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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Abdimas UNEJ e-Proceeding Fountain of Informatics Journal Jurnal Ilmiah KOMPUTASI Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Indonesian Journal of Applied Statistics InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Indonesian Journal of Applied Informatics Mathvision : Jurnal Matematika Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) International Journal of Quantitative Research and Modeling Pattimura Proceeding : Conference of Science and Technology Emerging Statistics and Data Science Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Abduh Riski
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Jember
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Analisis Pola Harga Saham dengan Modifikasi Metode Eksponen Hurst dan Box Counting: Analisis Pola Harga Saham Kosala, Kosala Dwidja Purnomo; Irma Dwi Anggraeni; Abduh Riski
Emerging Statistics and Data Science Journal Vol. 1 No. 3 (2023): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol1.iss.3.art35

Abstract

Pola harga saham merupakan interpretasi dari grafik harga saham dengan rentang waktu tertentu. Pola dinamika dari harga saham penting untuk diketahui karena sejatinya seorang investor melakukan investasi mengharapkan imbalan (return) tinggi dengan risiko rendah. Pola dinamika harga saham dapat diketahui melalui analisis dimensi fraktal karena grafik harga saham memiliki sifat self-affine yang merupakan salah satu sifat dari objek fraktal. Pada penelitian ini dimensi fraktal akan dianalisis menggunakan modifikasi metode eksponen Hurst dan box counting. Klasifikasi dari hasil perhitungan dibedakan atas tiga jenis berdasarkan sifatnya, yaitu random, persistent, dan anti-persistent. Terdapat dua interval data yang diamati yaitu harga saham dari Januari sampai Desember ( data) dan harga saham dari bulan Januari sampai Juni ( data). Nilai eksponen Hurst yang dihasilkan dari kedua interval secara berurutan yaitu dan . Berdasarkan nilai eksponen Hurst yang dihasilkan menunjukkan bahwa data bersifat anti-persistent karena nilai . Kemudian nilai dimensi fraktal yang diperoleh dari penerapan metode box counting yaitu dan yang artinya pola harga saham Bank Rakyat Indonesia bersifat anti-persistent. Pola data bersifat anti-persistent yang berarti pada bulan-bulan tertentu saham memiliki harga yang tinggi dan pada bulan-bulan berikutnya saham memiliki harga yang rendah untuk diperjualbelikan.
Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network Shofia Nabila Azzahra; Ahmad Kamsyakawuni; Abduh Riski
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p04

Abstract

The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process.
Co-Authors Adhitiyah Redaya Kusuma Bhakti Agustina Pradjaningsih Ahmad Husnan Fanani Ahmad Kamsyakawuni Ahmad Saiful Rizal Ahmad Tanto Wiraga Alfian Futuhul Hadi Anisa Wahyu Illahi Ari Susiana Azimatul Matsniya Bella Audina Cahya Ramadhani Azhar Dian Anggraeni Durratul Sayyidah Adilah Munawaroh Febia Zein Aziza Heri Purwantoro I Made Tirta Irma Dwi Anggraeni Kiswara Agung Santoso Kosala, Kosala Dwidja Purnomo M Dika saputra M. Ziaul Arif Mohamad Fatekurohman Shofia Nabila Azzahra Shofia Nabila Azzahra Sholikhah, Nining Taufiqi Fadhilah Amieni