems that align with the company's existing challenges and to provide guidelines for application development. Design/methodology/approach: This study focuses on designing an IS/IT strategy using the Ward and Peppard methodology. Findings/result: Based on the findings, PT Wahid Bangun Semesta Yogyakarta is advised to implement an integrated information system to support operations and enhance competitiveness. Fingerprint systems are recommended to reduce attendance misuse. The application portfolio is designed to strengthen business processes according to identified needs. The IT strategy includes LAN and wireless networks at the head office, as well as USB modems and web-based applications at branch offices for flexible information access. Establishing an IT division is also recommended for more focused management, enabling IS/IT investments to positively contribute to company growth. Originality/value/state of the art: This study designs an IS/IT strategy for PT Wahid Bangun Semesta Yogyakarta to enhance operations and competitiveness through integrated information solutions. Proposed solutions include implementing an Integrated Information System, a fingerprint machine to prevent attendance misuse, LAN and wireless networks at the head office, broadband access at branch offices, and web-based applications.Establishing an IT division is recommended to manage IS/IT resources and ensure successful investments that support the company’s growth.

Purpose: This research aims to analyze the performance and accuracy of machine learning algorithms in predicting heart disease, which is a cause of death throughout the world.Design/methodology/approach: The algorithms analyzed include Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Neural Network. A publicly available dataset containing patients' medical records was utilized, with the methodology encompassing data collection, Exploratory Data Analysis (EDA), model training, and performance evaluation.Findings/result: The results indicate that the Random Forest algorithm achieved the highest accuracy with an accuracy of 90.16%, followed by Logistic Regression and Naive Bayes with accuracies of 85.25%. The K-Nearest Neighbors algorithm exhibits the lowest accuracy at 67.21%.Originality/value/state of the art: This research highlights the advantages of certain machine learning algorithms in predicting heart disease and contributes knowledge to early detection technology in the health sector.

Konsep bangun ruang kerap menjadi tantangan bagi siswa kelas 7 SMP. Kesulitan dalam membayangkan bentuk tiga dimensi dari gambar dua dimensi seringkali menghambat pemahaman mereka. Salah satu tantangan utama adalah rendahnya pemahaman siswa terhadap konsep abstrak dalam bangun ruang. Penelitian ini bertujuan untuk memenuhi kebutuhan siswa untuk memahami konsep bangun ruang melalui pengembangan media pembelajaran interaktif yang lebih menarik dan mudah dipahami. Penelitian ini menggunakan metode R&D dengan model pengembangan ADDIE. Hasil dari penilitian ini mendapatkan penilaian dari berbagai aspek yaitu ahli media, ahli materi, dan siswa. Hasil dari penilaiannya mengindikasikan tingkat keberhasilan yang tinggi dengan persentase Ahli Media sebesar 92,39%. Kemudian Ahli Materi menunjukkan persentase sebesar 97,05%. Sedangkan penilaian dari siswa menunjukkan persentase sebesar 95,96%. Sehingga dapat diperoleh kesimpulan bahwa pengembangan media pembelajaran bangun ruang berbasis augmented reality (AR) untuk siswa kelas 7 SMP menunjukkan hasil yang sangat layak untuk digunakan.

The most important aspect of human resource management greatly impacts the success of the agency by selecting the best employees. The best employees are those who have certain skills that can contribute to the success of the agency. Non-Civil Servant Government Employees (PPNPN) are honorary personnel recognized by the Government and the State who are needed by agencies to assist a job in the unit in need. Problems occur when selecting employees with performance every month, namely the absence of a system that supports decisions to determine the selection of the best non-civil servants in Cipamokolan Bandung Village, so that the selection of the best employees is currently still done manually. Thus the selection of the best employees is not accurate and very subjective. The purpose of this research is to provide solutions to these problems by creating a decision-making system in selecting the best non-civil servants in Cipamokolan Bandung Village. The method used is by applying the Fuzzy Analytical Hierarchy Process method which can overcome uncertainty and inaccuracy in judgment. The research results obtained from the selection of non-civil servant government employees using the web-based Fuzzy Analytical Hierarchy Process method can create efficiency and effectiveness in selecting the best employees in Cipamokolan Bandung Village.

Kidney disease includes a variety of disorders affecting renal function, such as kidney cysts, tumors, and stones. If left untreated, these conditions can progress to chronic kidney disease, posing significant health risks and potentially leading to mortality. This study aims to classify kidney diseases by using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the Kernel Extreme Learning Machine (KELM) as a classification method, with renal CT images as the dataset. The classification process categorizes kidney conditions into four classes: Cyst, Normal, Stone, and Tumor. The dataset consists of 4,232 CT images, with 1,058 images per class, evenly divided into axial and coronal orientations. The study utilizes k-fold cross-validation with k = 5 and k = 10 and implements the Radial Basis Function (RBF) as the kernel function in the KELM model. An iterative tuning of parameters, including the kernel parameter () and the regularization constant (), was conducted to identify the optimal model configuration. The best classification performance was achieved at angle using k = 5, with an accuracy of 97.26%, sensitivity of 97.16%, and specificity of 99.05%. Furthermore, the model demonstrated high computational efficiency, requiring only 6.07 seconds.

Purpose: Develop a machine learning model to predict stock market activity by finding the Root Mean Squared Error (RMSE) value.Design/methodology/approach: LSTM (Long Short-Term Memory) is one of the machine learning techniques that can be used to anticipate traffic in realtime. Using this method can be used to analyze stock market activity that has time series data.Findings/result: This research obtained a Root Mean Squared Error (RMSE) value of 43.32.Originality/value/state of the art: By using the same machine learning method as the previous research, namely LSTM. The research provides more efficient results.

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.