Universitas Methodist Indonesia

Penelitian ini bertujuan untuk menganalisis pengaruh pelatihan kerja, kompensasi, dan pengembangan karier terhadap kinerja pegawai pada PT Yumeida Utama Industri Medan. Penelitian menggunakan pendekatan kuantitatif dengan populasi sebanyak 46 pegawai dan sampel 30 responden yang dipilih melalui teknik probability sampling. Data dikumpulkan menggunakan instrumen penelitian dan dianalisis dengan regresi linier berganda, uji parsial (uji t), uji simultan (uji F), serta koefisien determinasi menggunakan IBM SPSS. Hasil penelitian menunjukkan bahwa pelatihan kerja berpengaruh positif dan signifikan terhadap kinerja pegawai dengan nilai t hitung 2,597 dan signifikansi 0,015. Kompensasi juga berpengaruh positif dan signifikan terhadap kinerja pegawai dengan nilai t hitung 4,805 dan signifikansi 0,000. Sementara itu, pengembangan karier menunjukkan koefisien negatif dan tidak berpengaruh signifikan terhadap kinerja pegawai, dengan nilai t hitung -0,804 dan signifikansi 0,428. Secara simultan, pelatihan kerja, kompensasi, dan pengembangan karier berpengaruh signifikan terhadap kinerja pegawai, yang ditunjukkan oleh nilai F hitung 132,734 dengan signifikansi 0,000. Nilai R Square sebesar 0,939 menunjukkan bahwa 93,9% variasi kinerja pegawai dapat dijelaskan oleh ketiga variabel independen tersebut, sedangkan 6,1% sisanya dipengaruhi oleh faktor lain di luar model penelitian. Temuan ini menegaskan pentingnya program pelatihan yang tepat sasaran dan sistem kompensasi yang adil dalam mendukung peningkatan kinerja pegawai. Implikasi praktis penelitian ini dapat menjadi dasar evaluasi kebijakan pengelolaan sumber daya manusia perusahaan secara berkelanjutan.

Penelitian ini bertujuan untuk memperoleh bukti empiris mengenai peran manajemen laba dalam memediasi pengaruh struktur kepemilikan, manajemen aset, dan ukuran perusahaan terhadap kinerja keuangan pada perusahaan sub sektor food and beverage yang terdaftar di Bursa Efek Indonesia periode 2020–2024. Penelitian ini menggunakan pendekatan kuantitatif dengan teknik pengambilan sampel purposive sampling sehingga diperoleh 52 perusahaan yang memenuhi kriteria penelitian. Metode analisis data yang digunakan adalah Structural Equation Modeling–Partial Least Squares (SEM-PLS) dengan bantuan perangkat lunak WarpPLS versi 7.0. Hasil penelitian menunjukkan bahwa struktur kepemilikan, manajemen aset, dan ukuran perusahaan berpengaruh terhadap kinerja keuangan perusahaan. Selain itu, ketiga variabel tersebut juga terbukti berpengaruh terhadap manajemen laba. Temuan penelitian menunjukkan bahwa manajemen laba mampu berperan sebagai variabel mediasi dalam hubungan antara struktur kepemilikan dan manajemen aset terhadap kinerja keuangan. Hal ini menunjukkan bahwa praktik manajemen laba dapat menjadi mekanisme yang mempengaruhi hubungan antara faktor-faktor internal perusahaan dengan kinerja keuangan yang dihasilkan. Dengan demikian, pengelolaan struktur kepemilikan, efektivitas manajemen aset, serta pengelolaan ukuran perusahaan perlu diperhatikan oleh manajemen agar dapat meningkatkan kinerja keuangan secara optimal. Penelitian ini diharapkan dapat memberikan kontribusi bagi perusahaan dan investor dalam memahami faktor-faktor yang mempengaruhi kinerja keuangan perusahaan.

Breast cancer is one of the types of cancer that causes the most deaths in women in the world. Breast cancer prognosis is important to assist medical personnel in predicting the possibility of recurrence so that treatment can be provided more effectively. This study aims to implement a hybrid Artificial Neural Network (ANN) and Gaussian Naïve Bayes method for breast cancer prognosis prediction using the Breast Cancer Wisconsin Prognostic (WPBC) dataset. The dataset consisted of 198 patient records with 35 numerical features. The research stages included data preprocessing, normalization, splitting the dataset into training and testing data using an 80:20 ratio, feature extraction using ANN, and classification using Gaussian Naïve Bayes. Unlike previous studies that generally used single methods, this study utilizes ANN as a feature extractor before the classification process using Gaussian Naïve Bayes. ANN was used with one hidden layer containing 16 neurons to learn non-linear relationships among features before the classification process. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The experimental results showed that the hybrid ANN-Gaussian Naïve Bayes method achieved an accuracy of 90%, precision of 72.73%, recall of 88.89%, and an F1-score of 80%. These results indicate that the hybrid method provides better classification performance compared to single methods in breast cancer prognosis prediction.

The rapid development of digital learning technologies has transformed educational practices by enabling more personalized and accessible learning experiences in e-learning environments. As a Background, adaptive technologies have emerged as a promising approach to address diverse learner needs and improve educational accessibility. The Objective of this study is to examine the influence of adaptive technologies on personalized learning experiences and their impact on educational accessibility in e-learning environments. The Method employed a quantitative research approach using a survey distributed to 334 respondents who had experience using e-learning platforms. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS to evaluate the relationships among the proposed constructs. The Results indicate that adaptive technologies significantly enhance personalized learning experiences by providing tailored learning content, individualized feedback, and flexible learning pathways. Furthermore, the findings reveal that personalized learning experiences have a positive and significant effect on educational accessibility, enabling learners with different backgrounds, abilities, and learning preferences to engage more effectively in the learning process. Adaptive technologies were also found to have a direct positive influence on educational accessibility. In Conclusion, the study demonstrates that the integration of adaptive technologies plays a crucial role in fostering personalized learning and improving educational accessibility within e-learning environments. These findings provide valuable insights for educators, educational institutions, and e-learning developers in designing inclusive and learner-centered digital learning systems that support diverse educational needs and promote equitable access to quality education.

This study aims to analyze the impact of sustainability reporting on the financial performance of public companies listed on the Indonesia Stock Exchange (IDX) for the period 2015–2024. A quantitative approach was adopted using secondary data from sustainability reports and annual financial statements of 120 non-financial companies listed on the IDX that published sustainability reports. Dependent variables include Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while the primary independent variable is the sustainability disclosure index measured using the Global Reporting Initiative (GRI) framework. Panel data analysis with fixed effects and random effects approaches was employed. Results indicate that sustainability disclosure has a significant positive effect on ROA (β = 0.214, p < 0.001), ROE (β = 0.187, p < 0.01), and Tobin’s Q (β = 0.312, p < 0.001), indicating that companies that comprehensively disclose sustainability practices have better financial performance and market value. These findings reinforce the literature on the positive relationship between environmental and social responsibility and firm value creation in the context of the Indonesian capital market