Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine

Fadilah, Widya Rizka Ulul; Agfiannisa, Dewi; Azhar, Yufis

doi:10.21111/fij.v5i2.4449

Cited by 15 publications

(14 citation statements)

References 2 publications

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“…Dalam proses pencarian hyperplane yang optimal, SVM sangat bergantung pada parameter yang digunakan, seperti nilai C, nilai epsilon, dan fungsi kernel yang digunakan. Ada beberapa fungsi kernel yang dikenal dalam SVM, diantaranya adalah linear, sigmoid, radial, dan polynomial [17] [18]. Fungsi kernel yang akan digunakan dalam penelitian kali ini adalah kernel linear.…”

Section: Support Vector Machineunclassified

Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Sabrila

Sari

Minarno

2021

FIJ

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Section: Support Vector Machineunclassified

Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Sabrila

Sari

Minarno

2021

FIJ

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“…Pergerakan saham cenderung bersifat non linier, maka para investor memerlukan suatu metode untuk melakukan monitoring prediksi pergerakan harga saham yang akurat, dimana informasi ini dapat digunakan dalam pengambilan keputusan. Hal ini yang menyebabkan prediksi perubahan harga saham menjadi topik utama di kalangan investor [8].…”

Section: Pendahuluanunclassified

“…Algoritma SVM tidak memiliki masalah overfittin dan dapat menentukan model otomatis. Kyong-jae juga melakukan penelitian pada metode SVM dan diketahui bahwa metode SVM mampu mengurangi adanya kesalahan klasifikasi dan penyimpangan data training [8]. Support Vector Machine (SVM) adalah salah satu metode klasifikasi yang bertujuan untuk menemukan hyperplane terbaik yang memisahkan dua buah kelas pada input space.…”

Section: Pendahuluanunclassified

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Implementasi Algoritma Support Vector Machine (Svm) Dalam Memprediksi Harga Saham Pt. Garuda Indonesia TBK

Wulandari

Anubhakti

2021

IDEALIS Budi Luhur

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Abstrak- Saham adalah salah satu bentuk investasi yang dapat memindah tangankan kepemilikan perusahaan. Saham diperjual-belikan di lantai bursa, salah satunya yang berada di Indonesia adalah IDX (Indonesia Stcok Exchange). IDX adalah pihak penyelenggara dan penyedia sistem dan sarana yang mempertemukan penawaran jual beli Efek pihak-pihak lain dengan tujuan memperdagangkan Efek diantara mereka. Harga saham yang sangat fluktuatif sangat beresiko dapat menimbulkan kerugian kepada para trader ataupun investor, tetapi harga saham masih dapat diprediksi. Model prediksi pergerakan harga saham yang akurat dapat membantu para investor dalam pertimbangan pengambilan keputusan. Ada beberapa variabel yang mempengaruhi nilai return saham, yang sifatnya linier dan non-linier terhadap return harga saham. Prediksi harga saham ini akan sangat berguna bagi investor untuk mengetahui bagaimana alur investasi bekerja dan tentunya akan sangat membantu untuk mengurangi tingkat kerugian. Penelitian ini bertujuan untuk menerapkan metode Support Vector Machine (SVM) dalam melakukan prediksi harga saham harian PT. Garuda Indonesia, Tbk yang mana hasil akhirnya berupa nilai akurasi prediksi harga saham. Data yang digunakan dalam penelitian ini adalah data sekunder berupa data historis harga saham harian sejak tanggal 18 Maret 2019 sampai dengan tanggal 23 April 2021. Penelitian ini menunjukkan bahwa menggunakan metode SVM mampu memprediksi harga saham dengan nilai akurasi prediksi sebesar 0.545. Abstract- Stock is one of the investment firms that can transfer ownership of a company. Shares are traded on the stock exchange floor, one of which is in Indonesia is IDX (Indonesia Stock Exchange). IDX is the provider of systems and facilities that bring together other parties' buying and selling offers for the purpose of trading Securities between them. Very volatile stock prices are very risky, which can cause losses to traders or investors, but stock prices are still predictable. An accurate prediction model for stock price movements can help investors in considering decision-making. Several variables affect the value of stock returns, which are linear and non-linear on stock price returns. This stock price prediction will be very useful for investors to find out how the investment flow works and of course will be very helpful to reduce the level of losses. This study aims to apply the Support Vector Machine (SVM) method in predicting the daily stock price of PT. Garuda Indonesia, Tbk, where the result is the accuracy of the stock price prediction. The data used in this study is secondary data in the form of historical data on daily stock prices from March 18, 2019, to April 23, 2021. This study shows that using the SVM method can predict stock prices with a predictive accuracy value of 0.545.

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“…Price prediction research had also been conducted to made a stock price prediction of PT. Telekomunikasi Indonesia with the Support Vector Machine (SVM) method used a dataset from Yahoo Finance [11]. The dataset contained as many as 1265 records per day from 2015 to 2020, with a split of 20% testing data and 80% training data.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Price Grouping by Specifications using K-Means Clustering Method

Syahputra

Atika

Aslamsyah

et al. 2021

J. Teknik Informatika CIT Medicom,

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The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.

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Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine

Cited by 15 publications

References 2 publications

Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Implementasi Algoritma Support Vector Machine (Svm) Dalam Memprediksi Harga Saham Pt. Garuda Indonesia TBK

Smartphone Price Grouping by Specifications using K-Means Clustering Method

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