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"Just like electricity, Machine Learning will revolutionize our life in many ways-some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with."

"The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise."

"Paper ini mengeksplorasi penerapan teknik machine learning (ML) untuk memproyeksikan konsumsi energi biosolar di Indonesia di masa depan, yang bertujuan untuk memberikan informasi dan memandu pengambilan kebijakan di sektor energi. Transisi ke sumber energi terbarukan sangat penting bagi pembangunan berkelanjutan, terutama di negara-negara berkembang seperti Indonesia, yang telah menunjukkan peningkatan minat terhadap energi biosolar. Metode penelitian ini menggunakan Penelitian Kuantitatif dengan pendekatan Regresi Linier dan Sarima. Kami menggunakan beberapa model ML, menggunakan Phyton yang menganalisis dengan Multiple Linear Regression, Lasso Regression, dan Sarima, untuk menganalisis data historis mengenai konsumsi energi, indikator ekonomi, perubahan demografi, dan kemajuan teknologi. Temuan kami menunjukkan bahwa model ml dapat secara efektif memprediksi tren konsumsi biosolar, menyoroti pengaruh pertumbuhan ekonomi, urbanisasi, dan inovasi teknologi terhadap adopsi energi terbarukan. Model-model tersebut menunjukkan adanya peningkatan konsumsi biosolar, didorong oleh insentif kebijakan, kemajuan teknologi, dan meningkatnya kesadaran akan isuisu lingkungan. Keakuratan prediksi ml bergantung pada ketersediaan dan kualitas data. Selain itu, proyeksi tersebut mungkin tidak memperhitungkan perubahan ekonomi atau teknologi yang tidak terduga. Penelitian di masa depan harus fokus pada penggabungan sumber data yang lebih dinamis dan mengeksplorasi dampak perubahan kebijakan terhadap penerapan energi terbarukan. Kesimpulannya, pemanfaatan pembelajaran mesin untuk proyeksi kebijakan menawarkan pendekatan yang menjanjikan untuk mendukung pertumbuhan konsumsi biosolar di Indonesia. Studi ini memberikan landasan untuk penelitian di masa depan dan menyoroti potensi ml dalam menyusun kebijakan energi yang terinformasi dan efektif.

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This paper explores the application of machine learning (ML) techniques to project the future consumption of bio solar energy in indonesia, aiming to inform and guide policy decisions in the energy sector. The transition to re-newable energy sources is crucial for sustainable development, especially in emerging economies like indonesia, which has shown a growing interest in bio solar energy. This research method uses Quantitative Research with Linear Regression and Sarima approaches. We employed several ML models, using Phyton which analyse with Multiple Linear Regression, Lasso Regres- sion and Sarima, to analyze historical data on energy consumption, economic indicators, demographic changes, and technological advancements. Our findings indicate that ml models can effectively predict bio solar consumption trends, highlighting the influence of economic growth, urbanization, and technological innovation on renewable energy adoption. The models suggest an increasing trajectory in bio solar consumption, driven by policy incentives, technological advancements, and a growing awareness of environmental is- sues. The accuracy of ml predictions is contingent upon the availability and quality of data. Furthermore, the projections may not account for unforeseen economic or technological changes. Future research should focus on incor- porating more dynamic data sources and exploring the impact of policy changes on renewable energy adoption. In conclusion, leveraging machine learning for policy projection offers a promising approach to support the growth of bio solar consumption in indonesia. This study provides a foundation for future research and highlights the potential of ml in crafting informed, effective energy policies."

