bluuebunny/crossref_metadata_2025 huggingface dataset:
A Cross-Language Information Retrieval Method Based on Multi-Task Learning
Authors: Linli Peng
Published: September 2024

This study introduces a novel Cross-Language Information Retrieval (CLIR) method employing multi-task learning and soft parameter sharing to enhance neural retrieval models' feature extraction across languages. The approach integrates an interaction-based neural retrieval model with a semantic-based text classification model, exchanging hidden vectors for richer feature representation. Experimental results across four language pairs—English-Chinese, English-Arabic, English-French, and English-German—demonstrate significant performance improvements. The proposed method achieved the highest Mean Average Precision (MAP) scores: 0.419 for EN-ZH, 0.403 for EN-AR, 0.427 for EN-FR, and 0.441 for EN-DE, surpassing other models like BM25, BPNRM, KNRM, KNRM-Trans, and KNRM-Embed. This research underscores the potential of multi-task learning for CLIR, showcasing improved retrieval performance through semantic information and knowledge transfer.
Pengembangan Sistem Pengolahan Data Jemaat Gereja Kristen Sumba (GKS) Berbasis Object Oriented Analysis and Design
Authors: Hanggalang Ronaldo Turu , Lede Pingky Alfa Ray Leo , Sitaniapessy Desy Asnath
Published: September 2024

GKS Jemaat Tanalingu Cabang Kahembi merupakan gereja yang belum memanfaatkan teknologi komputer dan sistem informasi dalam pengelolaan data keanggotaan, antara lain pencatatan baptisan, pencatatan pengukuhan, pencatatan kematian, dan pencatatan perkawinan. Dalam proses pengolahan data Gereja Kristen Sumba (GKS) Jemaat Tanalingu Cabang Kahembi pada saat ini, informasi yang saya dapatkan dari Ketua BPMJ sekaligus merangkap menjadi pendeta dalam gereja tersebut yang menjadi keluhan mereka yaitu mengalami kesulitan dalam munulis data dalam kertas buku dan mudah tercecer dan cepat rusak dalam mengelola data jemaat, data jemaat pendaftaran baptis, data jemaat pendaftaran sidi, data jemaat meninggal dan data jemaat pendaftaran nikah. Peneliti ini bertujuan untuk merancang dan mengembangkan sistem informasi untuk membantu dalam mengelolaan data di GKS Jemaat Tanalingu Cabang Kahembi. Metodologi pendekatan Object-Oriented Analysis and Design (OOAD) serta menerapkan metode waterfall, penelitian ini merancang sistem yang mempermudah gereja mengakses data jemaat secara online. Dalam sistem ini tidak hanya dapat mengatasi masalah keterlambatan dan tidak dapat mengalami kesulitan dalam munulis data dalam kertas buku dan tidak mudah tercecer dan cepat rusak dalam pencatatan manual. Dengan demikian, pengurus gereja dapat lebih mudah mengelola mengelola data jemaat, data jemaat pendaftaran baptis, data jemaat pendaftaran sidi, data jemaat meninggal dan data jemaat pendaftaran nikah.
Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping
Authors: Duan Lianzhai
Published: September 2024

Online shopping merchants will conduct a series of marketing activities to increase customers, but in many cases, most of the new customers will not make repeat purchases, which is not conducive to the long-term interests of the merchants. Therefore, it is important for merchants to target users who are more likely to repurchase, as this can reduce marketing costs and increase ROI. Based on the dataset provided by the online shopping website, this paper conducts mining and exploratory analysis of the data, utilizes feature engineering methodology, and modeling analysis using LightGBM, Logistic, Xgboost for machine learning modeling. Meanwhile, parameter optimization and model evaluation verification are performed, Finally, the comparative analysis resulted in LightGBM as the best prediction model, will provide efficient marketing decisions for the operation of online shopping stores.
Analysis of WiFi Reliability at 2.4 GHz and 5 GHz Frequencies in the Environment of STMIK Sinar Nusantara Surakarta
Authors: Yunanta Raymundus Erik Chandra , Susyanto Teguh
Published: September 2024

This research is expected to provide solutions to improve the performance and reliability of the WiFi network at STIMIK Sinar Nusantara, thus supporting teaching and learning activities and enhancing the quality of education. By comparing the quality of WiFi networks between 2.4 GHz and 5 GHz, it aims to shed light on the differences and advantages of each frequency band. Furthermore, the findings of this research can be beneficial for the design of future networks, helping network designers to better understand the importance of frequency management in network infrastructure planning. This research employs an experimental method by testing QoS parameters (throughput, delay, and packet loss) as well as reliability (connection and uptime) at both frequencies. The results of the research indicate that the 5 GHz frequency provides better QoS performance and reliability compared to 2.4 GHz. This research concludes that the 5 GHz frequency could be a solution to improve the WiFi network quality at STIMIK Sinar Nusantara
Prediksi Angka Kelahiran dalam Berbagai Kelompok Umur Ibu Menggunakan Metode K-Nearest Neighbor
Authors: Syafana Vinka , Hilabi Shofa Shofiah , Novalia Elfina , Huda Baenil
Published: September 2024

