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Penggunaan Media Peraga Berbasis Energi Terbarukan Guna Menunjang Model Pembelajaran Student-Centered Learning di SDN Ajung 01 Kalisat Fadhilah, Nur; Risanti, Doty Dewi; Wahyuono, Ruri Agung; Sawitri, Dyah; Mawarani, Lizda Johar; Muharja, Maktum
Sewagati Vol 9 No 1 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i1.2403

Abstract

Pengembangan media pembelajaran berbasis energi terbarukan dilakukan untuk mendukung implementasi Kurikulum Merdeka yang berfokus pada pembelajaran berbasis siswa (student-centered learning) dan pendekatan eksperimen. Media ini mencakup alat peraga yang memanfaatkan energi surya, angin, dan air, serta dilengkapi modul pembelajaran dan Lembar Kerja Siswa (LKS). Berdasarkan observasi di SDN Ajung 01 Kalisat Kabupaten Jember, keterbatasan media pembelajaran menjadi hambatan dalam penerapan Kurikulum Merdeka. Proses pembelajaran energi terbarukan cenderung bersifat teoretis tanpa visualisasi yang memadai. Untuk mengatasi hal ini, media pembelajaran yang dikembangkan tidak hanya mendemonstrasikan prinsip kerja energi terbarukan, tetapi juga memungkinkan siswa melakukan eksplorasi dan praktik langsung. Monitoring program selama empat bulan menunjukkan hasil yang positif. Siswa mengalami peningkatan pemahaman signifikan, sebagaimana terlihat dari hasil pretest dan posttest. Analisis berdasarkan Taksonomi Bloom menunjukkan capaian yang baik pada aspek kognitif, psikomotor, dan afektif. Selain itu, guru memberikan respon yang sangat baik terhadap media ini karena relevansinya dalam mendukung pembelajaran aktif dan inovatif.
Cocos nucifera l. (coir) fiber application as a filler and its effect on the volumetric shrinkage of flowable composite resin: an in vitro study cevanti, Twi agnita; Tameno, Yolanda Agustina; Mawarani, Lizda Johar; Akwan, Yulie Emilda; Prananingrum, Widyasri; Son, Meekyoung
Padjadjaran Journal of Dentistry Vol 37, No 1 (2025): March 2025
Publisher : Faculty of Dentistry Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/pjd.vol37no1.58864

Abstract

Introduction: The use of composite resin as a restorative material has the disadvantage of experiencing volumetric shrinkage during polymerization, which can lead to restoration failure. Fiber-reinforced composites can reduce volumetric shrinkage in restorative materials. Coir fiber has the potential to replace synthetic fillers because its hollow fiber structure resembles foam, supporting a compact yet lightweight structure. This characteristic is expected to reduce the volumetric shrinkage of composite resin. This study aims to analyze the effect of coir fiber application as a filler on flowable composite resin on shrinkage, comparing it with flowable composite resin containing synthetic filler. Methods: The cellulose fiber used as a filler was synthesized from coir, while the matrix consisted of BisGMA and TEGDMA resins in a ratio 5:1. The composite was prepared with a coir fiber-to-matrix resin composition of 70:30, and polymerization was carried out by irradiation for 40 seconds. Volumetric shrinkage was tested 90 minutes after irradiation. The dimensions of the composite sample were measured from images captured using a digital microscope and analyzed with ImageJ software. The volume shrinkage was then calculated based on these dimensions. Shrinkage was further confirmed through SEM analysis of marginal adaptation tests. Results: The application of coir fiber as a filler effectively reduced the volumetric shrinkage of the composite compared to the composites with synthetic filler. Statistical analyses using the Mann-Whitney test results indicated that the type of filler in flowable composite resin had a significant effect, with a p-value of 0.047 (p<0.05). Conclusion: Application of coconut fiber as filler in flowable composite resin significantly reduce volumetric shrinkage, making it three times smaller than that of flowable composite resin with a synthetic filler.
STUDI NUMERIK KEKUATAN BAHAN PADA STRUKTUR CHASSIS DENGAN BEBAN STATIS DAN DINAMIS Rahmatia; Lizda Johar Mawarani; Riyki Apriandi
Scientific Journal of Mechanical Engineering Kinematika Vol 10 No 2 (2025): SJME Kinematika Desember 2025
Publisher : Mechanical Engineering Department, Faculty of Engineering, Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/sjmekinematika.v10i2.757

Abstract

The growth of microcars as an urban mobility solution demands an efficient yet strong and safe structural design. This study aims to analyze the structural performance of microcar chassis using a Finite Element Method (FEM)-based numerical simulation approach with ANSYS Workbench software. Evaluation was conducted on five types of materials-Aluminum 6061-T6, Aluminum 6082, Steel AISI 1018, Steel S355JR, and Carbon Fiber Reinforced Polymer (CFRP) Prepreg through static and dynamic analysis. Simulation results show that all materials have safety factor values above 1, with CFRP prepreg recording the highest value. Aluminum is considered the most optimal material for mass production because it is lightweight, strong enough, and economical. In the dynamic analysis, the entire structure showed a safe response to random vibration loads due to road surface irregularities, although there was potential for resonance at certain frequencies. Therefore, fatigue analysis is recommended for long-term evaluation. This study makes an important contribution to the design of lightweight, safe and sustainable microcar chassis.
Rice Identification Using Convolutional Neural Network with YOLOv7 algorithm and VGG16 Assad Resi Alfurqan; Detak Yan Pratama; Andi Rahmadiansah; Dyah Sawitri; Lizda Johar Mawarani
IPTEK The Journal of Engineering Vol. 10 No. 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v10i3.a20143

Abstract

Rice is the most widely consumed food worldwide. The many types of rice cause various difficulties in the process of classifying rice varieties. The process of manually classifying rice varieties that rely on human power has drawbacks including the subjectivity of assessment between observers, limited physical capabilities, and longer observation times. In this research a rice variety classification system has been developed using the Convolutional Neural Network with the YOLOv7 and VGG16 algorithms. The rice varieties classified are basmati, IR64, and rojolele varieties. The model with the YOLOv7 algorithm is trained for object segmentation of rice grains and is used to create rice grain image datasets. The model with the VGG16 algorithm was trained by transfer learning and used for classifying rice grain varieties. The model with a learning rate hyperparameter of 0,000061, the ReLU activation function, the number of neurons 256 in the second classification layer, with the fine-tuning training method, has the best performance with an accuracy value of 100%. The best VGG16 model weight is used in application implementation. Identification of the type of rice with the application can be done on the image of a batch of homogeneous and heterogeneous rice grains with various arrangements.