bluuebunny/crossref_metadata_2025_split huggingface dataset:
Klasifikasi Ekspor Impor Produk Pertanian dengan Metode Deep Learning
Authors: Oktavia Samita , Mambang Mambang , Prasetya M. Riko Anshori , Nurhaeni Nurhaeni , Naparin Husni , Budiman Haldi 
Published: December 2024

Abstrak - Perubahan nilai impor dan ekspor memiliki pengaruh signifikan terhadap pertumbuhan ekonomi suatu negara, dengan inflasi berperan penting dalam mempengaruhi neraca perdagangan. Teknik pembelajaran mesin, khususnya Deep Learning yang merupakan subset dari Machine Learning, menawarkan solusi efektif untuk mengklasifikasi dan mendiagnosis pola dalam data ekspor-impor pertanian. Menggunakan Artificial Neural Network (ANN), yang terinspirasi oleh struktur otak manusia, teknik ini dapat memproses data kompleks untuk mendukung pengambilan keputusan yang tepat dalam analisis perdagangan pertanian. Penelitian ini fokus pada penerapan Deep Learning untuk mengidentifikasi pola ekspor-impor pertanian dengan akurasi tinggi, mencapai 93.7%, dan precision sebesar 89.6%, Recall sempurna sebesar 100% dan F-Measure yang tinggi pada 94.5% menunjukkan keseimbangan antara precision dan recall.Kata kunci: Artificial Neural Network, Deep Learning, Ekspor, Impor, Klasifikasi. Abstract - The changes in import and export values have a significant impact on a country's economic growth, with inflation playing a crucial role in influencing the trade balance. Machine learning techniques, particularly Deep Learning, a subset of Machine Learning, offer effective solutions for classifying and diagnosing patterns in agricultural export-import data. Using Artificial Neural Networks (ANN), inspired by the structure of the human brain, this technique can process complex data to support accurate decision-making in agricultural trade analysis. This research focuses on the application of Deep Learning to identify agricultural export-import patterns with high accuracy, achieving 93.7%, precision of 89.6%, perfect recall of 100%, and a high F-Measure of 94.5%, indicating a balance between precision and recall.Keywords: Artificial Neural Network, Classification, Deep Learning, Ekspor, Impor
Implementasi Algoritma Fisher-Yates untuk Pengacakan Dinamis Soal TOEFL dalam Ujian Online Berbasis Web
Authors: Abdi Musta'inul , Amru Amru , Rafiqa Siti Fathiyya , Safriadi Safriadi 
Published: December 2024

Abstrak -Pengacakan soal dalam ujian online, seperti TOEFL, adalah salah satu metode penting untuk menjaga keamanan dan keadilan ujian. Penelitian ini berfokus pada implementasi Algoritma Fisher-Yates sebagai solusi untuk pengacakan dinamis soal TOEFL dalam platform ujian berbasis web. Algoritma ini dipilih karena kompleksitasnya yang rendah, yakni O(n), sehingga mampu mengacak soal dengan efisien bahkan dalam jumlah besar. Pengujian dilakukan dengan berbagai jumlah soal, mulai dari 50 hingga 1000, untuk mengukur performa waktu eksekusi pengacakan. Hasil penelitian menunjukkan bahwa waktu eksekusi pengacakan meningkat secara linier seiring bertambahnya jumlah soal, namun tetap berada dalam skala milidetik, menjadikannya sangat cepat dan efisien untuk diterapkan dalam skenario ujian real-time. Pengacakan yang dilakukan oleh algoritma ini terbukti konsisten dan efektif, menghasilkan urutan soal yang berbeda di setiap sesi ujian tanpa perubahan pada tingkat kesulitan keseluruhan. Implementasi ini dapat mencegah kecurangan dan meningkatkan keadilan antar peserta ujian. Secara keseluruhan, penelitian ini menegaskan bahwa Algoritma Fisher-Yates adalah pilihan yang tepat untuk pengacakan soal dalam ujian online berskala besar seperti TOEFL, karena mampu mengombinasikan kecepatan, efisiensi, dan keamanan dalam satu solusi yang stabil dan mudah diimplementasikan.Kata kunci: TOEFL, Fisher-Yates, Pengacakan Abstract - Randomizing questions in online exams, such as TOEFL, is crucial for ensuring exam security and fairness. This study focuses on implementing the Fisher-Yates algorithm as a solution for dynamic question randomization in web-based exam platforms. The algorithm was chosen due to its low complexity, O(n), making it efficient even for large numbers of questions. Testing was conducted with various quantities of questions, ranging from 50 to 1000, to evaluate the execution time performance. The results indicate that execution time increases linearly with the number of questions but remains within the millisecond range, demonstrating its suitability for real-time exam scenarios. The randomization performed by the algorithm proved consistent and effective, producing different question sequences for each exam session without altering the overall difficulty level. This implementation helps prevent cheating and enhances fairness among exam participants. Overall, this research underscores that the Fisher-Yates algorithm is an optimal choice for question randomization in large-scale online exams like TOEFL, as it combines speed, efficiency, and security into a stable and easily implementable solution..Keywords: TOEFL, Fisher-Yates, Randomizing
Penerapan Data Mining untuk Klasifikasi Penyakit Diabetes Menggunakan Metode Decision Tree
Authors: Haryadi Alya Nabila Putri , Setiawan Wahyudi , Fatah Doni Abdul 
Published: December 2024