Studi ini bertujuan untuk mengembangkan prediksi angka kelahiran dalam berbagai kelompok umur ibu melalui pendekatan metode K-Nearest Neighbors (K-NN) dalam suatu sistem informasi. Penelitian ini difokuskan pada analisis dinamika kelahiran dalam konteks kelompok umur ibu, yang merupakan informasi kritis dalam perencanaan kebijakan kesehatan dan pengembangan sosial. Metode K-NN digunakan sebagai pendekatan analisis utama untuk meramalkan angka kelahiran, memanfaatkan pola kemiripan dalam karakteristik kelompok umur ibu. Integrasi metode K-NN dalam sistem informasi memungkinkan pengelolaan dan analisis data yang lebih efisien untuk mendukung kebijakan perencanaan keluarga. Data yang digunakan mencakup variabel demografis, ekonomi, dan sosial yang dapat memengaruhi tingkat kelahiran. Hasil prediksi angka kelahiran diharapkan dapat memberikan wawasan mendalam tentang perubahan dinamis dalam struktur kelahiran, memungkinkan pemahaman yang lebih baik untuk mengarahkan kebijakan kesehatan dan strategi intervensi yang lebih terarah. Penelitian ini menghadirkan kontribusi pada pengembangan sistem informasi yang dapat mendukung analisis prediktif dalam konteks kelahiran. Implikasi temuan ini relevan untuk kebijakan kesehatan, demografi, dan perencanaan keluarga, serta dapat membantu merancang langkah-langkah intervensi yang lebih efektif dan tepat sasaran.
Penerapan Teknologi LangChain pada Question Answering System Fikih Empat Madzhab
Authors: Rahayu Suci , Harahap Nazruddin Safaat , Agustian Surya , Pizaini Pizaini
Published: September 2024

Fikih sebagai ilmu yang luas, terkadang menimbulkan beragam persoalan dan perbedaan pandangan antara madzhab-madzhabnya. Tujuan pandangan ulama tentang isu-isu fikih adalah untuk memperkaya opsi pemahaman, bukan menyebabkan perpecahan. Keberadaan mazhab penting bagi umat Islam awam dalam memahami hukum Islam, karena membantu dalam menafsirkan Al-Qur'an dan Hadits untuk masalah sehari-hari. Pengiriman informasi saat ini dapat dilakukan dengan cepat dan mudah, salah satunya melalui aplikasi tanya jawab atau Question Answering System (QAS) terkait materi yang ingin diketahui oleh pengguna. Sehingga pada penelitian ini bertujuan membuat sebuah QAS berbasis web tentang fikih empat madzhab menggunakan teknologi LangChain dan Large Language Model (LLM). LangChain dan model LLM mampu memberikan jawaban atas pertanyaan terkait file Portable Document Format (PDF). QAS dilatih menggunakan kumpulan data berupa file PDF serta memanfaatkan model LLM untuk menghasilkan respons teks yang relevan terhadap pertanyaan yang diajukan oleh pengguna. Sistem yang telah dikembangkan berhasil memberikan respons kepada pengguna dengan pengujian menggunakan BERTScore yang mendapatkan nilai rata-rata dari precision sebesar 80%, recall sebesar 81%, dan f-1 score sebesar 81%. Sedangkan ROUGEScore mendapatkan nilai rata-rata dari ROUGE-1 sebesar 56%, 58%, dan 56%, ROUGE-2 sebesar 33%, 33%, 33%, dan ROUGE-L sebesar 43%, 44%, dan 43%.
Implementasi Algoritma Convolutional Neural Network (Resnet-50) untuk Klasifikasi Kanker Kulit Benign dan Malignant
Authors: Gusti Gogor Putra Hafi Puja , Haerani Elin , Syafria Fadhillah , Yanto Febi , Gusti Siska Kurnia
Published: September 2024

Kulit sebagai organ terluar yang menutupi seluruh bagian tubuh manusia rentan terhadap berbagi penyakit, salah satunya kanker kulit. Penggunaan teknologi malignant, khususnya Convolutional Neural Network (CNN) diangkat menjadi topik penelitian karena kemampuan CNN untuk secara otomatis mengenali fitur penting dalam klasifikasi citra medis kanker kulit. Oleh karena itu dilakukan penelitian pengklasifikasian penyakit kanker kulit benign (jinak) dan malignant (ganas) menggunakan algoritma CNN arsitektur ResNet-50 dengan dataset berupa 5000 data latih kanker kulit benign dan 4600 data latih kanker kulit malignant.Model CNN yang telah dirancang dengan epoch 50 menggunakan optimizer Adam dan batch size sebesar 54 serta melibatkan beberapa teknik augmentasi data guna meningkatkan keragaman dataset untuk kemudian model hasil perancangan diimplementasikan ke dalam tampilan sebuah website dengan menggunakan Flask sebagai kerangka kerja yang menghubungkan antara model deep learning dan website agar bisa diakses oleh pengguna. Metode pengujian blackbox dilakukan demi memastikan sistem dapat melakukan klasifikasi kanker kulit melalui input berupa citra medis kedalam 2 kelas yaitu benign dan malignant dengan baik serta didapatkan hasil akurasi model sebesar 94,88 % dan loss sebesar 13,24%.
Comparative Analysis of Neural Network Architectures for Predicting Chronic Disease Indicators Using CDC’s Chronic Disease Indicators Dataset
Authors: Airlangga Gregorius
Published: September 2024

This research evaluates the performance of three machine learning models—Neural Network (NN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units—in predicting chronic disease indicators using the CDC's Chronic Disease Indicators (CDI) dataset. The study employs a comprehensive preprocessing pipeline and 5-fold cross-validation to ensure robustness and generalizability of the results. The CNN model outperformed both the NN and RNN models across all key performance metrics, achieving an accuracy of 0.6303, precision of 0.6445, recall of 0.6303, and F1 score of 0.5950. The superior performance of the CNN is attributed to its ability to capture spatial hierarchies and interactions within the structured dataset. The findings underscore the importance of selecting appropriate machine learning architectures based on the data characteristics. This research provides valuable insights for public health officials and policymakers to enhance chronic disease monitoring, early detection, and intervention strategies. Future work will explore hybrid models and advanced techniques to further improve predictive performance. This study highlights the potential of CNNs in public health informatics and sets a foundation for further research in this domain
Pengelompokan Data Pendistribusian Listrik Menggunakan Algoritma Mini Batch K-Means Clustering
Authors: Mulyadi Syahrul , Insani Fitri , Agustian Surya , Afriyanti Liza
Published: September 2024

