Hasil Pencarian  ::  Simpan CSV :: Kembali

"With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book"

"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race."

"This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6, held in Paris, France, in January 2012. The 23 long and 30 short revised papers were carefully reviewed and selected from a total of 99 submissions. The papers focus on the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. In addition to the paper contributions the conference also included 3 invited speakers, who presented forefront research results and frontiers, and 3 tutorial talks, which were crucial in bringing together the different components of LION community."

"Machine learning focuses on predition-- using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. "Machine learning with Spark and Python" simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language. This edition shows how pyspark extends these two algorithms to extremely large data sets requiring multiple distributed processors. The same basic concepts apply. Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of penalized linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves"

"Latar Belakang Gangguan kecemasan lebih banyak terjadi pada saat seseorang memasuki fase dewasa muda. Kecemasan merupakan salah satu faktor risiko dalam perilaku bunuh diri di dunia dan penyebab kematian kedua yang terjadi di kalangan mahasiswa atau dewasa muda. Saat ini, perkembangan AI dalam bentuk aplikasi berbasis machine learning telah banyak digunakan dalam berbagai bidang. Akan tetapi, penggunaan aplikasi berbasis machine learning di dunia medis, khususnya dalam mendeteksi dini gangguan kecemasan di Indonesia masih terbatas. Metode Studi ini menggunakan desain studi cross-sectional, dengan metode pengambilan sampel purposive sampling. Data terkait gejala kecemasan akan diambil dari hasil pengisian kuesioner STAI, sedangkan perseverasi akan dihitung melalui hasil transkrip perekaman suara pada aplikasi “StethoSoul”. Karakteristik studi akan ditampilkan dalam bentuk data deskriptif. Analisis statistik menggunakan uji alternatif Mann-Whitney, dengan hasil yang dianggap signifikan adalah p<0,05. Hasil Dalam penelitian ini terdapat total sebanyak data dari 121 mahasiswa yang memadai untuk dianalisis. Berdasarkan hasil analisis statistik, ditemukan adanya perbedaan yang signifikan pada komponen SAI (p=0.007), sedangkan pada komponen TAI, tidak ditunjukkan adanya perbedaan yang signifikan (p=0.480) antara perseverasi dengan kelompok gejala kecemasan. Kesimpulan Hipotesis nol penelitian ini ditolak karena pada kedua komponen ditemukan adanya perbedaan perseverasi antara kelompok dengan gejala kecemasan sedang dan gejala kecemasan berat.

---

Introduction Anxiety disorders are becoming increasingly prevalent throughout the adolescent years. It is also a major risk factor for suicide behavior and the second leading cause of death among university students and adolescents. AI is now being used in a variety of fields as a machine learning-based application. However, its use in medicine, particularly for the early detection of anxiety disorders, is yet unknown in Indonesia. Method Purposive sampling was used in this cross-sectional study. Data regarding anxiety symptoms are obtained from STAI questionnaire, while perseveration was count from the recording transcript in the “StethoSoul” applicaiton. Study characteristics were shown as a descriptive data. Mann-Whitney test was applied in this study, with the findings considered significant if p<0,05. Results A total of 121 samples are eligible for analysis. Statistical analysis revealed a significant difference between perseveration and anxiety symptoms on the SAI component (p=0.007) but no significant difference on the TAI component (p=0.480). Conclusion The null hypothesis was rejected because there is difference between perseveration in moderate anxiety symptoms and high anxiety symptoms group."

"Penelitian ini bertujuan untuk menganalisis prediksi kebangkrutan perusahaan manufaktur menggunakan machine learning. Data keuangan dari perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia selama periode tahun 2013 hingga 2023 digunakan dalam penelitian ini. Metode analisis yang digunakan adalah Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest, dan Extreme Gradient Boosting (XGBoost). Hasil penelitian diharapkan dapat memberikan manfaat bagi perusahaan manufaktur dalam mengidentifikasi tanda-tanda awal kebangkrutan, kreditur dalam mengevaluasi kelayakan pemberian kredit, investor dalam pengambilan keputusan investasi, akademisi dalam pengembangan riset di bidang prediksi kebangkrutan, serta regulator pasar modal (OJK) dalam meningkatkan efisiensi pengawasan terhadap perusahaan manufaktur. Hasil penelitian menunjukkan bahwa SVM efektif dalam memprediksi data historis dengan performa yang konsisten, sementara LSTM memiliki keunggulan dalam menangani variasi dan pola dalam data baru.

---

This study aims to analyze bankruptcy prediction for manufacturing companies using machine learning. Financial data from manufacturing companies listed on the Indonesia Stock Exchange for the period from 2013 to 2023 are used in this study. The analytical methods employed include Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The results of this study are expected to provide benefits to various stakeholders: manufacturing companies in identifying early signs of bankruptcy, creditors in evaluating the feasibility of extending credit, investors in making investment decisions, academics in advancing research in bankruptcy prediction, and market regulators (OJK) in enhancing the efficiency of supervision over manufacturing companies. The results indicate that SVM is effective in predicting historical data with consistent performance, while LSTM excels in handling variations and patterns in new data."