Abstrak - Diabetes merupakan penyakit yang bukan hanya menyerang orang dengan usia lanjut, akan tetapi saat ini banyak pula anak remaja bahkan anak dibawah umur yang sudah menderita diabetes. Hal ini dikarenakan pola hidup yang tidak sehat. Disamping makanan dan minuman yang dikonsumsi banyak mengandung gula, penyakit ini juga sering disebabkan oleh kurangnya aktivitas pada sehari-hari. Maka dirasa sangat perlu dilakukan penelitian semacam ini guna mencegah dan juga menolong untuk mengobati pasien yang sudah terkena penyakit diabetes maupun yang beresiko terkena penyakit diabetes. Maka untuk membantu dalam bidang kesehatan, maka dilaksanakan penelitian ini dengan menggunakan metode algoritma Decision Tree, dan menggunakan tools RapidMiner. Dalam penggunaan metode algoritma Decision Tree dalam klasifikasi penyakit diabetes hasil Akurasi yang dihasilkan yaitu 77,34% , Precision yaitu 75,08%, Recall 97,60%, dan F1 Score sebesar 0,8486, hal ini menunjukkan bahwa ketepatan akurasi dalam memprediksi klasifikasi data penyakit diabetes dengan menggunakan metode decision tree adalah 84,86%. Sehingga dirasa metode ini memiliki tingkat akurasi yang cukup tinggi.Kata kunci: Data Mining, Klasifikasi, Diabetes, Pohon Keputusan, Rapid Miner Abstract - Diabetes is a disease that not only attacks people with old age, but currently many teenagers and even minors already suffer from diabetes. This is due to an unhealthy lifestyle. In addition to the food and drinks consumed containing a lot of sugar, this disease is also often caused by a lack of daily activity. So it is felt that this kind of research is very necessary to prevent and also help treat patients who already have diabetes or who are at risk of developing diabetes. So to help in the health sector, this research was carried out using the Decision Tree algorithm method, and using the RapidMiner tool. In using the Decision Tree algorithm method in the classification of diabetes, the resulting Accuracy results are 77.34%, Precision is 75.08%, Recall 97.60%, and F1 Score of 0,8486, this shows that the accuracy of the accuracy in predicting the classification of diabetes data using the decision tree method is 84.86%. So it is felt that this method has a fairly high level of accuracy.Keywords:Data Mining, Classification, Decision Tree, Diabetes, Rapid Miner
Pengaruh Celebrity Endorser, Kualitas Produk, dan Gaya Hidup Terhadap Keputusan Pembelian Pada Skincare (Studi Pengguna Skincare Skintific di Kota Cirebon)
Authors: Viora Siska , Wulandari Oryz Agnu Dian , Yuningsih Yuningsih 
Published: December 2024

Tujuan studi ini ialah untuk memiliki pengetahuan dalam menganalisis dampak celebrity endorser, kualitas produk, dan gaya hidup pada keputusan pembelian pada skintific khususnya pada pemakai skintific di Kota Cirebon. Penelitian berikut sifatnya aspiratif yang mana bisa merumuskan permasalahan penelitian perihal menemukan klausaitas diantara variabel ketika mencari suatu dampak terkait pada variabel independen dan dependen. Studi yang dilaksanakan mempergunakan metode quantitative dengan menyebarluaskan kuesioner terhadap pengguna skincare skintific di Kota Cirebon dengan sampel 100 responden. Penilaian yang dipergunakan pada research ini ialah uji validitas, reliabilitas, asumsi klasik, uji analisis regresi berganda, uji parsial, uji simultan dan koefisien determinasi. Studi ini dinilai menggunakan alat analisis SPSS 26. Variabel Variabel independet dalam studi ini yaitu celebrity endorser (x1), kualitas produk (x2), gaya hidup (x3) serta variabel dependen ialah keputusan pembelian (y). Perolehan studi mengindikasikan jika variabel celebrity endorser tidak signifikan, variabel kualitas positif dan signifikan, variabel gaya hidup berdampak positif serta signifikan. Variabel celebrity endorser, kualitas produk, dan gaya hidup berdampak signifikan secara simultan kepada keputusan pembelian.
Model peningkatan kinerja dosen melalui penerapan Organizational Happiness Character dan Komitmen organisasi
Authors: Agustina Granit , Novedliani Riztrya , Febrian Feby 
Published: December 2024