Manajemen distribusi listrik merupakan aspek penting dalam infrastruktur yang memerlukan pemahaman mendalam tentang pola distribusi di berbagai wilayah untuk memastikan pasokan listrik stabil bagi masyarakat dan pemerintah. Namun, mengelola data distribusi listrik yang melibatkan berbagai variabel seperti rumah tangga, industri, bisnis, sosial, gedung kantor pemerintah, dan jalan umum membutuhkan pendekatan yang efektif dalam mengelompokkan data untuk mengidentifikasi pola-pola yang signifikan. Penelitian ini bertujuan mengimplementasikan algoritma mini batch k-means untuk mengelompokkan distribusi listrik di setiap wilayah. Data yang digunakan dalam penelitian ini adalah data distribusi listrik dari tahun 2014-2022 yang terdiri dari 35 provinsi. Hasil pengujian menunjukkan bahwa terdapat tiga cluster yang signifikan dalam data peneliti, masing-masing ditandai dengan Silhouette Score yang berbeda. Cluster dengan Silhouette Score tertinggi adalah Cluster 2 dengan nilai 0.625, menunjukkan kohesi yang tinggi di wilayah regional Kalimantan-Sulawesi yang diamati dalam cluster 2 ini. Sementara itu, Cluster 4 memiliki Silhouette Score yang terendah dengan nilai 0.419, menunjukkan tingkat kohesi yang lebih rendah dalam pola distribusi listrik di setiap wilayah regional bagian Kalimantan-Sulawesi. Penelitian ini menegaskan bahwa algoritma mini batch k-means efektif untuk pengelompokkan data distribusi listrik dengan hasil yang memuaskan dalam pemisahan klaster yang berbeda
Pengelompokan Data Pendistribusian Listrik Menggunakan Algoritma Density Based Spatial Clustering of Application With Noise (DBSCAN)
Authors: Farid Miftah , Insani Fitri , Agustian Surya , Afriyanti Liza
Published: September 2024

Pada masa kini, listrik sudah menjadi kebutuhan penting dalam kehidupan, karena kebanyakan aktivitas manusia bergantung pada listrik. Kebutuhan listrik pada setiap wilayah di Indonesia dipengaruhi oleh sejumlah faktor dan karakteristik khusus masing-masing. PLN mempublikasikan statistik penggunaan listrik untuk setiap wilayah di Indonesia dari tahun 2014 hingga 2022, yang terdiri dari 35 provinsi di Indonesia. Data ini menawarkan wawasan berharga untuk prediksi permintaan listrik, pelacakan tren historis untuk memprediksi pengembangan wilayah, memprioritaskan wilayah dengan permintaan tinggi untuk efisiensi dan konservasi energi, dan lain sebagainya.  Salah satu alat untuk mencapai tujuan tersebut adalah dengan mengelompokkan (clustering) wilayah berdasarkan karakteristik dan ciri-ciri wilayahnya. Penelitian ini melakukan proses clustering dengan membagi data pada tiga regional utama: Sumatera, Jawa-Bali, dan Kalimantan-Sulawesi, sementara regional Papua tidak dianalisis karena jumlah wilayah/propinsi yang terbatas. Metode yang dipakai adalah Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Tuning parameter dengan cara pencarian grid dilakukan untuk memperoleh hasil optimal berdasarkan silhouette score. Hasil clustering dapat memberikan gambaran keunikan profil konsumsi listrik di tiap wilayah, dengan silhouette score terbaik sebesar 0.62 untuk regional Jawa-Bali, 0,67 untuk Kalimantan-Sulawesi, dan 0,64 untuk Sumatera. Penelitian menunjukkan bahwa algoritma DBSCAN dapat digunakan untuk pengelompokkan distribusi listrik dengan hasil yang efektif.
Pengelompokan Data Pendistribusian Listrik Menggunakan Algoritma Mean Shift
Authors: Utari Roid Fitrah , Insani Fitri , Agustian Surya , Afriyanti Liza
Published: September 2024

Penelitian ini mengkaji regionalisasi dan klasterisasi data distribusi listrik di Indonesia menggunakan algoritma Mean Shift, dengan tujuan untuk meningkatkan efisiensi distribusi energi di berbagai wilayah geografis yang beragam. Listrik memiliki peran krusial dalam kehidupan modern namun distribusinya masih belum merata, terutama di daerah terpencil dan pedesaan yang terkendala oleh akses dan keterbatasan dana. Sebagai salah satu Bada Usaha Milik Negera (BUMN)  utama di sektor ketenagalistrikan, Perusahaan Listrik Negera (PLN) bertanggung jawab dalam menyediakan listrik di seluruh Indonesia, mendukung pertumbuhan ekonomi melalui penyediaan energi untuk sektor industri, pertanian, dan perdagangan. Dengan menggunakan algoritma Mean Shift, penelitian ini mengelompokkan Indonesia menjadi Sumatra, Jawa-Bali, Kalimantan-Sulawesi, dan Papua berdasarkan pola distribusi listrik, dengan menemukan bahwa pengaturan bandwidth optimal 0.5 menghasilkan tiga klaster per wilayah yang mencerminkan infrastruktur serupa, kebutuhan energi, dan sektor ekonomi dominan. Temuan ini menunjukkan fleksibilitas Mean Shift dalam menangani struktur data yang kompleks tanpa jumlah klaster yang telah ditentukan sebelumnya, yang penting untuk perencanaan strategis dalam pengelolaan energi di Indonesia demi mencapai distribusi listrik yang lebih efisien dan berkelanjutan
Comparison of the Accuracy of The Bahasa Isyarat Indonesia (BISINDO) Detection System Using CNN and RNN Algorithm for Implementation on Android
Authors: Aryananda I Gusti Agung Oka , Samopa Febriliyan
Published: September 2024