"Penelitian ini menganalisis akurasi peramalan permintaan produk barang konsumsi cepat (FMCG) menggunakan model Machine Learning, yaitu LSTM (Long Short-Term Memory) dan SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors), dengan data sekunder dari April 2021 hingga April 2024 yang terdiri dari 36 observasi bulanan. Variabel dependen adalah total penjualan, sementara variabel eksogen mencakup pengeluaran per kapita, adopsi produk, proporsi penjualan dari promosi, jumlah toko yang menjual produk, dan pangsa pasar produk. Hasil menunjukkan model LSTM memiliki akurasi lebih tinggi dalam memprediksi nilai penjualan dibandingkan SARIMAX, dengan nilai Mean Absolute Percentage Error (MAPE) yang lebih rendah pada sebagian besar sampel. Analisis korelasi mengungkapkan variabel jumlah toko yang menjual produk dan adopsi produk berpengaruh signifikan terhadap nilai penjualan dalam model LSTM, sedangkan SARIMAX unggul dalam menangkap pola musiman namun memiliki MAPE lebih tinggi. Penelitian ini menyarankan penggunaan model LSTM untuk data time series yang kompleks dan tidak stasioner, sementara SARIMAX lebih cocok untuk data dengan komponen musiman yang kuat. Pemilihan model harus mempertimbangkan karakteristik data dan tujuan analisis.

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This study analyzes the forecasting accuracy of fast-moving consumer goods (FMCG) demand using Machine Learning models, namely LSTM (Long Short-Term Memory) and SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors), utilizing secondary data from April 2021 to April 2024 with a total of 36 monthly observations. The dependent variable is sales value, while the exogenous variables include spend per buyer, product penetration, promo % of value, the number of stores selling, and market share. The results indicate that the LSTM model has higher accuracy in predicting sales value compared to the SARIMAX model, with a lower Mean Absolute Percentage Error (MAPE) for most samples. Correlation analysis reveals that the variables number of stores selling and product penetration significantly influence sales value in the LSTM model, whereas SARIMAX excels in capturing seasonal patterns but has a higher MAPE. This study recommends using the LSTM model for complex and non-stationary time series data, while SARIMAX is more suitable for data with strong seasonal components. Model selection should consider the characteristics of the data and the objectives of the analysis."

"Banyaknya konferensi menyulitkan peneliti memilih konferensi berkualitas. Kemungkinan peneliti tertipu dengan konferensi predator merupakan ancaman nyata yang perlu diperhatikan. Penilaian konferensi umumnya menggunakan pakar yang membutuhkan waktu dan biaya yang tinggi. Penelitian ini fokus untuk menganalisis jika h-indeks, impact factor, jumlah dokumen, dan SJR dapat menghasilkan penilaian kualitas yang sesuai dengan penilaian manual pakar dari beberapa situs penilaian konferensi serta membandingkan hasil performanya dengan penilaian jurnal. Data yang digunakan dikumpulkan dari empat sumber situs web yang mengkalkulasi kualitas konferensi luar negeri, yaitu CORE, ERA/QUALIS, AMiner, dan ScimagoJR. Data untuk penilaian jurnal didapatkan dari Guide2Research. Variabel yang digunakan untuk penilaian adalah h-indeks, jumlah dokumen, impact factor, dan SJR. Penelitian ini menggunakan metode K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, dan Decision Tree (DT). KNN menghasilkan nilai akurasi tertinggi sebesar 72,22% dan f1 score senilai 63,06% menggunakan data Qualis dengan faktor h-indeks, IF, dan SJR.

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The number of conferences makes it difficult for researchers to choose quality conferences. The possibility of researchers being fooled by predatory conferences is a real threat that deserves attention. Conference assessments generally use experts who require time and money to evaluate the conferences. This study focuses on analyzing whether h-index, impact factor, number of documents, and SJR can produce quality assessments in accordance with expert manual assessments from several conference assessment sites and compare the resulting performance with journal assessments. The data used were collected from four website sources that calculate the quality of overseas conferences, namely CORE, ERA/QUALIS, AMiner, and ScimagoJR. Data for journal assessments were obtained from Guide2Research. The variables used for the assessment are h-index, number of documents, impact factor, and SJR. This research used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT). KNN produced the highest accuracy value of 72.22% and the f1 score of 63.06% using Qualis data with the h-index, IF, and SJR factors."

"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."