Kondisi kelembagaan merupakan aspek penting dalam peranannya mendongkrak peringkat perguruan tinggi dimana kondidi kelembagaan sanganlah mepengruhi kinerja dosen. Banyak factor yang diasumsikan dalam peningkatan kinerja dosen salah satunya adalah organizational happiness character (OHC). Penelitian ini bertujuan untuk mengetahui peran organizational happiness character dalam mempengaruhi kinerja dosen dimediasi oleh komitmen organisasi. Penelitian dilakukan keada para dosen dilingkungan perguruan tinggi swasta di wilayah Bandung timur. Jumlah sample penelitian sebanyak minimal 200 dosen yang diperoleh dari 5 perguruan tinggi swasta di Bandung timur. Data penelitian dikumpulkan dengan menggunakan kuesioner menggunakan skala semantic differensial. Teknik analisis data yang digunakan yaitu analis SEM-AMOS. Penelitian ini dilakukan untuk melihat persepsi dosen terhadap organizational happiness character, komitmen organisasi dan kinerja dosen dengan menguji 4 hipotesis penelitian. Dari hasil pengjuin hipotesis diperoehl hasil bahwa organizational happiness character memiliki ubungan positif signifikan terhadap kinerja dosen. organizational happiness character (OHC) dan komitmen organisasi juga telah terbukti memberikan pengaruh positif dan signifikan dan komitmen organisasi juga memberikan pengaruh positif signifikan terhadap peningkatan kinerja dosen. Dari hasil penelitian ini juga dapat diperlihatkan bahwa komitmen organisasi memberikan pengaruh mediasi secara parsial dalam peningkatan kinerja dosen sehingga dapat dikatakan bahwa komitmen organisasi merupakan variable yang dapat memediasi organizational happiness character terhadap kinerja dosen
The effect of educational intervention through sending emails on improving physical posture in female computer users of Eastern Iran: a quasi-experiment study
Authors: Hosseini Zahra , Ziapour Arash , Rahimi Seyyede Fateme , Dalake Fatemeh , Yıldırım Murat 
Published: December 2024

BackgroundMusculoskeletal disorders are among the most common occupational injuries and disabilities in developing and industrialized countries. This study aims to determine the effectiveness of e-mail training to improve the physical posture of female computer users at Birjand University of Medical Sciences in Iran.MethodsThe present interventional research explores the effect of email-based training to correct the body posture of female computer users in Birjand, Iran. In this quasi-experiment, 120 women who worked in Birjand University of Medical Sciences using computers were selected through a census. 60 computer users were selected from the deputy of education and 60 from the deputy of development for the intervention group (IG) and control group (CG), respectively. A training program was developed on the ergonomics of office work (12 emails at an interval of 6 weeks). The data was collected using demographic, occupational information, and a knowledge assessment questionnaire. Nordic Musculoskeletal Questionnaire (NMQ) and Rapid Office Strain Assessment (ROSA) were used in both groups before the intervention and 6 months later.ResultsAfter the educational intervention, a significant increase was observed in the ergonomics knowledge of the IG compared to the control. The ROSA score was lowered from a high-risk to a low-and medium-risk level (p &lt; 0.05). In the IG, 44 subjects (73.30%) who needed ergonomic intervention (a score above 5) were reduced to 10 subjects (16.70%) with a need for ergonomic intervention. According to NMQ, the highest frequency of pain in the IG and CG was related to the back (56.70% and 55%, respectively). The neck, shoulders, wrists, back and elbows were next.ConclusionsThis quasi-intervention study was conducted to determine the effect of email-based training on correcting female computer users’ body posture in 2022. Training ergonomics through email is a practical and acceptable way to improve ergonomic behaviors among computer users. It enables them to adapt to the workplace by applying the correct ergonomics, changing their work behavior to prevent occupational musculoskeletal disorders, and reduce risks and complications.
Clinical and neuroimaging characteristics of diabetic striatopathy: a case series report
Authors: Chen Yaning , Wu Chunliu , Ren Ming , Wang Qingjun , Wang Zhiwei , Zhang Yimo , Yu Yingxin 
Published: December 2024