Communication is a process of exchanging information that aims to establish relationships between humans. Communication difficulties are an obstacle for people with deaf disabilities or often called Deaf Friends, where they find it difficult to interact with friends around them. Sign language is the main medium of communication used worldwide by people with disabilities i.e. deaf and speech impaired. Communication between deaf people and those around them is often an obstacle because most people do not understand sign language which is often used as a medium of communication by deaf people. In dealing with this problem, researchers want to analyze the accuracy level of the Android-Based Bahasa Isyarat Indonesia Detection System (BISINDO) using the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods in order to determine which methods can be implemented to an Android device. This study shows that Convolutional Neural Network (CNN) has a greater and more stable accuracy rate compared to the Recurrent Neural Network (RNN) model where the CNN model produces an accuracy rate of 89%, and indicates that the ability to recognize images based on the division of Bahasa Isyarat Indonesia (BISINDO) alphabetic classes is good..
Analisis Sentimen Penggunaan TikTok Sebagai Media Pembelajaran Menggunakan Algoritma Naïve Bayes Classifier
Authors: Apriani Elsa , Oktavianalisti Falah , Monasari La Denna Hasri , Winarni Indah , Hanif Isa Faqihuddin
Published: September 2024

Media sosial seperti TikTok telah menjadi platform yang populer di kalangan pengguna internet, terutama di kalangan generasi muda. Di sisi lain, media sosial juga memiliki potensi sebagai media pembelajaran yang efektif. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terkait penggunaan TikTok sebagai media pembelajaran yang menggunakan algoritma Naïve  Bayes untuk menganalisis data sentimen tersebut. Sebanyak 176 data dikumpulkan melalui kuesioner yang disebarkan menggunakan Google Form. Data yang diperoleh kemudian dianalisis menggunakan algoritma Naïve  Bayes Classifiers untuk mengkategorikan sentimen positif, negatif, dan netral. Hasil analisis menunjukkan bahwa akurasi model adalah 52.27%, dengan nilai precision sebesar 19.60%, dan recall sebesar 50.83%. Meskipun akurasi dan precision yang diperoleh relatif rendah, hasil ini memberikan gambaran awal mengenai persepsi pengguna terhadap TikTok sebagai alat pembelajaran. Penelitian ini diharapkan dapat memberikan wawasan lebih lanjut bagi pengembang aplikasi pembelajaran berbasis media sosial dan membantu dalam meningkatkan kualitas konten edukatif di TikTok.
Analisis Sentimen Dugaan Pelanggaran Pemilu 2024 Berdasarkan Tweet Menggunakan Algoritma Naïve Bayes Classifier
Authors: Nugroho Dafa Setyo , Hanif Isa Faqihuddin , Hasbi Muhammad Adryan , Fredianto Fredianto , Saputra Adrian Maulana , Zildjian Rachmad
Published: September 2024

Twitter merupakan media sosial yang menjadi sumber informasi dan opini utama masyarakat tentang berbagai topik, termasuk  tragedi dan kejadian terkini. Dengan jumlah penggunanya yang besar, Twitter memberikan para peneliti wawasan berharga dalam menganalisis sentimen publik. Namun pelaksanaan pemilu pada tahun 2024 kali ini di warnai berbagai isu serta banyak masyarakat yang berpendapat bahwa telah terjadi adanya pelanggaran dalam proses penyelenggaraan pemilu tahun 2024. Dengan adanya berbagai pendapat masyarakat akan hal ini, maka kami mencoba membuat sebuah artikel yang di dalamnya memuat analisa sentimen masyarakat. Analisis sentimen menggunakan data Twitter telah menjadi pendekatan yang populer, terutama menggunakan algoritma klasifikasi Naïve Bayes, mengekstraksi sentimen dari teks yang terbukti efektif dalam melakukan analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimental masyarakat atas adanya dugaan pelanggaran terhadap penyelenggaraan pemilu 2024 dengan menggunakan data Twitter. Tujuannya adalah untuk mendapatkan wawasan lebih dalam mengenai opini masyarakat atas dugaan pelanggaran yang terjadi dalam proses penyelenggaraan pemilu di Indonesia.
Implementasi Metode AHP dan SMART untuk Penentuan Keputusan Calon Penerima Zakat Produktif
Authors: Santoso Budi , Rafiq Ainur , Kacung Slamet
Published: September 2024

Lembaga Amil Zakat Infaq dan Shodaqoh Nahdlatul Ulama’ (LAZISNU) Wonocolo memiliki program zakat produktif. Diadakannya zakat produktif untuk meningkatkan mustahik menjadi muzakki. Dalam program zakat produktif ini para mustahik biasanya diberikan modal usaha untuk dikembangkan. Calon penerima zakat (mustahik) biasanya dilakukan survey terlebih dahulu untuk menghindari salah sasaran penerima. Banyaknya calon penerima zakat menyulitkan penyeleksian manual oleh LAZISNU Wonocolo untuk menentukan penerima yang layak. kebutuhan akan pengelolaan zakat yang sistematis, LAZISNU Wonocolo kemudian membuat  Sistem Pendukung Keputusan (SPK) untuk menentukan kategori calon penerima zakat. Metode yang digunakan dalam penelitian ini adalah metode Analytic Hierarchy Process (AHP) dan Simple Multi-Attribute Rating Technique (SMART). MetodeAHP untuk penentuan bobot masing-masing kriteria dan SMART sebagai cara pengambilan keputusan. Tujuan penelitian ini memberikan kemudahan pada pihak LAZISNU Kecamatan Wonocolo Sidoarjo dalam menentukan calon penerima zakat produktif, sehingga zakat dapat tersalurkan secara tepat. Penelitian ini menunjukkan hasil tes calon penerima Zakat Produktif dengan menggunakan kombinasi AHP dan SMART diperoleh salah satu calon penerima dengan nilai akhir 1,5759.
Peramalan Ekspor Batu Bara Indonesia Menggunakan Metode Double Exponential Smoothing Brown
Authors: Masa Amin Padmo Azam , Prafanto Anton , Setyadi Hario Jati
Published: September 2024

