Universitas Internasional Semen Indonesia

Sida conduplicata H.Sant. & Felayati (Malvaceae) is described as a new species from East Java, Indonesia. It is similar to S. rhombifolia var. maderensis in having a single awn on the mericarp, but differs from it by a set of diagnostic morphological characters such as habit, leaf apex and surface of the mericarp. We provide an identification key to the species of Sida known from Java.

PT Kereta Api Logistik Area Wilayah Barat 3 mengalami permasalahan rendahnya repeat purchase pada layanan pengiriman paket. Dari total 274 pelanggan selama Januari hingga April 2025, sebanyak 200 pelanggan tidak melakukan repeat purchase meskipun berasal dari sektor industri dengan kebutuhan pengiriman rutin. Kondisi ini menjadi perhatian karena sebagian besar pendapatan justru berasal dari pelanggan yang tidak melakukan repeat purchase, sehingga perusahaan berisiko kehilangan pelanggan bernilai tinggi serta mengalami penurunan pendapatan secara berkelanjutan. Teknik pengambilan sampel yang digunakan adalah purposive sampling, karena peneliti menetapkan kriteria tertentu dalam pemilihan responden, yaitu pelanggan yang telah melakukan pengiriman dengan berat minimal 100 Kg dan nilai transaksi minimal Rp500.000 dalam satu kali pengiriman. Penelitian ini menggunakan pendekatan kuantitatif melalui penyebaran kuesioner kepada 73 responden, kemudian dianalisis dengan metode Partial Least Square–Structural Equation Modeling (PLS-SEM) untuk melihat pengaruh antar variabel. Hasil penelitian menunjukkan bahwa Logistics Service Quality berpengaruh signifikan terhadap Kepuasan dan Loyalitas Pelanggan. Kepuasan Pelanggan juga berpengaruh signifikan terhadap Loyalitas Pelanggan. Namun, secara simultan, Logistics Service Quality dan Kepuasan Pelanggan tidak berpengaruh signifikan terhadap Loyalitas Pelanggan. Artinya, meskipun keduanya berpengaruh secara individu, kombinasi keduanya belum cukup kuat untuk membentuk loyalitas pelanggan secara bersama-sama.

Penelitian ini bertujuan untuk menganalisis pengaruh model Discovery Learning berbasis metode eksperimen menggunakan bahan sederhana terhadap hasil belajar IPA pada materi tekanan zat cair. Penelitian kuasi eksperimen ini menggunakan desain Pretest-Posttest Nonequivalent Control Group Design. Sampel penelitian adalah peserta didik Kelas VIII SMP Negeri 11 Jayapura. Sampel penelitian ini terbagi ke dalam dua kelompok yaitu Kelas VIII A dan Kelas VIII C, masing-masing terdiri dari 30 peserta didik. Penentuan sampel dilakukan melalui teknik purposive sampling. Instrumen penelitian meliputi tes hasil belajar pilihan ganda (23 butir valid) dan angket responss peserta didik (25 butir valid). Data dianalisis menggunakan uji N-Gain, independent sample t-test, dan regresi linear sederhana dengan bantuan SPSS 21.0. Hasil penelitian menunjukkan peningkatan hasil belajar pada kelas eksperimen dengan rata-rata N-Gain 0,75 (kategori tinggi), lebih tinggi secara signifikan dibandingkan kelas kontrol yang memperoleh rata-rata N-Gain 0,58 (kategori sedang). Hasil uji statistik mengonfirmasi perbedaan yang signifikan antara kedua kelas (sig. 0,000 < 0,05) serta pengaruh signifikan model pembelajaran terhadap hasil belajar (sig. 0,04 < 0,05). Respons peserta didik terhadap model pembelajaran berada dalam kategori baik dengan rata-rata 61%. Disimpulkan bahwa model discovery learning berbasis eksperimen dengan bahan kontekstual efektif meningkatkan hasil belajar kognitif, keterampilan saintifik, motivasi, dan relevansi pembelajaran IPA (fisika) bagi peserta didik.

The increasing need for construction materials requires companies to manage the efficient distribution of concrete sand building materials. The main problem in distribution is the determination of the optimal delivery route so that transportation costs can be reduced without reducing the timeliness and load capacity of the vehicle. This study aims to determine the optimal distribution route of concrete sand using the Vehicle Routing Problem approach with the Saving Matrix method in TB. Hehe in the city of Bandung. The research was conducted through quantitative and qualitative approaches through observation, interviews, and documentation. Primary data is in the form of existing routes, distances between customers, cargo volume, and vehicle capacity on May 10, 2023. The analysis uses distance matrix calculations, distance savings matrices, and the preparation of alternative routes and actual distances with Microsoft Excel and Google Maps. The results showed that there was no savings in distribution mileage, before the optimization of 103 kilometers and after the application of the Saving Matrix method to 107 kilometers. However, there was a reduction in the number of routes from 10 routes to 5 routes including two routes that were able to optimize truck capacity by 100 cubic meters in one delivery on the route. In terms of cost, there is a fuel saving of Rp. 91,000 per day by combining the destination location in one route.