"ABSTRAKSynthesis loop merupakan salah satu sistem kritis di pabrik amoniak. Oleh karena itu, ada urgensi untuk menjaga reliability dan availability pada sistem ini. Sebagian besar peristiwa shutdown di pabrik amoniak terjadi tiba-tiba setelah alarm tercapai. Jadi, perlu ada sistem deteksi dini untuk memastikan masalah anomali ditangkap oleh operator sebelum menyentuh set point alarm. Implementasi algoritma machine learning dalam membuat model deteksi potensi kegagalan telah digunakan di berbagai industri dan objek sebagai penelitian. Algoritma yang digunakan adalah classifier dasar dan ensemble untuk membandingkan algoritma mana yang menghasilkan hasil klasifikasi terbaik. Penelitian ini dapat memberikan ide dan perspektif baru ke dalam industri pabrik amoniak untuk mencegah terjadinya shutdown yang tidak terjadwal dengan memanfaatkan data menggunakan algoritma machine learning.

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ABSTRACTSynthesis loop is one of the critical systems in ammonia plant. Therefore, there is urgency for maintaining the reliability and availability of this system. Most of the shutdown events occur suddenly after the alarm is reached. So, there needs to be an early detection system to ensure anomaly problem captured by the operator beforetouching the alarm settings. The implementation of machine learning algorithms in making fault detection models has been used in various industries and objects. The algorithm used is the basic and ensemble classifier to compare which algorithms generate the best classification results. This research can provide a new idea and perspective into ammonia plant industry to prevent unscheduled shutdown by utilizingdata using machine learning algorithm."

"Budidaya udang vaname (Litopenaeus vannamei) sangat diminati sehingga permintaan udang ini meningkat setiap tahunnya. Masalah terberat para petambak adalah kegagalan panen yang berakibat kepada keberlangsungan usaha mereka. Perlu adanya usaha perbaikan untuk meningkatkan keberhasilan panen. Penelitian ini bertujuan untuk lebih menggali mengenai penggunaan machine learning dalam prediksi hasil panen dari data kualitas air. Hasil prediksi ini selanjutnya dipakai dan digunakan dalam proses bisnis sehingga dapat meningkatkan produktivitas. Analisis yang digunakan pada penelitian ini adalah analisis kuantitatif dan kualitatif serta perbaikan proses bisnis. Analisis kuantitatif dengan metode big data dan machine learning. Model yang dipakai adalah k-Nearest Neighbor (kNN), Decision Tree (DT) dan Logistic Regression (LR). Analisis kualitatif dilakukan dengan observasi dan interview untuk memperbaiki proses bisnis. Proses bisnis diperbaiki mengikuti BPM Lifecycle dengan memasukan hasil analisis kuantitatif. Dari penelitian ini didapatkan bahwa prediksi machine learning dengan model Decision Tree dari variabel rasio bakteri merugikan dan NH4+ memberikan akurasi tertinggi mencapai 96%. Setelah didapatkan model dan variabel dengan akurasi tertinggi, penelitian ini juga melakukan penerapan ke dalam proses bisnis dengan pendekatan BPM Lifecycle sehingga hasil tersebut dapat diimplementasi dan memberikan hasil yang lebih produktif.

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Interest in Vaname shrimp (Litopenaeus vannamei) farming is growing every year. The biggest problem for shrimp farming was the unsuccessful harvest that affected their business sustainability. So, there should be an improvement made to increase the chance of a successful harvest and its productivity. Past research mentioned that vaname shrimp harvest result can be predicted by machine learning approach from water quality data. It gave good accuracy and can be used to have faster decision making. The objective of this research is to deep dive into the utilization of machine learning to predict the successful harvest from water quality data. The predicted result will be utilized in the business process to improve productivity. Analysis that used at this research are quantitative and qualitative with business process improvement. Quantitative analysis used big data methode and machine learning. Models that have been applied are k-Nearest Neighbor (kNN), Decision Tree (DT) dan Logistic Regression (LR). Data that is used for analysis are pH, salinity, NOx, NH4+, and harmful bacteria index. Qualitative analysis was applied by observation and interview with the focus to improve business process. Business processes will be improved using BPM Lifecycle with the utilization of quantitative result. This research showed that prediction machine learning with Decision Tree model from harmful bacteria index and NH4+ giving the best accuracy until 96%. The next step was utilizing the quantitative result at the business process with BPM Lifecycle approach so the result can be implemented and gave more productive result."