Batu bara adalah sumber energi penting untuk pembangkit listrik di banyak negara. Sebagai sumber daya alam yang tidak dapat diperbaharui, batu bara tersedia di berbagai negara termasuk Indonesia. Indonesia memiliki sumber daya batu bara sekitar 161 miliar ton dengan cadangan mencapai 28 miliar ton. Indonesia merupakan negara pengekspor batu bara terbesar di dunia dan produsen kedua terbesar. Sekitar 75% dari produksi batu bara di Indonesia diekspor ke luar negeri, sementara 25% digunakan untuk keperluan domestik. Berdasarkan potensi sumber daya batu bara yang besar dan dominasi ekspor yang signifikan dibandingkan dengan konsumsi dalam negeri, peramalan ekspor batu bara di Indonesia menjadi sangat penting. Peramalan ini memberikan panduan untuk mengoptimalkan produksi batu bara dengan tujuan memaksimalkan keuntungan negara tanpa mengorbankan kelestarian lingkungan. Dengan demikian, beberapa kebijakan dapat dipilih berdasarkan pendekatan strategis yang diambil untuk menjaga keseimbangan antara manfaat ekonomi dan keberlanjutan lingkungan. Terdapat beberapa pendekatan untuk memprediksi ekspor batu bara di Indonesia, termasuk menggunakan metode Double Exponential Smoothing Brown. Hasil peramalan tren produksi batu bara Indonesia untuk periode 2023-2027 adalah sebanyak 354.847,71 ribu ton, 353.656,62 ribu ton, 352.465,52 ribu ton, 351.274,43 ribu ton, dan 350.083,33 ribu ton. Penelitian ini menunjukkan bahwa nilai Minimum Absolute Percentage Error (MAPE) terendah yang dicapai adalah 0,06212.
Sistem Pendukung Keputusan Penetuan Penerima BLT-DD Menggunakan Algoritma C4.5
Authors: Santosa Gabriel Adven Dwi , Santosa Budi , Sumirat Lambang  Probo , Sumirat Lambang  Probo
Published: September 2024

Kemiskinan menjadi masalah yang sangat serius di Indonesia Pemerintah mengeluarkan salah program Bansos atau yang di sebut dengan Bantuan Langsung Tunai Dana Desa (BLT-DD) untuk mengentas kemiskinan. Salah satu desa yang menerima manfaat program BLT-DD yaitu desa Ngandong, Klaten Jawa Tengah sejak tahun 2023. Tantangan dalam menentukan penerima BLT-DD yaitu banyak ketidaktepatan sasaran karena masih dilakukan secara manual menyebabkan adanya masalah kecurangan yaitu penilaian secara subjektif oleh oknum tertentu. Oleh sebab itu dibutuhkan sebuah sistem pendukung keputusan dalam menentukan penerima BLT-DD. Salah satu metode SPK yang dikenal adalah Algoritma C4.5 dengan menggunakan lima parameter yaitu kemiskinan ekstrim, tunggal lansia, memiliki anggota keluarga yang menderita difabel, memiliki anggota keluarga yang sakit menahun, dan belum menerima bantuan apapun dari desa. Hasil penelitian diperoleh 8 rules dengan 5 rules berstatus layak dam 3 rules berstatus tidak layak. Hasil dari perhitungan gain menggunakan Algoritma C4.5 yang mendapat kriteria dengan nilai gain tertinggi yaitu Tunggal Lansia dengan gain 0,20998. Dengan adanya penerapan algoritma C4.5 mampu memberikan solusi kepada Pemerintah Desa dalam menentukan masyarakat yang berhak dalam menerima BLT-DD, penerima bantuan dapat menerima bantuan dengan cepat daan kemudian ditampilkan dalam laporan yang dapat diunduh
CAPITAL STRUCTURE AND FINANCIAL PERFORMANCE OF LISTED INFORMATION AND COMMUNICATIONS TECHNOLOGY FIRMS IN NIGERIA
Authors: Kanoma Nasiru Adamu , Miko Nurudeen Usman , Ayuba Augustine , Mohammed Idris , Mohammed Idris , Tagwai Mark G
Published: September 2024

This study seeks to examine the effect of capital structure on financial performance of the listed information and communications technology firms in Nigeria. This study adopted correlation and ex-post facto research design. The population of this study consists of all listed information and communications technology firms in Nigeria. Census sampling technique was employed. Multiple regression model based on pooled ordinary lease square, robust test was adopted in analyzing the panel data obtained from audited financial statement of the listed sampled information and communications technology firms for the periods of 10 years between (2013- 2022). The study reveals that both long term debt financing ratio short term debt financing, and debt to equity financing ratio have positive and significant influence on return on assets of the listed information and communications technology firms in Nigeria. On the other hand, equity financing ratio has a positive but insignificant effect on performance of listed information and communications technology companies in Nigeria. Therefore, it is recommended that the management of the listed information and communications technology firms in Nigeria should initiate coherent and integrated financial policies towards encouraging long-and short-term debt financing and debt to equity financing to ultimately improve the financial performance of the list information and communications technology firms in Nigeria.
Engagement for Equitable Outcomes
Authors: Newcomer Kathryn , Wilson Quentin , Brown Allyson Criner
Published: September 2024

Engagement for Equitable Outcomes provides practical suggestions for practitioners addressing urgent social problems and reducing inequities in their communities. Newcomer, Wilson, and Criner Brown offer approaches and models customized to local conditions and equity-focused guidance for innovating and adapting encouraging interventions. Their approach stresses intentional end-user engagement and collaboration, including a five-step Data and Engagement for Equitable, Measurable Outcomes Model: 1) inclusively collaborating to prioritize equitable outcomes; 2) identifying and developing promising interventions; 3) engaging and adapting to implement customized interventions; 4) scaling interventions for maximum impact; and 5) sustaining and improving equity-focused programming. The authors provide road maps, check lists, insights, and practical tips for navigating these five essential practices. Ultimately, this book is designed to enhance the knowledge, skills, and perspectives of policy makers, researchers, practitioners, and all who are interested in addressing urgent social problems with sustainable, equitable results.
Comparison of Transforaminal Magnesium Sulfate with Steroid Injection in the Management of ‎Radicular Back Pain: A Randomized Double-Blinded Clinical Trial Study
Authors: Imani Farnad , Sadegi Kambiz , Rahimzadeh Poupak , Kaveh Mania , Narimani Mahnaz , Khademi Seyed-Hossein
Published: September 2024

