Studi Literatur Mengenai Prediksi Harga Saham Menggunakan Machine Learning

Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas; Riyantoko, Prismahardi Aji; Fahrudin, Tresna Maulana

doi:10.33005/santika.v1i0.20

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Pendekatan tradisional seperti analisis teknikal dan fundamental telah digunakan untuk ini. Namun, perkembangan teknologi telah mendorong penggunaan model machine learning yang lebih canggih dan kompleks dalam prediksi harga saham [10].…”

Section: Prediksi Harga Sahamunclassified

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Sudriyanto,

Syahro,

Fitriani

2023

JARS

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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, which is higher than K-NN, it still provides reasonably accurate predictions. Meanwhile, the SVM model has an RMSE of 0.075, indicating a higher level of error in its predictions. Based on these results, the recommendation is to utilize the K-NN model as the preferred choice for predicting the ASII.JK stock price.

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Section: Prediksi Harga Sahamunclassified

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Sudriyanto,

Syahro,

Fitriani

2023

JARS

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“…Oleh karena itu, "Kamar Gelap" perlu berinvestasi dalam teknologi analisis data yang sesuai agar dapat mengoptimalkan operasional mereka. Namun, dengan perkembangan teknologi, ada solusi yang dapat membantu usaha ini dalam menghadapi tantangan tersebut, yaitu penggunaan data transaksi penjualan dan machine learning (Hindrayani et al, 2021;Kiedrowsky, 2023). Dalam era digital saat ini, machine learning dapat digunakan sebagai Langkah penting untuk kemajuan bisnis.…”

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Transformasi Digital Dalam Industri Kerajinan Rumahan Melalui Penggunaan Teknologi Machine Learning

Jeffry,

Nurdyansa,

Usman

et al. 2023

JMM

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Abstrak: Salah satu masalah mendasar yang dihadapi oleh Industri Rumah Tangga adalah pengumpulan dan pengolahan data yang masih dilakukan secara konvensional atau manual. Dalam dunia bisnis modern, analisis data adalah kunci untuk memahami tren penjualan dan preferensi pelanggan. Pengabdian kepada masyarakat ini bertujuan untuk mengatasi masalah ketidaktersediaan teknologi dalam usaha sablon "Kamar Gelap," yang menghambat analisis data penjualan dengan memberikan pemahaman kepada mitra tentang penggunaan digital marketing dan machine learning. Pendekatan pengabdian melibatkan identifikasi kebutuhan mitra, perencanaan program, pelaksanaan kegiatan, dan evaluasi hasil. Metode yang digunakan pada kegiatan ini adalah metode sosialisasi dan pelatihan tentang digital marketing dan machine learning. Usaha "Kamar Gelap Screen Printing" menjadi mitra dalam pengabdian ini, dengan partisipasi sebanyak 14 peserta. Evaluasi menunjukkan peningkatan signifikan dalam kemampuan analisis data, penguasaan teknologi informasi, dan strategi pemasaran peserta. Hasilnya meliputi pembuatan sistem informasi penjualan dan sistem prediksi penjualan berdasarkan data transaksi. Penilaian menunjukkan peningkatan pemahaman mitra: transformasi digital meningkat 29%, konsep machine learning naik 56%, relevansi teknologi machine learning meningkat 86%, dan pemahaman tentang dampak transformasi digital terhadap efisiensi produksi meningkat 7%.Abstract: One fundamental challenge faced by Household Industries is the conventional and manual collection and processing of data. In the modern business world, data analysis is key to understanding sales trends and customer preferences. This community engagement aims to address the issue of technology unavailability in the "Kamar Gelap" screen printing business, which hampers sales data analysis, by providing understanding to partners about digital marketing and machine learning usage. The approach involves identifying partner needs, program planning, activity implementation, and results evaluation. The methods employed in this initiative include socialization and training on digital marketing and machine learning. "Kamar Gelap Screen Printing" business is the partner in this engagement, with the participation of 14 attendees. The evaluation demonstrates a significant improvement in participants' data analysis skills, IT proficiency, and marketing strategies. The outcomes include the development of a sales information system and a sales prediction system based on transactional data. The assessment indicates an enhancement in partner understanding: a 29% increase in digital transformation comprehension, a 56% rise in machine learning concept awareness, an 86% increase in the relevance of machine learning technology, and a 7% improvement in understanding the impact of digital transformation on production efficiency.

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Studi Literatur Mengenai Prediksi Harga Saham Menggunakan Machine Learning

Cited by 2 publications

References 4 publications

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham

Transformasi Digital Dalam Industri Kerajinan Rumahan Melalui Penggunaan Teknologi Machine Learning

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