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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Kajian dan Terapan Matematika
Damayanti, Adelia
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Implementasi Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam Prediksi Harga Saham X Damayanti, Adelia; Agustina, Dwi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25278

Abstract

Today, the stock market is one of the most important financial vehicles. Investors were concerned about X shares because it fluctuated significantly during Elon Musk's acquisition process. This study was aimed to predict the future price trend of X stocks. Thus, this analysis can assist investors in controlling X stocks. Data for this study were gathered from the Kaggle website. This study uses data from January 2016 to October 2022. The Adaptive Neuro Fuzzy Inference System (ANFIS) will be used to estimate the price of X stocks. The results demonstrated that the ANFIS approach accurately captured the pattern of stock price changes. Based on the accuracy test results, this method has an RMSE of 0.005. It demonstrates that the ANFIS method can accurately anticipate the price of X stock.
ANALISIS REGRESI ROBUST ESTIMASI-M PEMBOBOT HUBER DAN TUKEY BISQUARE PADA TINGKAT KEMISKINAN INDONESIA Damayanti, Adelia; Susanti, Mathilda
Jurnal Kajian dan Terapan Matematika Vol 10, No 2 (2024): Jurnal Kajian dan Terapan Matematika (Juli)
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jktm.v10i2.19812

Abstract

AbstrakRegresi robust merupakan metode yang penting untuk menganalisis data yang mengandung outlier. Untuk mengetahui faktor-faktor yang mempengaruhi tingkat kemiskinan di Indonesia, dapat diterapkan analisis regresi linear dengan melihat pencilan atau outlier. Metodenya, yaitu dengan metode estimasi regresi robust estimasi M. Penelitian ini bertujuan untuk membandingkan keefektifan antara model regresi robust estimasi-M pembobot huber dan tukey bisquare untuk mengatasi outlier serta mengetahui faktor yang mempengaruhi tingkat kemiskinan di Indonesia tahun 2021. Berdasarkan uji signifikansi yang dilakukan, variabel independen yang paling berpengaruh terhadap tingkat kemiskinan di Indonesia adalah penduduk dengan sumber penerangan listrik PLN dan tingkat pengangguran terbuka. Dari dua metode estimasi-M yang dipilih, metode estimasi-M pembobot huber menghasilkan nilai RSE 3,706 dan adj R-square 53,15% sedangkan pembobot tukey bisquare menghasilkan nilai RSE 3,294 dan adj R-square 51,59%. Berdasarkan data tersebut, dapat disimpulkan bahwa metode regresi robust estimasi-M pembobot huber lebih efektif digunakan untuk mengatasi outlier pada data tingkat kemiskinan di Indonesia.Kata kunci: kemiskinan, pencilan (outlier), regresi robust, estimasi-M.
Cluster Analysis of Environmental Pollution in Indonesia Using Complete Linkage Method with Elbow Optimization Damayanti, Adelia; Utami, Wika Dianita; Novitasari, Dian Candra Rini; Intan, Putroue Keumala; Kurniawan, Mohammad Lail
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i2.12961

Abstract

The issue of environmental contamination remains unsolved. The problem continues to have a substantial detrimental impact. This research aimed to identify provinces in Indonesia with high or low levels of environmental pollution so that the government may offer treatment to provinces with high levels of pollution and seek a significant reduction in the incidence of environmental pollution in Indonesia. Clustering is required to identify provinces with high and low pollution levels using the complete linkage method because this method can provide tight clusters and is less impacted by outliers. The analysis of the complete linkage method with Elbow optimization revealed two optimal clusters, namely high and low clusters. The high cluster consists of three provinces: Central Java, West Java, and East Java. The low cluster consists of 31 provinces. This research used a Silhouette Coefficient validity test. The value of the Silhouette Coefficient is 0.75. The value indicates that the data object is in the correct cluster and that the cluster structure is relatively strong.
Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method Musfiroh, Musfiroh; Novitasari, Dian C Rini; Hakim, Lutfi; Damayanti, Adelia; Haq, Dina Zatusiva; Aisah, Siti Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11967

Abstract

Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
Co-Authors Dwi Agustina Haq, Dina Zatusiva Kurniawan, Mohammad Lail Lutfi Hakim Mathilda Susanti Musfiroh Musfiroh, Musfiroh Novitasari, Dian C Rini Putroue Keumala Intan Sari, Dian Candra Rini Novita Siti Nur Aisah Wika Dianita Utami