Background: This study compares the effects of transforaminal magnesium sulfate injection versus other methods for managing radicular back pain, highlighting its potential for improved pain relief and functional outcomes. Methods: This randomized, double-blind clinical trial involved 30 patients with radicular back pain who were randomly assigned to receive either transforaminal magnesium sulfate or triamcinolone injection. Primary outcomes were pain intensity and functional disability, assessed using the Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI), respectively. These were evaluated at five time points: Before the injection, 2 weeks, 1 month, 3 months, and 6 months after the injection. Secondary outcomes included drug-related adverse events within the six-month follow-up period. Results: Baseline characteristics were not significantly different between the two study groups. Compared to pre-injection measures, post-injection pain intensity and functional disability were significantly reduced in both groups at all time points (P < 0.001). At all postoperative evaluations, pain intensity and functional disability were lower in the magnesium sulfate group compared to the steroid group (P < 0.001). No drug-related side effects were recorded in either group. Conclusions: For patients with radicular back pain, transforaminal magnesium sulfate injection appears to be an effective and safe alternative to transforaminal steroid injection.
Effectiveness of Pulse Intravenous Infusion of Methylprednisolone on Pain in Patients with Lumbar Disc Herniation: A Randomized Controlled Trial
Authors: Mohammadi Hassan Reza , Asadoola Yousef , Erfani Ali , Ghoreishi Amin Nazila , Karimiyarandi Hosein , Sadeghi Sohrab , Abiri Mohammad
Published: September 2024

Background: Lumbar disc herniation (LDH) can cause pain in the lower back and leg, as well as numbness or weakness in the affected area. Various steroids, including methylprednisolone, are currently used for treatment. Objectives: This study aimed to compare the effectiveness of pulse intravenous infusion of 500 mg methylprednisolone with common non-steroidal anti-inflammatory drugs (NSAIDs) in relieving pain and improving the clinical condition of patients with lumbar disc herniation. Methods: This clinical trial, registered under code IRCT20211116053077N1, included an experimental group (37 patients) and a control group (35 patients). Pain assessments were conducted before treatment, and at one, two, and three weeks, as well as one and six months after treatment. The control group received common painkillers (diclofenac sodium tablets 100 mg), while the experimental group received a single dose of 500 mg methylprednisolone sodium succinate (intravenous injection in 500 cc normal saline). Pain scores were analyzed using SPSS 16 and statistical tests such as ANOVA, independent t-tests, and repeated measures ANOVA. Results: Prior to intervention, the mean (SD) pain score was 8.7 (3.57) in the experimental group and 8.17 (0.66) in the control group (P > 0.76). Six months after methylprednisolone injection, the mean (SD) pain score in the experimental group was 1.56 (0.83), compared to 6.48 (0.91) in the control group (P = 0.000). Analysis of variance indicated that methylprednisolone significantly reduced pain in patients with LDH (P = 0.000, F = 660.668). Conclusions: Given the effectiveness of intravenous pulse infusion of 500 mg methylprednisolone compared to common NSAIDs in relieving pain and improving clinical outcomes for patients with lumbar disc herniation, the use of this drug is recommended for pain reduction in these patients.
Virtual Education in Military Medical University: A Descriptive Study
Authors: Nehrir Batool , Mokhtari Jamileh , Raei Mehdi , Moayed Malihe Sadat
Published: September 2024

Background: Evaluating the strengths and weaknesses of virtual education is essential today. Objectives: This study examines the satisfaction of professors and students with the learning management system and the quality of virtual education in a military university. Methods: This descriptive-correlational study was conducted between 2021 and 2022. The sample included 44 professors and 167 students from the Faculty of Nursing, Medicine, and Pharmacy at a university. Data were collected using a demographic form and three standardized questionnaires that assessed satisfaction with the quality of virtual education, system interface, evaluation of professors' performance, and professors' opinions about virtual teaching. The data were analyzed using SPSS software version 22, employing correlation tests, one-way analysis of variance, independent t-tests, and multivariate analysis. Results: The students' average age was 21.23 ± 2.78 years, and 81.8% of them were male. The overall satisfaction of students with the learning management system (LMS) had an average score of 497.21 ± 109.68. The professors' performance, as perceived by the students, had an average score of 43.18 ± 9.98. The quality of online education was rated at 58.81 ± 12.51, with 68% of students reporting a moderate level of satisfaction. Among the professors, 29.5% expressed dissatisfaction with the LMS system interface, while 81.8% were moderately satisfied with the virtual education approach. Conclusions: Given the significance of virtual education, it is imperative to devise solutions that enhance professor satisfaction, improve content quality, and optimize performance. These measures will contribute to the overall success and effectiveness of virtual education, benefiting both professors and students alike.
Investigation the Frequency of Fungal Contamination in Surgical Instruments
Authors: Danesh Hossein Ali , Arasteh Hossein , Keikha Nasser , Dehghan Haghighi Javid , Aslani Peyman
Published: September 2024