Cardiovascular disease remains the most significant contributor to global mortality, highlighting the importance of early and precise risk assessment within preventive healthcare frameworks. Alongside the rapid growth of clinical data availability, machine learning approaches have increasingly been adopted to assist medical decision-making, particularly for interpreting complex and high-dimensional health information. This research investigates the predictive capability of six supervised machine learning models in determining the likelihood of cardiovascular disease incidence: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. The Cleveland Heart Disease dataset from the UCI Machine Learning Repository served as the study's foundation. It includes 303 patient samples with a total of 76 recorded attributes. From this dataset, 14 clinically significant variables frequently reported in previous studies were selected for analysis. Considering the relatively small dataset size and the possibility of redundant or low-impact features, a feature selection approach was implemented to improve model robustness, minimize overfitting, and enhance interpretability. The data preparation process involved cleaning, normalization, feature selection, and division into datasets for testing and training. Metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. The results of the experiment show that Random Forest and Logistic Regression models produced the highest predictive performance, followed by k-Nearest Neighbours and Support Vector Machine. These results indicate that supervised machine learning techniques, when supported by appropriate feature selection methods, are effective as decision-support tools for the early detection of cardiovascular disease.

ABSTRACT As supply chains become weaponized in the context of intensifying geoeconomic rivalry, why do similarly exposed industrialized states adopt divergent strategies toward strategic resource dependence? This article examines how the United States and Japan, both reliant on Chinese rare earth elements (REE), respond differently to China's dominance in this sector. We argue that variation in state responses stems from differences in securitization and state–private sector interaction. The United States pursues a relatively hard neo‐mercantilist strategy, characterized by nationalizing supply chains and treating REE access as a security imperative. In contrast, Japan adopts a soft neo‐mercantilist approach, relying on public–private coordination and market‐based diversification. We reconceptualize resource security by introducing a typology of neo‐mercantilist strategies and show how institutional configurations mediate states' responses to coercive economic dependencies. The article contributes to the literature on geoeconomics and international political economy by illuminating how states adapt to the strategic instrumentalisation of technology.

Emotional labor among teachers in Raudhatul Athfal (RA), Islamic early childhood education in Indonesia, is a complex and underexplored phenomenon. This study aims to explore and provide preliminary findings of the emotional labor experiences of RA teachers. Using a qualitative case study design, ten female RA teachers from Bandung, Indonesia, were recruited through convenience sampling. This study utilized an open-ended online survey and Braun and Clarke’s reflexive thematic analysis as data collection method and analysis, respectively. Three themes emerged. The first theme is Multirole Job Demands, with sub-themes: RA teachers’ job characteristics (n=10), multirole demands (n=8), cultural display rules (n=5), and spiritual educators as emotional labor intensifier (n=4). The second theme is Emotional Labor Strategies, with sub-themes: surface acting (n=6), and deep acting (n=3). The last theme is Islamic Framework in Emotional Labor, with sub-themes: Islamic practices as emotional regulation resources and strategies (n=6), spiritual meaning-making (n=9), and workplace spirituality (n=2). The overall emotional labor experiences of the RA teachers are discussed. The findings highlight key constructs that can be explored and directly measured in future research to validate the emotional labor process among RA teachers in Indonesia. Ultimately, this research can contribute to developing support programs for RA teachers that effectively promote their well-being. Keywords: surface acting, deep acting, early childhood teachers, spirituality, Islamic education

Dalam era digital, inovasi pelayanan public berbasis teknologi menjadi kebutuhan mendesak bagi pemerintah daerah dalam meningkatkan partisipasi masyarakat dan kualitas layanan. Transformasi digital dalam pelayanan public tidak hanya bertujuan untuk meningkatkan efisiensi birokrasi, tetapi juga untuk memperkuat keterlibatan masyarakat dalam proses pemerintahan. Penelitian ini bertujuan untuk menganalisis peran inovasi digital dalam meningkatkan partisipasi masyarakat dan kualitas pelayanan public di pemerintaha daerah. Pendekatan penelitian yang digunakan adalah metode kualitatif dengan teknik pengumpulan data melalui srudi literatur dan wawancara dengan pemangku kepentingan terkait. Hasil penelitian menunjukkan bahwa implementasi teknologi digital, seperti e-government, aplikasi layanan public dan sistem berbasis AI, kontribusi dalam menciptakan layanan yang lebih responsive, transparan dan partisipatif. Namun masih terdapat tantangan dalam aspek infrastruktur digital, kesiapan sumber daya manusia, regulasi, dan literasi digital masyarakat. Oleh karena itu, penelitian ini merekomendasikan strategi peningkatan kapasitas teknologi, pengutan regulasi, serta edukasi digital bagi masyarakat dan aparatur pemerintah. Dengan demikian inovasi pelayanan public berbasis digital dapat menjadi katalisator bagi reformasi birokrasi dan peningkatan kualitas layanan kepada masyarakat.

The rapid development of financial technology (fintech) has transformed the way Micro, Small, and Medium Enterprises (MSMEs) manage their financial activities, particularly in urban areas such as Tangerang City. This study aims to examine the impact of fintech innovation, digital trust, and consumer education on the financial performance of MSMEs. A quantitative research approach was employed using primary data collected from 150 MSME owners and managers in Tangerang City through a structured questionnaire measured on a five-point Likert scale. Data analysis was conducted using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 3. The results reveal that fintech innovation has a positive and significant effect on MSME financial performance. Digital trust also shows a significant and positive influence and emerges as the strongest determinant of financial performance. In addition, consumer education significantly contributes to improved financial performance by enhancing financial literacy and the effective use of digital financial services. Collectively, these findings indicate that MSME financial performance in the digital era is driven by an integrated framework combining technological innovation, trust in digital systems, and human capital development. The study provides practical insights for policymakers, fintech providers, and MSME stakeholders in developing inclusive and sustainable digital financial ecosystems.