Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Murad, Murad; Sukmawaty, Sukmawaty; Ansar, Ansar; Sabani, Rahmat; Hidayat, Syahroni

doi:10.35746/jtim.v3i4.169

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Ensemble learning adalah teknik pembelajaran mesin yang melibatkan penggabungan beberapa model untuk meningkatkan performa tugas prediksi secara keseluruhan (Murad et al, 2021;Rismayati et al, 2022). Pada penelitian ini metodologi penelitian dapat dijelaskan sebagai berikut:…”

Section: Metode Penelitianunclassified

Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara

Sunarko,

Hasanah,

Hidayat

et al. 2023

Edukomputika

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Pencemaran udara merupakan masalah serius yang berdampak negatif pada kesehatan manusia. Berbagai jenis polutan udara seperti partikel halus, sulfur dioksida, nitrogen oksida, dan ozon dapat menyebabkan gangguan pernapasan, penyakit jantung, kanker paru-paru, dan masalah kesehatan lainnya. Untuk memahami dampak kesehatan pencemaran udara, klasifikasi efek kesehatan akibat pencemaran udara menjadi penting. Metode klasifikasi ini membagi efek kesehatan berdasarkan jenis polutan, dosis, dan waktu paparan. Penelitian ini mengusulkan penerapan metode klasifikasi dengan ensemble learning untuk mengidentifikasi polutan berdampak dan tingkat risiko kesehatannya. Ensemble learning adalah teknik pembelajaran mesin yang menggabungkan beberapa model untuk meningkatkan akurasi prediksi. Stacking ensemble learning merupakan salah satu metode yang digunakan dalam klasifikasi efek kesehatan pencemaran udara dengan mengintegrasikan beberapa model dasar seperti Logistic Regression, Decision Tree, K-Nearest Neighbor, Support Vector Machine, dan Multi-Layer Perceptron. Hasil penelitian menunjukkan bahwa model Stacking memberikan performa tertinggi dengan akurasi sekitar 99,9% pada dataset baik yang seimbang maupun tidak seimbang. Namun, model Decision Tree dan K-Nearest Neighbor juga berhasil memberikan performa yang sangat baik. Waktu pelatihan model menjadi pertimbangan penting, di mana K-Nearest Neighbor dan Decision Tree memiliki waktu yang jauh lebih singkat dibandingkan dengan model Stacking.

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Section: Metode Penelitianunclassified

Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara

Sunarko,

Hasanah,

Hidayat

et al. 2023

Edukomputika

View full text Add to dashboard Cite

show abstract

“…Therefore, opportunities for improvement can still be made by applying better machine learning algorithms such as ensemble learning. The use of ensemble learning has been proven to increase the accuracy of machine learning and is implemented in many areas such as health [12], agriculture [13] and environmental [14], electrical [15], education [16], and many others.…”

mentioning

confidence: 99%

Implementation of Digital Marketing Strategy with Chatbot Technology

Ramadhan,

Sitanggang,

Wibawa

et al. 2023

Int. J. Artif. Intell. Res

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Chatbots are a rapidly growing technology in the field of digital marketing. They are computer programs designed to simulate conversation with human users. Chatbots can be integrated into websites, mobile apps, and messaging platforms to provide instant customer service and support, as well as personalized recommendations and promotions. By using natural language processing (NLP) and machine learning (ML) techniques, chatbots can understand and respond to user input in a human-like manner. They can also be programmed to respond to specific keywords, trigger events, and customer behavior. Chatbot implementation in digital marketing can help companies to improve customer engagement, increase sales and reduce costs. However, the key to successful chatbot implementation is to ensure that the chatbot is designed to meet the specific needs of the target audience and that it is integrated into the overall marketing strategy. This thesis explains the beneficial role of chatbots and shows how chatbots can be integrated into digital marketing strategies.

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mentioning

confidence: 99%

Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data

Sunarko,

Hasanah,

Hidayat

2023

Int. J. Artif. Intell. Res

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The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.

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Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA

Cited by 4 publications

References 12 publications

Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara

Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara

Implementation of Digital Marketing Strategy with Chatbot Technology

Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data

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