Background: Fungal contamination of surgical instruments poses a significant risk to patient safety, potentially leading to surgical site infections and other severe complications. By examining contamination levels both immediately after opening sterile sets and after two hours of environmental exposure, it becomes possible to assess contamination reduction strategies. Objectives: The aim of this study was to identify which instruments and operating rooms were most affected by fungal contamination. Understanding the extent of this contamination is critical to developing more effective sterilization protocols, improving infection control practices, and ultimately enhancing patient safety in surgical settings. Methods: This study was conducted on surgical instruments at Ali ebn Abi Talib Zahedan Hospital in 2023, across seven operating rooms (emergency, general surgery, gynecology, and urology). Samples were randomly collected under sterile conditions to prevent external contamination. The collected samples were cultured in Sabouraud Dextrose Agar (SDA), and data were analyzed using SPSS version 26. Results: The findings revealed that different surgical instruments, such as Pinzette (without teeth), Allis forceps, Babcock forceps, curved clamps, Gully pots, receivers, retractors, ring forceps, and towel clamps, were contaminated with fungal pathogens within two hours of opening the sterile sets. The highest contamination rates were observed in general surgery (39%), followed by emergency (29%), urology (21%), and gynecology (11%) rooms. The most common fungal contaminants were Aspergillus spp., yeast, and Penicillium spp. Conclusions: This study highlights the challenge of maintaining sterility in surgical environments post-sterilization, as fungal contamination reappeared on surgical instruments within two hours despite the initial sterilization. These findings emphasize the need for enhanced infection control measures, advanced environmental controls, and regular monitoring to reduce the risks associated with fungal contamination in surgical settings.
Investigation of Prevalence, Etiological Factors and Risk Factors of Urinary Tract Infection in Spinal Cord Injury Patients
Authors: Komlakh Khalil , Rajabi Ramin , Sadeghi Sohrab
Published: September 2024

Background: Urinary tract infection (UTI) is a common infection that constitutes a significant portion of all hospital infections. Objectives: Given the importance of spinal cord trauma, this study aimed to investigate the prevalence, etiological factors, and risk factors of UTI in spinal cord injury (SCI) patients. Methods: In this study, patients were examined daily. If a diagnosis of UTI was confirmed, based on laboratory results and the attending physician's opinion, they were included in the study. A researcher-made checklist was then completed by the researchers. Results: The results showed that 89 (67.4%) of the patients were male, and 101 (76.5%) were married. Additionally, 44.7% of the patients were hospitalized due to traffic accidents. The results also indicated that 30.3% of patients had hematuria, 35.5% had fever, and 12.9% had scrotal pain or turbid urine. Regarding the microorganisms identified, 55.3% of patients had Escherichia coli, 11.4% had Proteus, 9.1% had Klebsiella, and 7.6% had Candida spp. Conclusions: Given the significant prevalence of UTI in patients, necessary preventive measures should be implemented to prevent infections.
The Efficacy of Cognitive Behavioral Therapy on Mental Health, Self-esteem and Emotion Regulation of Medical Students with Imposter Syndrome
Authors: Bagheri Sheykhangafshe Farzin , Nouri Elahe , Savabi Niri Vahid , Choubtashani Mersad , Farahani Hojjatollah
Published: September 2024

Background: Medical students often experience self-doubt due to the demanding nature of their coursework and numerous assignments, which may indicate Imposter Syndrome (IS). Objectives: This study aimed to evaluate the effectiveness of cognitive behavioral therapy (CBT) in improving mental health, self-esteem, and emotional regulation among medical students dealing with IS. Methods: A semi-experimental design was used, incorporating a pre-test-post-test approach with a control group. The statistical sample included medical students from Tarbiat Modares University in 2023. A total of 36 students were selected through purposeful sampling and randomly assigned to either the experimental group (n = 18) or the control group (n = 18). Data collection tools included questionnaires assessing IS, mental health, self-esteem, and emotion regulation. The experimental group received eight 90-minute CBT sessions based on the approach by Egan and Shafran (2017), while the control group received no intervention. Data were analyzed using a multivariate analysis of covariance with SPSS24 software. Results: The results showed that CBT significantly reduced expressive suppression (F = 78.04, η² = 0.72) and increased mental health (F = 37.73, η² = 0.56), self-esteem (F = 54.76, η² = 0.64), and cognitive reappraisal (F = 35.65, η² = 0.55) among students with IS (P < 0.001). Conclusions: The findings suggest that CBT is an effective intervention for improving the psychological well-being of medical students experiencing IS. The significant improvements in mental health, self-esteem, and emotional regulation underscore CBT's potential as a valuable tool for this population.
Effects of Spirulina platensis Supplementation on COVID-19 Severity in Critically Ill Patients: A Randomized Clinical Trial
Authors: Shadnoush Mahdi , Santos Heitor O. , Hatami Monireh , Norouzi Mehdi , Taghavi Mohsen , Santa Capita Cerqueira Henrique , Mojani- Qomi Mansoore Sadat , Bakhshandeh Hooman , Rahmani Jamal , Javid Zainab , Sanaei Delir Zavaragh Davood , Mikaniki Fatemeh , Chaharmahali Arezoo , Ghanavati Matin , Nazari Mojgan
Published: September 2024

Background: Spirulina is a functional food with antioxidant and anti-inflammatory properties. Its anti-inflammatory, antioxidant, antiviral, and immunomodulatory potential may improve certain clinical conditions in patients with coronavirus disease 2019 (COVID-19) in the intensive care unit (ICU). Objectives: This study aimed to investigate the effects of Spirulina supplementation on clinical conditions in critically ill patients with COVID-19. Methods: A double-blind clinical trial randomized patients admitted to an ICU to receive either Spirulina platensis supplementation (5g/day, n = 97) or to a control group (n = 95). The severity of COVID-19 was assessed using the Acute Physiology and Chronic Health Evaluation (APACHE), National Early Warning Score (NEWS) 2, and Sequential Organ Failure Assessment Score (SOFA). The study also evaluated the length of stay in the hospital and ICU, respiratory support at discharge, and 28-day mortality. In the survival analysis, 126 participants were evaluated (58 in the control group and 68 in the Spirulina group). Results: There was no significant difference between the groups in 28-day mortality (HR = 1.07, 95% CI 0.57 - 1.97) or NEWS2 (P = 0.76). However, the SOFA score significantly decreased in the Spirulina group compared to the control group (P = 0.03). The Spirulina group had a shorter ICU length of stay (7.43 ± 3.98 days) compared to the control group (10.00 ± 4.69 days, P = 0.007). The hospital length of stay was also shorter in the Spirulina group compared to the control group (P = 0.001). Conclusions: Spirulina supplementation reduced disease severity (as measured by the SOFA score) and shortened hospital and ICU length of stay in critically ill COVID-19 patients, but it did not reduce mortality.
The Prevalence of Metabolic Syndrome After Cesarean and Natural Delivery in Southwest Iran, 2021 - 2022
Authors: Hadavi Marzieh , Hassanpour Pegah , Mozafari Aliashraf , Khorshidi Ali , Sayyadi Hojjat , Kheiry Maryam
Published: September 2024