BackgroundDiabetic striatopathy (DS) is a rare disorder characterized by clinical manifestations of hemichorea, non-ketotic hyperglycemia, and high signal on T1-weighted MRI or high density on CT scan in basal ganglia, typically associated with poor glycemic control.ObjectiveThis study aimed to analyze clinical characteristics of patients with diabetic striatopathy to raise awareness amongst physicians, especially endocrinologists, about this rare neurological manifestation in patients with diabetes.MethodsWe retrospectively analyzed the data on clinical presentations, laboratory workups, and cranial CT and MRI of six patients with DS who were admitted to our hospital from October 2013 to June 2022.ResultsThe mean age of onset among the six patients was 80.5 years, and the mean value of HbA1c was 13.65%. All six patients complained of involuntary movements, which primarily affected the arm and leg on one side of the body. Bilateral caudate nucleus hyperdensities were shown on the CT examination in Case 3,while the other 5 patients, unilateral caudate nucleus hyperdensity was shown. In addition, five patients (except Case 5) underwent MRI, all showing hypersignal lesions on the T1-weighted images. A low signal in the right basal ganglia was shown on MRI susceptibility weighted imaging (SWI) sequences in Case 6. All six patients exhibited carotid artery or cerebral artery stenosis. Following strict blood glucose control and symptomatic management, the symptoms of chorea improved significantly in all patients, and repeat images indicated that the lesions gradually disappeared.ConclusionBoth poor vascular conditions and severe hyperglycemia contribute to the development of diabetic striatopathy. The prognosis is usually good by active treatment.
Traditional versus intensive blood glucose control: long-term target range duration and cardiovascular disease risk and all-cause mortality - a real-world cohort study
Authors: Zhang Jingdi , Li Liuxin , Luo Donglei , Huo Zhenyu , Zhang Xiaoxue , Xu Yiran , Jiang Jingyuan , Liu Tiantian , Wu Shouling , Huang Zhe 
Published: December 2024

ObjectiveFor a long time, the dispute about whether improved glycemic control can bring significant benefits has remained unresolved. We aimed to investigate the association of time spent in different fasting glucose target ranges with cardiovascular risk and all-cause mortality in a population with type 2 diabetes (T2DM).MethodA total of 3460 T2DM patients in the Kailuan cohort were included in this study. The Time in Target Range (TITRE) was calculated as the proportion of time that fasting blood glucose (FBG) was within the usual glycemic control range and the intensive glycemic control range between 2006 and 2016. The Cox proportional hazards regression model analyzed the relationship between TITRE, defined by different glucose ranges, and cardiovascular disease and all-cause mortality.ResultsDuring a median follow-up of 4.23 years, a total of 360 CVDs and 238 deaths were recorded. After correcting for traditional risk factors, we observed that in the conventional blood glucose control group, each increase of 1 standard deviation in TITRE was associated with a 23% (HR, 0.77; 95%CI, 0.68-0.87) reduction in CVD risk and a 20% reduction in all-cause mortality (HR, 0.80; 95%CI, 0.69-0.92). Similar results were also observed in the intensified blood glucose control group. In the conventional blood glucose control group, participants with TITRE of 50% or more had an absolute incidence rate of CVD of 16.77%, whereas in the intensified blood glucose control group, participants with TITRE of 50% or more had an absolute incidence rate of CVD of 11.82%.ConclusionsIn patients with type 2 diabetes, achieving near-normal blood glucose levels appears to significantly reduce the risk of diabetes-related cardiovascular outcomes.
Short-term effects of temperature and air pollution on mortality in Norway: a nationwide cohort-based study
Authors: Rao Shilpa , Zhang Siqi , Ahimbisibwe Ashley , Bekkevold Terese , Di Ruscio Francesco , Diz-Lois Palomares Alfonso , Frohn Lise Marie , Geels Camilla , Vázquez Fernández Liliana , Schneider Alexandra 
Published: December 2024

