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All Journal Indonesian Journal on Computing (Indo-JC) Teorema: Teori dan Riset Matematika KUBIK: Jurnal Publikasi Ilmiah Matematika
Huda, Arief Fatchul
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Computer Science & IT Economics, Econometrics & Finance Education Mathematics

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Penyelesaian Masalah Transportasi dengan Degenerasi dan Siklus Berulang Menggunakan Minimum Demand Method dan Maximum Difference Extreme Difference Method Muhtarulloh, Fahrudin; Mardiah, Evi Wardah; Huda, Arief Fatchul; Zulkarnaen, Diny
KUBIK Vol 8, No 1 (2023): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v8i1.30024

Abstract

The transportation problem is a linear programming model that can be used to regulate distribution from a source (product supply) to a destination that requires the product optimally with minimum costs. However, when carring out optimality tests, sometimes the optimal value cannot be determined due to degeneration and repeated cycles. The aim of this research is to overcome the problem of degeneration and repeated cycles that occur in optimization problems. The methods used in this research are Minimum Demand Method (MDM) and Maximum Difference Extreme Difference Method (MDEDM) as well as the optimality test, namely Modified Distribution (MODI). The results of data analysis show that analysis show that the Minimum Demand Method has more degeneration problems, namely 132 data in the balanced case and 137 data in the unbalanced case. The Maximum Difference Extreme Difference Method has more repeated cycle problems, namely 8 data in the balanced case and 9 data in the unbalanced case. From the calculation results it can be concluded that the Maximum Difference Extreme Difference Method is more optimal than the Minimum Demand Method.
Estimasi Harga Opsi Saham melalui Simulasi Monte Carlo Menggunakan Model Volatilitas Stokastik Heston Muhtarulloh, Fahrudin; Sari, Andini Fadillah; Huda, Arief Fatchul
Teorema: Teori dan Riset Matematika Vol 10, No 1 (2025): Maret
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v10i1.16716

Abstract

Kontrak opsi saham (KOS) di pasar modal membutuhkan metode prediksi dan perhitungan yang akurat, terutama bagi saham yang menunjukkan fluktuasi tinggi. Salah satu model yang sering digunakan untuk memperkirakan harga opsi saham pada data yang tidak stabil adalah Model Volatilitas Stokastik Heston. Model ini merupakan perkembangan dari model Black-Scholes, dengan mengombinasikan dua jenis Gerak Brown, yaitu Gerak Brown Geometrik dan Model Cox-Ingersoll-Ross. Penelitian ini bertujuan untuk menghitung harga opsi saham Tesla, Inc. (TSLA) menggunakan berbagai tingkat volatilitas, meliputi tiga bulan, empat bulan, enam bulan, dan satu tahun sebelum transaksi terakhir. Hasil pengujian menunjukkan bahwa semakin tinggi volatilitas, semakin besar nilai opsi yang dihasilkan. Setelah dilakukan analisis lebih lanjut, volatilitas pada periode satu tahun sebelum transaksi terakhir memberikan hasil yang paling besar dan paling mendekati harga opsi aktual di pasar. Oleh karena itu, volatilitas satu tahun ini dipilih untuk menentukan harga teoritis dari berbagai strike price. Berdasarkan hasil tersebut, kontrak opsi dengan strike price 15 USD, 25 USD, dan 80 USD dianggap layak untuk dibeli, sedangkan kontrak dengan strike price 30 USD dan 40 USD perlu ditinjau ulang sebelum diputuskan untuk dibeli.
Implementation of Dependency Parser Using Artificial Neural Network Methods Izzah, Nurul; Bijaksana, Moch Arif; Huda, Arief Fatchul
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.504

Abstract

In recent years, parsing has become very popular within the scope of NLP (Natural Language Processing) with the presence of Dependency Parser. However, almost all existing Dependency Parser do classifications based on millions of sparse indicator features. This feature is not only bad in drawing conclusions, but also significantly limits the speed of parsing so that the resulting parsing is not optimal. To overcome these problems, changing the use of sparse features becomes dense features to reduce sparsity between words. The Artificial Neural Network classification method is used to produce fast and concise parsing in the Transition-Based Dependency Parser by using 2 hyperparameters. The dataset used in this study is Arabic, Chinese, English, and Indonesian. Based on the evaluation that has been done, it shows a higher result using the second hyperparameter. In testing with English test data, the accuracy value of LAS (Labeled Attachment Score) is 80.4% and UAS (Unlabelled Attachment Score) is 83%, Then with dev data obtained an accuracy value of LAS 81.1% and UAS 83.7%, and parsing speed of 98 sentences per second (sent/s).Keywords: Parsing, dependency parser, transition-based dependency parsing.
Co-Authors Diny Zulkarnaen, Diny Fahrudin Muhtarulloh IZZAH, NURUL Mardiah, Evi Wardah Moch Arif Bijaksana Sari, Andini Fadillah