Work environment conditions and employee discipline are important factors in the operation of Nutrition Fulfillment Service Units (SPPG). This study aimed to analyze the effects of work environment and work discipline on employee performance at the Nutrition Fulfillment Service Unit (SPPG) in Tugumulyo Village, Ogan Komering Ilir Regency. A quantitative associative research design was employed. The population consisted of 105 employees, with 83 respondents selected using the Slovin formula at a 5% margin of error. Data were collected through Likert-scale questionnaires, observation, and documentation, and analyzed using multiple linear regression, t-test, F-test, and coefficient of determination. The results showed that work environment had a positive and significant effect on employee performance, with a t-value of 4.216 and significance of 0.000. Work discipline also had a positive and significant effect, with a t-value of 3.870 and significance of 0.000. Simultaneously, both variables significantly affected employee performance, with an F-value of 34.756. The R² value of 0.465 indicates that 46.5% of the variation in employee performance was explained by work environment and work discipline.
The utilization of financial technology (fintech) has become a critical strategy for MSMEs to increase revenue in the digital era. This study aims to analyze the influence of Payment Gateway and Peer-to-Peer (P2P) Lending on the income growth of culinary MSMEs in the Seberang Ulu area, Palembang City. A quantitative method with an associative approach was employed. The sample consisted of 100 culinary MSME respondents selected through Simple Random Sampling. Data were collected using a five-point Likert scale questionnaire and analyzed with multiple linear regression using SPSS. Results indicate that Payment Gateway has a positive and significant effect on MSME income growth (β = 0.391; t = 5.606; p < 0.05), as does P2P Lending (β = 0.183; t = 3.202; p < 0.05). Simultaneously, both variables explain 57.3% of the variance in income growth (R² = 0.573; F = 65.165; p < 0.001). These findings affirm that integrated adoption of digital financial technology for both payment and financing constitutes an effective strategy for promoting MSME income growth in the digital era.
Adolescent depression has become an important public health concern, particularly in relation to increasing digital media exposure, lifestyle changes, and psychosocial pressure. This study proposes an explainable machine learning framework for predicting depression risk among teenagers using social media usage, lifestyle behavior, and psychosocial indicators. The dataset consisted of 1,200 records with 13 variables, including age, gender, daily social media hours, platform usage, sleep hours, screen time before sleep, academic performance, physical activity, social interaction level, stress level, anxiety level, addiction level, and depression label. The target variable was highly imbalanced, with 1,169 samples categorized as non-depression and only 31 samples categorized as depression risk. Several machine learning models were evaluated, including Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting. The experiments compared two feature settings, namely behavioral-only features and full features, combined with three imbalance handling strategies: no imbalance treatment, class weighting, and SMOTE. Model performance was evaluated using accuracy, precision, recall, F1-score, balanced accuracy, ROC-AUC, PR-AUC, Cohen’s Kappa, MAE, and RMSE. The results showed that the full-feature setting substantially outperformed the behavioral-only setting. The best performance was achieved by Random Forest using full features without imbalance handling, producing perfect classification results with accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC of 1.0000. Permutation importance analysis identified sleep hours, stress level, anxiety level, and daily social media hours as the most influential predictors. These findings indicate that teenage depression risk in this dataset is strongly associated with sleep behavior and psychosocial conditions, in addition to social media exposure. Although the model achieved excellent performance, the result should be interpreted cautiously due to the small number of positive depression-risk samples and the possibility of highly separable label patterns. Therefore, the proposed approach should be positioned as an early risk screening framework rather than a clinical diagnostic tool
Penelitian ini bertujuan untuk menguji secara empiris hubungan antara potensi akademik dan efikasi diri pada siswa bimbingan belajar (bimbel) persiapan sekolah kedinasan. Penelitian ini menggunakan pendekatan kuantitatif. Populasi penelitian berjumlah 190 siswa Bimbel PKN-STAN Madani Kota Palembang, dengan sampel sebanyak 123 siswa yang ditentukan menggunakan teknik purposive sampling berdasarkan formula Isaac dan Michael pada taraf kesalahan 5%. Pengumpulan data dilakukan menggunakan alat ukur berupa Skala Potensi Akademik (48 aitem valid, alpha = 0,955) dan Skala Efikasi Diri (47 aitem valid, alpha = 0,966). Data dianalisis menggunakan teknik regresi linear sederhana setelah memenuhi uji prasyarat normalitas dan linieritas. Hasil analisis menunjukkan bahwa mayoritas siswa memiliki potensi akademik tinggi (59%) dan efikasi diri tinggi (55%). Hasil uji hipotesis menunjukkan adanya hubungan positif yang sangat signifikan antara potensi akademik dan efikasi diri (R = 0,716; p = 0,000, p < 0,01). Koefisien determinasi (R^2) sebesar 0,513 mengindikasikan bahwa potensi akademik memberikan kontribusi efektif sebesar 51,3% terhadap efikasi diri siswa, sedangkan 48,7% sisanya dipengaruhi oleh faktor lain di luar fokus penelitian. Temuan ini menegaskan pentingnya pengembangan potensi akademik untuk memperkuat keyakinan diri siswa dalam menghadapi ujian seleksi kedinasan.
Pengenalan literasi digital dan kecerdasan buatan (AI) di SMA Al Ihsan Tanjung Lago merupakan langkah strategis untuk membekali generasi muda dalam menghadapi tantangan era digital. Kegiatan ini mendukung kebijakan Merdeka Belajar Kampus Merdeka (MBKM) dan bertujuan untuk membangun sikap kritis, beretika, dan bertanggung jawab dalam penggunaan media digital. Melalui penyampaian materi interaktif tentang etika digital, keamanan data, dan bahaya hoax, siswa diajak untuk memahami pentingnya verifikasi informasi dan perlindungan privasi. Demonstrasi penggunaan perangkat AI seperti Gamma.app dan ChatGPT membantu siswa melihat penerapan praktis teknologi dalam pembelajaran. Respon positif dari siswa dan guru menunjukkan keberhasilan program dalam meningkatkan kesadaran digital dan menjembatani kesenjangan antara perkembangan teknologi dan kebutuhan pendidikan. Untuk dampak jangka panjang, disarankan agar program ini dilaksanakan secara berkala dengan dukungan fasilitas yang memadai dan peningkatan kompetensi guru. Dengan demikian, diharapkan siswa dapat menjadi pengguna teknologi yang tidak hanya cakap, tetapi juga memiliki kesadaran kritis dan etika digital yang kuat.