BackgroundThis time-stratified case-crossover study examined short-term associations of air temperature with cause-specific mortality (natural-cause, cardiovascular and respiratory) and potential effect modification by daily mean air pollution concentrations and individual characteristics in the Cohort of Norway (CONOR) cohort.MethodsThe CONOR cohort recruited ∼173,000 participants from 1994 to 2003. Participants’ vital status and the cause of death were collected from the Cause of Death Registry of Norway until 2018. Daily mean air temperatures and concentrations of fine particulate matter (PM2.5) and ozone (O3) estimated by spatial-temporal models were assigned to participants’ residences. We applied conditional logistic regression models with the distributed lag non-linear model approach to assess cold and heat effects on cause-specific mortality. The potential effect modification was analyzed by incorporating an interaction term between air temperature and the modifier in the regression model. The cold and heat effects were estimated for different subgroups of participants and at the low (5th percentile), medium (50th percentile), and high (95th percentile) levels of air pollution.ResultsWe observed an increased risk of natural-cause mortality (OR: 1.26 95% CI: 1.09, 1.46) for a decrease in temperature from the minimum mortality temperature (MMT, 17.6°C) to the 1st percentile and an increased risk of cardiovascular mortality (OR: 1.32, 95% CI: 1.04, 1.67) for a decrease from MMT (16.1°C) to the 1st percentile. The cold effect on natural-cause mortality was more pronounced among women, former smokers, those aged below 75 years and people with a history of cardiovascular diseases. The cold effect on natural-cause mortality was stronger at higher levels of air pollution in winter, and the heat effect on cardiovascular mortality were stronger with elevated air pollution levels in summer.ConclusionWe find adverse short-term cold effects on overall mortality in Norway. We further observe significant risk of cold related effects on natural and cardiovascular mortality in the wintertime and increased risk of respiratory mortality due to higher temperatures in the summer. We identified subpopulations who were likely to be more at risk for temperature-related mortality. We also see that increased air pollution impacts temperature related mortality in winter and summer time.
Case report: Low-dose interleukin-2: a treatment of bullous pemphigoid with predominantly perifollicular blistering caused by PD-1/PD-L1 inhibitor
Authors: Zhang Yingyue , Cao Xianwei , Tong Jianbo 
Published: December 2024

ObjectivesThis study aimed to evaluate the efficacy of low-dose interleukin (IL-2) treatment for bullous pemphigoid (BP) caused by anti-programmed cell death protein 1/ligand 1 (PD-1/PD-L1) inhibitors.MethodsLow-dose IL-2 treatment was standardized for BP. The Bullous Pemphigoid Disease Area Index (BPDAI), 5D-Itch Scale (5D-IS), and Dermatology Life Quality Index (DLQI) were recorded before and after treatment, and hexachromatic lymphocytes, regulatory T cells (Treg cells), and cytokines were measured.ResultsA significant decline in the BPDAI score, 5D-IS, and DLQI score was observed following treatment. The count of B-cells, CD4+ T-cells, CD8+ T-cells, Treg cells, and the levels of cytokines (IL-4, -8, and -10) were significantly downregulated in comparison to baseline measurements.ConclusionLow-dose IL-2 could be an effective therapeutic choice for treating BP caused by PD-1/PD-L1 inhibitors.
Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations
Authors: Lin Zheng , Pan Si-yi , Shi Yue-yi , Wu Xuan , Dou Yuan , Lin Ping , Cao Yi 
Published: December 2024

BackgroundPsoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease screening model and the Psoriatic Arthritis Inflammation Index (PSAII) based on serologic data to enhance the efficiency of PSA screening.MethodA total of 719 PSO and PSA patients from the National Health and Nutrition Examination Survey (NHANES) (as training set and test set) and 135 PSO and PSA patients who were seen at The First Affiliated Hospital of Zhejiang Chinese Medical University (as external validation set) were selected, 31 indicators for these patients were collected as potential input features for the model. Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify PSA-related features. Five models of logistic regression (LR), random forest, k-nearest neighbor, gradient augmentation and neural network were developed in the training set using quintuple cross validation. And we developed PSAII based on the results of LASSO regression and weights of logistic model parameters. All performance metrics are derived on the test set and the external validation set.ResultsFive variables were selected to build models, including age, lymphocyte percentage, neutrophil count, eosinophilic count, and C-reactive protein. In all established models, the LR model performed the best, with an Area Under Curve (AUC) of 0.87 (95% confidence interval (CI): 0.83-0.90) on the test set; on the external validation set the AUC was 0.82 (95%CI: 0.74-0.90). The PSAII formula was PSAII = percentage of lymphocytes × C-reactive protein/(neutrophil count × eosinophilic count × 10). The AUC of PSAII in the test is 0.93 (95%CI: 0.88-0.97), and the cutoff value is 18. The AUC of the external validation set is 0.81 (95%CI: 0.72-0.89).ConclusionsThis study developed and validated five models to assist screening for PSA by analyzing serum data from NHANES and Chinese populations. The LR model demonstrated the best performance. We created PSAII for PSA screening. However, the high false positive rate of PSAII makes it necessary to combine it with other PSA screening tools when applied.