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All Journal Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATHunesa: Jurnal Ilmiah Matematika
Sakti, Adam Indra
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Computer Science & IT Mathematics

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IMPLEMENTASI LOGIKA FUZZY MAMDANI PADA PREDIKSI INDEKS PEMBANGUNAN MANUSIA PROVINSI KEP. BANGKA BELITUNG TAHUN 2010-2023 Halim, Nikken; Sakti, Adam Indra; Lutfiyaturrohmah, Khilma; Pramita, Agnes; Prayanti, Baiq Desy Aniska
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n1.p64-72

Abstract

The Human Development Index is a comprehensive picture of the level of human development in a region, as the impact of development activities carried out in that region. There are three basic dimensions as a benchmark for measuring the Human Development Index that covers longevity and healthy living, knowledge, and a decent standard of living. Fuzzy logic is one of the decision support systems that can be implemented for uncertainty issues. The aim of this research is to implement the fuzzy logic of the Mamdani method on the Human Development Index (HDI) prediction and obtain accurate results from such implementation. The research variables used were life expectancy, average school age, school age expectation and per capita expenditure. This research will be done with the help of Matlab software. Based on the implementation of the fuzzy logic of the Mamdani method on the IPM prediction, the MAPE value is obtained with an average of 0.087169912% which means that it has a true value of 99.91283009%. This shows that the Fuzzy Logic of Mammdani's method is well used to predict IPM in the Bangka Belitung Islands Province.
IMPLEMENTASI ELM MENGGUNAKAN SIGMOID BINER UNTUK PREDIKSI HARGA CABAI RAWIT DI PROVINSI KEPULAUAN BANGKA BELITUNG Sakti, Adam Indra; Desy Yuliana Dalimunthe
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cabai rawit merupakan salah satu komoditas holtikultura yang mempunyai nilai ekonomi dan harga jual tinggi serta mendapat perhatian serius dari pemerintah dan pelaku usaha. Melonjaknya harga cabai yang tidak menentu menyebabkan para petani dan pelaku distribusi kesulitan dalam mempersiapkan cadangan produksi untuk memenuhi permintaan. dan menjadi komoditas yang berkontribusi signifikan terhadap penyebab inflasi di Provinsi Kepulauan Bangka Belitung. Penelitian ini bertujuan untuk membangun model prediksi harga cabai rawit menggunakan algoritma Extreme Learning Machine (ELM) berdasarkan data historis mingguan dari Januari 2021 hingga Mei 2025. Hasil penelitian didapatkan dengan model terbaik yaitu 4-18-1 dengan menggunakan fungsi aktivasi Sigmoid Biner dengan akurasi MAPE pelatihan sebesar 0.6756192% dan akurasi pengujian sebesar 0.159652% dimana pada tabel kategori MAPE dikatakan sangat baik. Sehingga hal ini menunjukkan bahwa algoritma Extreme Learning Machine (ELM) cocok digunakan untuk memprediksi harga cabai rawit di Provinsi Kepulauan Bangka Belitung.
Implementasi Artificial Neural Network (ANN) dalam Memprediksi Nilai Tukar Rupiah terhadap Dolar Amerika Sakti, Adam Indra; Saputra, Lianda; Suhendra, Helen; Halim, Nikken; Alviari, Irfaliani; Ilham, Muhammad Rozan Nur; Putri, Marwah Hotimah Nada; Dalimunthe, Desy Yuliana
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.26654

Abstract

The exchange rate of one country's currency against other countries takes an important role in the development and economic activities for a nation. This condition of the Indonesian currency exchange rate, namely the rupiah, is now continuously increasing, meaning that the exchange rate is weakening and experiencing depreciation. Apart from that, the rupiah exchange rate also experiences fluctuations, so forecasting is needed to find solutions to problems that will arise if the currency exchange rate increases. This research purpose is to find the best of network archictecture and to predict the selling rate of the rupiah (Rp) per 1USD for one year. The forecasting method used in this research is using an Artificial Neural Network (ANN) with Backpropagation algorithm. This method is suitable for use in time series analysis because the algorithm is able to adjust the data and has a relatively small error. The data used is the rupiah exchange rate against the USD in the form of time series data, which from March 1, 2019 to February 28, 2024. The data scenario of 90% training and 10% testing at the training stage obtained the best architecture 4-20-1 with MSE is 0.0010385. The data scenario is 80% training and 20% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00089412. The data scenario is 70% training 30% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00099221. Thus, the prediction prices used are predictions for the 80% training data scenario and 20% testing data, because the accuracy results (MSE) are better than the other two scenarios.
Co-Authors Alviari, Irfaliani Baiq Desy Aniska Prayanti Desy Yuliana Dalimunthe Desy Yuliana Dalimunthe, Desy Yuliana Halim, Nikken Ilham, Muhammad Rozan Nur Lutfiyaturrohmah, Khilma Pramita, Agnes Putri, Marwah Hotimah Nada Saputra, Lianda Suhendra, Helen