Microplastics (MPs) are an emerging pollutant in agricultural irrigation systems, particularly in densely populated regions with intensive land use. This study investigated the distribution, characteristics, and ecological risks of MPs across seven sampling sites within a rural irrigation network. MPs concentrations ranged from 3.1 to 64.3 particles/L, with the highest levels observed at sites receiving domestic runoff and urban channel inputs. Fibers (44
Batik micro, small, and medium enterprises (MSMEs) around the world are increasingly facing pressure to adopt environmentally responsible practices while simultaneously adapting to rapid digital transformation. However, research that integrates environmental entrepreneurship, digital transformation, business innovation, and business sustainability—especially within culture-based creative industries—remains limited and fragmented. This study addresses this gap by developing a theoretical model that explains how environmentally oriented entrepreneurship and digital transformation influence innovation and long-term business sustainability in Batik MSMEs in Madura, Indonesia. A quantitative research design was employed using Structural Equation Modeling with the Partial Least Squares approach. Data were collected through a structured questionnaire administered to batik MSME owners and managers, and analyzed using SmartPLS version 4. The findings show that environmentally oriented entrepreneurship and digital transformation both enhance business innovation and overall business sustainability, with innovation serving as a key mediating factor. That most Batik MSMEs actors display environmental awareness and engage in product innovation, although creative promotion remains relatively low. Indicators of sustainability also show positive tendencies, including stable profit patterns and the creation of permanent employment. This study contributes to the international literature by offering a culturally grounded model that integrates environmental and digital dimensions in creative microenterprises. The conclusions highlight the importance of strengthening innovation capabilities and supporting environmentally responsible digitalization as strategic pathways for ensuring the long-term sustainability of Batik MSMEs.
Cara terbaik bagi sekolah untuk mendorong perkembangan karakter anak usia dini adalah melalui kegiatan pembiasaan. Ini merupakan fase awal dalam pembentukan karakter anak. Persepsi dan perilaku anak-anak dapat berubah karena stimulasi terus-menerus. Tujuan penelitian ini adalah bagaimana kegiatan pembiasaan internalisasi karakter anak. Penelitian ini menggunakan metode kualitatif deskriptif dengan melibatkan 25 anak didik kelompok B di TK Plus Usman Al-Farsy, serta satu kepala sekolah dan satu guru pendamping sebagai informan pendukung. Studi ini dilakukan di semester ganjil tahun 2025, dan berlangsung selama satu bulan. Pengumpulan data dilakukan melalui dokumentasi, wawancara, dan observasi; analisis data menggunakan model interaktif Miles dan Huberman. Menurut penelitian, internalisasi karakter disebabkan oleh kebiasaan membersihkan alat bermain, menyimpan barang pribadi, dan berbaris sebelum dan sesudah kegiatan. Pelaksanaan yang konsisten, dukungan orang tua, dan contoh guru adalah komponen penting yang mendukung. Penelitian ini memberikan gambaran mendalam tentang proses, tahapan, dan komponen yang mempengaruhi keberhasilan internalisasi karakter tanggung jawab di institusi PAUD.
Pendahuluan: Cabe Jawa (Piper retrofractum Vahl.) merupakan tanaman obat yang mengandung alkaloid, flavonoid, tanin, saponin, dan minyak atsiri yang berpotensi sebagai antibakteri. Perbedaan tingkat kepolaran pelarut memengaruhi senyawa aktif yang terekstraksi sehingga berpengaruh terhadap aktivitas antibakterinya. Tujuan: Penelitian ini bertujuan mengetahui pengaruh variasi pelarut terhadap aktivitas antibakteri ekstrak Cabe Jawa terhadap Staphylococcus aureus dan Escherichia coli. Metode: Penelitian eksperimental dilakukan menggunakan metode maserasi dengan pelarut etanol 96%, etil asetat, dan n-heksana. Uji aktivitas antibakteri dilakukan menggunakan metode difusi cakram pada media Nutrient Agar. Data dianalisis menggunakan uji ANOVA pada taraf kepercayaan 95%. Hasil: Variasi pelarut berpengaruh terhadap aktivitas antibakteri ekstrak Cabe Jawa. Variasi pelarut dan konsentrasi berpengaruh terhadap aktivitas antibakteri ekstrak buah Cabe Jawa, diikuti ekstrak etil asetat dan n-heksana. Semakin tinggi konsentrasi ekstrak, semakin besar diameter zona hambat yang terbentuk. Kesimpulan: Variasi pelarut berpengaruh terhadap aktivitas antibakteri ekstrak Cabe Jawa (Piper retrofractum Vahl.). Pelarut etanol 96% menghasilkan aktivitas antibakteri paling baik terhadap Staphylococcus aureus dan Escherichia coli Kata kunci : Cabe Jawa, antibakteri, variasi pelarut, Staphylococcus aureus, Escherichia coli
The reliability of Internet of Things (IoT)-based irrigation systems depends heavily on the quality of data acquired from various sensors. In real-world applications, missing values and faulty data, caused by transmission faults or sensor failures, can severely compromise the application of Machine Learning (ML), particularly in critical tasks such as detecting pump failures. This study investigates the impact of missing data imputation on the performance of an Extreme Gradient Boosting (XGBoost) classifier under a controlled setting. Missing data were simulated at rates ranging from 5% to 30%, and the imputation techniques applied included Multiple Imputation by Chained Equations (MICE), k-Nearest Neighbors (KNN), Random Forest (RF), and iterative gradient-boosting methods. The quality of the imputed data was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), whereas classifier performance was assessed using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC). For 5% missing data, MICE achieved the lowest errors (MAE = 0.018, RMSE = 0.026), whereas at 30% missing data, errors increased (MAE = 0.072, RMSE = 0.094). Classifier performance declined with increasing missing data: accuracy decreased from 0.972 to 0.818 and recall from 0.961 to 0.742. The application of MICE mitigated this decline, maintaining an accuracy of 0.982 and recall of 0.975 at 5% missing data, and an accuracy of 0.863 and recall of 0.812 at 30% missing data, with AUC remaining above 0.880 in both cases. Notably, the imputation method with the lowest MAE and RMSE did not always produce the best classification performance, indicating that numerical precision does not necessarily translate into improved classification skill. These results highlight the importance of selecting imputation techniques based on the specific nature of the problem to preserve feature integrity and sustain the performance of Artificial Intelligence (AI)-based smart irrigation systems in real-world sensor environments.