"Sistem Ekstraksi Informasi merupakan rangkaian proses untuk mendapatkan informasi dari suatu teks. Salah satu proses dalam Sistem Ekstraksi Informasi adalah Template Relation. Template relation bertugas mengenali relasi antar entitas bernama seperti person, organization, position, dan location. Penelitian ini bertujuan mengembangkan sistem pengenalan relasi antara dua entitas (person-organization, organization-position, position-person) dan relasi antara tiga entitas (personorgnization- position). Pendekatan yang digunakan adalah machine learning dengan model ruang vektor. Pada pendekatan ini terdapat dua proses, yaitu proses training untuk pelatihan sistem dan proses testing untuk pengenalan sistem. Proses training menghasilkan pola-pola relasi untuk digunakan sistem pada tahap testing. Pada proses testing dicari nilai kesamaan (similarity coefficient) dari pasangan entitas pada dokumen testing dengan pola-pola relasi yang dihasilkan sebelumnya. Suatu pasangan entitas bernama dikatakan berhubungan atau berelasi jika nilai kesamaannya berada di atas nilai ambang (threshold). Uji coba menggunakan 120 dokumen yang berasal dari media masa online berbahasa Indonesia (www.kompas.com dan www.republika.co.id). 60 dokumen diantaranya digunakan sebagai dokumen training dan sisanya digunakan sebagai dokumen testing. Hasil uji coba sistem dapat mengenali relasi antar entitas pada teks dokumen dengan nilai F-measure sebesar 76,2% untuk relasi person-organization , 84,5% untuk relasi organization-position, 74,5% untuk relasi position-person, dan Fmeasure sebesar 74,9% pada relasi person-organization-position."

"ABSTRAKAnak anak merupakan generasi penerus bangsa. Perubahan pada citra tubuh misal pembengkakan pada leher yang disebabkan goiter dapat menyebabkan persepsi negatif terhadap diri sendiri. Kelainan pada kelenjar tiroid dapat mengakibatkan diantaranya penyakit kardiovaskuler, hipertensi, stunting, dan gangguan kesuburan pada wanita. Dampak lainnya adalah siswa yang terkena goiter memiliki nilai rata-rata lebih rendah rata-rata nilai pelajarannya daripada siswa normal. Kecamatan Bulakamba Kabupaten Brebes merupakan daerah dengan kategori parah untuk kejadian goiter. Tujuan dari penelitian ini adalah membuat sebuah aplikasi berbasis web yang bisa digunakan untuk melakukan skrining untuk kejadian Goiter pada anak-anak yang terpapar pestisida dengan parameter evaluasi yaitu Sensitivitas, Spesifitas, Positive Predictive Value, Negative Predictive Value. Penelitian ini menggunakan data sekunder, data didapatkan dari penelitian Rasipin tahun 2011. Jumlah data yang akan digunakan sebanyak 53 anak yang positif goiter dan 48 anak yang negatif goiter. Metode machine learning akan diimplementasikan dengan aplikasi WEKA. Hasil analisa dengan 10-fold Cross Validation didapatkan bahwa dengan sebelas variabel mampu mengenali siswa normal sebesar 92% dengan nilai Sensitivitas, Spesifitas, Positive Predictive Value, Negative Predictive Value berurutan sebesar 49%, 92%, 87% dan 62%. Prototipe sistem pintar untuk memprediksi kejadian goiter dapat dikembangkan, dan dapat digunakan untuk skrining kejadian goiter pada anak yang terpapar pestisida.

---

ABSTRACTChildren are the next generation of a nation. Changes in body image such as swelling of the neck caused by goiter can produce negative self perceptions. Abnormalities in the thyroid gland results in cardiovascular disease, hypertension, stunting and fertility disorders in women. Another impact is that students affected by goiter have lower average grades than normal students. Bulakamba Subdistrict(Brebes District) is a region with a severe category of goiter cases. The purpose of this study was to create a web based application which can be used to screen out the Goiter cases in children exposed to pesticides with evaluation parameters namely sensitivity, specificity, positive predictive value and negative predictive value. This study used secondary data which were obtained from Rasipin's research. Determination of goiter cases in the study was done using palpation method. The amount of data used was 53 positive-goiter children and 48 goiter-negative children. Machine learning techniques were then implemented using WEKA version 3.8.2 application. The analysis results with 10-fold Cross Validation showed that with 11 variabel, was able to recognize normal students by 92% with sensitivity, specificity, positive predictive value and negative predictive value of 49%, 92%, 87% and 62%, respectively. Smart sistem for predicting goiter cases can be developed and be used for screening goiter on children exxposed to pesticide."