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All Journal JOIN (Jurnal Online Informatika) Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Alida, Mufni
Gunadarma University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression Alida, Mufni; Mustikasari, Metty
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.537

Abstract

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.
Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression Mufni Alida; Metty Mustikasari
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.537

Abstract

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.
PROGRAM REORGANISASI UNTUK PENINGKATAN KAPASITAS KELEMBAGAAN BUMDES KARYA MANDIRI DESA RAWA PANJANG HS, Ikhwan; Mildawani, Irina; Suzana, Dona; Hayuningsih, Sri; Rismiayati, Fitri; Alida, Mufni
Jurnal Pengabdian Kepada Masyarakat Darma Saskara Vol 5, No 1 (2025)
Publisher : Jurnal Pengabdian Kepada Masyarakat Darma Saskara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/abdimasug.2025.v5i1.14363

Abstract

Tujuan dari kegiatan pengabdian Masyarakat ini yaitu agar BUMDes Karya Mandiri Desa Rawa Panjang menjadi unit usaha yang berkembang untuk mengelola potensi ekonomi Desa Rawa Panjang. Metode pendekatan Input-Proses- Output. Dampak digunakan dalam kegiatan ini dengan tahapan pelaksanaan sebagai berikut: FGD untuk mapping problem BUMDes, Reorganisasi BUMDes, Penyusunan Roadmap potensi ekonomi Desa Rawa Panjang, business plan BUMdes, konsultasi dan assesment, pelatihan dan penyuluhan, evaluasi dan monitoring, output dan luaran serta dampak, kemanfaatan dan keberlajutan. Hasil kegiatan ini sangat positif bagi peningkatan efektivitas tatakelola BUMDes, penguatan internal proses bisnis dengan merancang dan mengembangkan website, penyusunan business plan sebagai pedoman acuan pengembangan manajemen dan usaha BUMDes dalam aktivitas bisnis, dan juga luaran lainya dipublikasikan dalam youtube, website Desa Rawa Panjang, HAKI dan Jurnal dharma Saskara.
Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression Alida, Mufni; Mustikasari, Metty
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.537

Abstract

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.
Implementasi Website Weji Coffee dengan Fitur Reservasi Menggunakan HTML, JavaScript, dan Tailwind CSS Alida, Mufni; Thyas, Lira Arum Kusumaning; Jajuli, Akbar Rizky
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15675

Abstract

Perkembangan teknologi pada era industri 4.0 mendorong transformasi di berbagai sektor, termasuk bidang perdagangan. coffee shop Weji Coffee, yang telah beroperasi sejak 2018 di Kalisari, Jakarta Timur, menghadapi tantangan dalam meningkatkan jangkauan pemasaran dan visibilitas digital. Untuk menjawab kebutuhan tersebut, pengembangan website dilakukan sebagai sarana penyedia informasi terkait menu, layanan, serta suasana kedai tanpa mengharuskan pelanggan datang langsung ke lokasi. Penelitian ini bertujuan memperluas cakupan usaha dan pemasaran Weji Coffee melalui pengembangan website berbasis HTML, JavaScript, dan Tailwind CSS. Metode yang digunakan adalah Software Development Life Cycle (SDLC), yang mencakup tahap perencanaan, analisis, perancangan, implementasi, serta pengujian. Proses implementasi dilakukan menggunakan Visual Studio Code dan basis data MySQL. Hasil penelitian menunjukkan bahwa website Weji Coffee berhasil dikembangkan dan berfungsi sesuai kebutuhan. Pengguna dapat dengan mudah mengakses informasi maupun melakukan reservasi. Pengujian lintas peramban menghasilkan kinerja optimal, dengan waktu akses tercepat sebesar 1,3 detik pada browser Edge. Selain itu, hasil blackbox testing mengonfirmasi bahwa seluruh fitur utama berjalan dengan baik.
Co-Authors Hayuningsih, Sri HS, Ikhwan Irina Mildawani Jajuli, Akbar Rizky Metty Mustikasari Rismiayati, Fitri Suzana, Dona Thyas, Lira Arum Kusumaning