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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Spektra: Jurnal Fisika dan Aplikasinya Jurnal Material dan Energi Indonesia
IAN YULIANTI
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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BURIED WAVEGUIDE POLYMETHYLMETHACRYLATE MODELING FOR REFRACTIVE INDEX SENSOR APPLICATION USING FINITE ELEMENT METHOD Ian Yulianti; Jauhar Azka; Ngurah Made Darma Putra; Budi Astuti
Spektra: Jurnal Fisika dan Aplikasinya Vol 4 No 3 (2019): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 4 Issue 3, December 2019
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.684 KB) | DOI: 10.21009/SPEKTRA.043.04

Abstract

The purpose of this study is to obtain the optimum buried waveguide structure through modeling for refractive index sensor applications. The waveguide cladding material used as Polymethylmethacrylate (PMMA). The core cross-section size was 1 × 1 mm2. The simulation was carried out at a wavelength of 650 nm using the Finite Element Method (FEM). The parameter of the buried waveguide optimized in this model was the core refractive index and the thickness of the upper cladding to obtain a high propagation constant and good sensitivity to refractive index. Modeling was done for various core refractive index values ​​varied in the range of 1.52 to 1.59, which are the refractive index of various types of polymers. To optimize the sensitivity, the thickness of the upper cladding was varied between 0.125mm to 0.5mm. Besides, a simulation was also carried out for a waveguide without an upper cladding. The results show that the optimum waveguide is a waveguide without upper cladding using polyester as core material with a refractive index value of 1.57 and a sensitivity of 4.9 × 10-10rad /m. RIU.
BRIKET KULIT BAWANG PUTIH DAN BAWANG MERAH SEBAGAI ENERGI ALTERNATIF RAMAH LINGKUNGAN DWI SUKOWATI; ISTI IKMAH; MUSA DIMYATI; MASTURI M; IAN YULIANTI
Jurnal Material dan Energi Indonesia Vol 6, No 01 (2016)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.599 KB) | DOI: 10.24198/jmei.v6i01.9365

Abstract

Penelitian ini bertujuan untuk mempelajari kualitas bahan bakar briket dari kulit bawang putih dan kulit bawang merah. Kulit bawang putih dan bawang merah merupakan sampah organik yang belum dioptimalkan pemanfaatannya, sehingga briket bahan bakar dari kulit bawang ini dapat  menjadi salah satu bentuk energi ramah lingkungan. Penelitian ini merupakan penelitian eksperimen dengan variabel bebasnya adalah komposisi kulit bawang, untuk variabel terikatnya adalah densitas, lama nyala briket dan kalor yang dihasilkan. Kulit bawang dicampur dengan air dan kanji. Rasio perbandingan kulit bawang putih, kanji, air ada dua variasi komposisi, variasi komposisi pertama  kulit bawang putih dan air adalah 1:1, 1:2, 1:3, sedangkan kanji yang digunakan tidak diubah dengan ukuran 50% dari rasio perbandingan 1:1. Variasi komposisi perbandingan yang kedua, kulit bawang putih, kanji, air adalah 1:1:3, 1:2:3, 1:3:3. Perbandingan komposisi briket kulit bawang merah hanya satu variasi yaitu 1:1:3, 1:2:3, 1:3:3. Campuran ketiga bahan dicetak untuk mendapatkan briket bahan bakar ramah lingkungan. Kemudian, hasil cetakan diuji densitas, lama nyala briket dan kalor dari tiap-tiap rasio perbandingan.
Comparison of Random Forest, K-Nearest Neighbors, Decision Tree, and Neural Network for Predicting Rainfall Fariyani, Fariyani; Sunarno; Iqbal; Upik Nurbaiti; Ian Yulianti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.13638

Abstract

Erratic rainfall due to climate change has significant impacts on the environment, agriculture and economy. To mitigate these impacts, a reliable rainfall prediction model is needed. Erratic rainfall due to climate change affects various sectors of life, so a reliable prediction model is needed to support data-based decision making. This study aims to compare the performance of Random Forest, k-Nearest Neighbors (kNN), Decision Tree, and Neural Network algorithms in predicting rainfall using observation data from the Citeko Meteorological Station. The data used include weather parameters such as temperature, humidity, and air pressure. The analysis was carried out using Orange software to evaluate the accuracy, precision, and computation time of each model. The results showed that Random Forest had the highest accuracy, while Neural Network showed consistent performance on more complex datasets. The kNN algorithm gave good results with the optimal number of neighbors, but was less efficient on large datasets. Decision Tree was easier to interpret but was prone to overfitting. This study provides insight into the most appropriate algorithm for rainfall prediction based on the characteristics of the data available.
Co-Authors Budi Astuti DWI SUKOWATI Fariyani, Fariyani Iqbal ISTI IKMAH Jauhar Azka MASTURI M MUSA DIMYATI Ngurah Made Darma Putra SUKOWATI, DWI SUNARNO Upik Nurbaiti