Background: The prevalence of abdominal obesity appears to vary depending on the mode of delivery. Abdominal obesity is the most common manifestation of metabolic syndrome and plays a significant role in diagnosing metabolic disorders. Objectives: This study aimed to determine whether the mode of childbirth is associated with metabolic syndrome (MetS). Methods: A total of 350 women who had either an elective cesarean section (CS) or natural vaginal delivery (NVD) at least three years ago were included in this cross-sectional study. The sample was recruited from the integrated health system (Sib) affiliated with Ilam University of Medical Sciences in southwest Iran, 2020 - 2021, using cluster randomization. Metabolic syndrome was assessed according to the 2005 revised NCEP ATP III criteria. Descriptive data were presented as means (standard deviations, SD) or frequencies (%). A t-test was used for comparison between groups, and logistic regression analysis was employed to estimate odds ratios (ORs). Results: Metabolic syndrome was found in 18.29% of women in the NVD group and 30.29% in the elective CS group. Multivariate logistic analysis revealed that breastfeeding duration (P < 0.001) and NVD (P = 0.04) were significantly associated with a decreased likelihood of MetS among mothers. Each additional month of breastfeeding was shown to reduce the likelihood of MetS by 30%. The results also demonstrated that NVD had a protective effect against MetS (Adjusted Odds Ratio (OR): 0.57, 95% CI: 0.34 - 0.97, P = 0.04) after adjusting for age and breastfeeding duration. Conclusions: Women with a history of CS were found to have a higher prevalence of MetS than those in the NVD group. This suggests that the mode of delivery may play a role in the development of MetS after childbirth. Therefore, obstetricians and midwives should consider the potential risk of disorders such as MetS when deciding on elective cesarean sections.
Impact of Learning Mediums: A Comparative Analysis of Telephone and Virtual Education on Women's Childbearing Attitudes
Authors: Bkhtiari Afsaneh , Hosseini Fatemeh Zahra , Behmanesh Fereshteh , Chehrazi Mohammad
Published: September 2024

Background: Improving women's attitudes towards childbearing plays a key role in promoting childbearing policies. Objectives: The present study aimed to compare the effectiveness of two educational methods-telephone-based and virtual training-on women's attitudes towards childbearing. Methods: This interventional study was conducted on 174 married women aged 18-35, without children, living in Babol city. The participants were randomly divided into three groups of 58: Two intervention groups and one control group. The first group received telephone-based training, while the second group received virtual training via WhatsApp video calls, both on the topic of childbearing over a four-week period. Data collection tools included a demographic questionnaire and a fertility and childbearing attitude scale, completed before, immediately after, and three months post-intervention. Data was analyzed using SPSS version 22 with chi-square tests, analysis of variance, and a linear mixed model. Results: The mean attitude scores towards childbearing increased significantly in both the telephone training group (P = 0.008) and the virtual training group (P = 0.038) compared to the control group three months later. Although telephone-based training had a greater effect on attitude scores during the follow-up phase compared to virtual training, this difference was not statistically significant. Conclusions: Both educational methods had a lasting impact on women's attitudes towards childbearing. This underscores the value of using accessible and cost-effective platforms to support childbearing policies.
Are There any Differences in the Outcome of Non-valvular AF Patients with Moderate CKD, Taking Rivaroxaban or Warfarin?
Authors: Boudagh Shabnam , Bayat Fatemeh , Khalili Mahdi , Heidarali Mona , Mahmoudian Zahra , Mozaffari Kambiz , Dalouchi Saeed , Pasha Hamid Reza , Ebrahimi Meimand Saeed
Published: September 2024

Background: Warfarin has long been a reliable anticoagulant, prized for its affordability and extensive track record. However, with advancements in anticoagulant therapies, many uses of warfarin have been replaced by Novel Oral Anticoagulant (NOAC) agents. Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting approximately 3% of the population. This arrhythmia significantly increases the risk of both morbidity and mortality, with the risk of ischemic stroke being five times higher compared to those without AF. The outcomes for patients with different comorbidities may vary when treated with either Rivaroxaban or Warfarin. Objectives: This study aims to evaluate renal function outcomes in AF patients with moderate chronic kidney disease (CKD) who are treated with either Warfarin or Rivaroxaban. Methods: This prospective cohort study involved 99 patients recently diagnosed with non-valvular AF (< 1 year) and moderate CKD, who were treated with either Warfarin or Rivaroxaban. These patients were followed for one year after their initial presentation to our center. Laboratory data and other relevant documents were collected at each visit until the conclusion of the study year. Data were analyzed using SPSS version 27, with chi-square tests and t-tests applied as appropriate. Results: The study included two groups: 25 patients on Warfarin and 74 patients on Rivaroxaban. Gender and other demographic factors were evenly distributed between the two groups. The mean glomerular filtration rate (GFR) in the Rivaroxaban group improved non-significantly at 6 and 12 months, whereas a slight, non-significant decrease in GFR was observed in the Warfarin group. Acute kidney injury (AKI) occurred in 2 patients in the Rivaroxaban group. Gastrointestinal bleeding was reported in 5 patients, with incidents evenly distributed between the two groups. Conclusions: There was no significant difference in renal outcomes between patients with newly diagnosed non-valvular AF (< 1 year) with moderate CKD treated with Warfarin or Rivaroxaban. However, for women with hypertension or high-risk thrombosis, Rivaroxaban may be associated with better renal outcomes.