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All Journal Home Economics Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal
Pratama, Mulki Rezka Budi
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Computer Science & IT Economics, Econometrics & Finance

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ANALYSIS VIDEO BASED LEARNING DESIGN IN PRACTICE COURSE AT VOCATIONAL SCHOOL Puspitasari, Feny; Pratama, Mulki Rezka Budi
Home Economics Journal Vol. 8 No. 2 (2024): October
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/hej.v8i2.67927

Abstract

This study aims to design educational videos as video-based learning and determine the design that makes students more interested in paying attention to the distance learning process. If students want to pay attention to the material during the learning process, students will also get the maximum learning experience. The study was focused on the Department of Fashion at SMKN 1 Dawuan Subang to improve clarity. To obtain empirical data, this study used quantitative comparative with data collection using questionnaires with census sampling. The number of respondents is 22 students. The steps are input measurement design video with Bandicam and output measurement obtained from the evaluation results based on three elements for video design and implementation: cognitive load, student engagement, and active learning together. The data collection technique used a questionnaire. The results indicate that video-based learning using Bandicam was most effective in organizing tempo, but more attention must be paid to segmentation because the visualization video was formal and tended to be static. Further research should explore video-based learning using more engaging and dynamic applications like Prezi or similar tools.
Identifikasi Intensitas Makan Ikan Budidaya Akuaponik berdasarkan Kualitas Air dan Pergerakan Ikan PRATAMA, MULKI REZKA BUDI; ALFATAH, REZA FIKRI; SUSILA, JAYA KUNCARA ROSA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.235-247

Abstract

AbstrakPemberian pakan ikan dapat ditentukan melalui pengamatan perilaku, kualitas air, dan ukuran ikan. Salah satu metode otomatisasi yang digunakan adalah ANFIS (Adaptive Neuro Fuzzy Inference System), dengan masukan berupa kualitas air (kekeruhan dan NH3) serta aktivitas gerak ikan menggunakan inframerah (IR). Sistem ini mendukung keputusan pemberian pakan secara optimal. Validasi alat dilakukan menggunakan model regresi linier dan evaluasi kinerja berdasarkan Mean Relative Error (MRE). Hasil menunjukkan akurasi perangkat mencapai 95,77%, lebih tinggi 6,55% dibandingkan perangkat tanpa kualitas air (89,22%). Model ini terbukti andal dan dapat diterapkan pada sistem akuaponik berbasis industri untuk meningkatkan efisiensi pemberian pakan ikan. Kata kunci: Akuaponik; biologi otomatis; rekayasa sistem; robotika; instrumentasiAbstractFish feeding can be determined by observing behavior, water quality, and fish size. One automation method used is ANFIS (Adaptive Neuro Fuzzy Inference System), which uses inputs such as water quality (turbidity and NH3) and fish movement activity detected by infrared (IR). This system supports optimal feeding decision-making. The tool validation was conducted using a linear regression model, and its performance was evaluated based on the Mean Relative Error (MRE). The results showed that the device achieved an accuracy of 95.77%, 6.55% higher than devices without water quality input (89.22%). This model has proven reliable and can be applied to industry-based aquaponic systems to enhance the efficiency of fish feeding.Keywords: Aquaponics; automated biology; systems engineering; robotics; instrumentation
Sensor MOS Hidung Elektronik untuk Membedakan Thrips dan Spodoptera pada Stroberi HAQ, FAJRIN NURUL; ALFATAH, REZA FIKRI; PRATAMA, MULKI REZKA BUDI; RISWANTO, SAHRUL; RAHMAN, AYU SUCI
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.48-60

Abstract

AbstrakPenelitian ini mengevaluasi efektivitas hidung elektronik berbasis sensor MOS, yaitu sensor TGS dan MQ, dalam mendeteksi dan membedakan hama thrips dan Spodoptera litura pada tanaman stroberi. Data volatil yang dihasilkan oleh tanaman diamati menggunakan sensor E-nose yang terhubung dengan model jaringan saraf tiruan Backpropagation (BPPN). Dengan penyetelan GridSearchCV, akurasi deteksi meningkat secara signifikan, terutama pada sensor TGS, yang menunjukkan kinerja lebih baik dibandingkan sensor MQ. Teknologi ini menawarkan pendekatan deteksi hama yang sensitif, tidak merusak, dan ramah lingkungan, dengan potensi untuk mendukung pengelolaan hama secara berkelanjutan dalam budidaya stroberi. Penelitian ini memberikan peluang baru untuk inovasi di bidang pertanian pintar dengan pengurangan penggunaan pestisida yang berlebihan dan optimalisasi strategi pengelolaan hama. Kata kunci: hidung elektronik, deteksi, trips, spodoptera, stroberiAbstractThis study evaluates the effectiveness of metal oxide semiconductor (MOS) electronic noses, specifically the TGS and MQ sensors, in detecting and distinguishing between thrips and Spodoptera litura pests on strawberry plants. Volatile compounds produced by the plants were analyzed using an E-nose connected to a Backpropagation Neural Network (BPNN) model. The GridSearchCV optimization significantly improved detection accuracy, particularly for the TGS sensor, which outperformed the MQ sensor. This technology offers a sensitive, non-invasive, and environmentally friendly approach to pest detection, supporting sustainable pest management in strawberry cultivation. The study opens new opportunities for smart agricultural innovations, reducing excessive pesticide use and optimizing pest control strategies. Keywords: electronic nose, detection, thrips, spodoptera, strawberry
Co-Authors HAQ, FAJRIN NURUL puspitasari, feny RAHMAN, AYU SUCI Reza Fikri Alfatah RISWANTO, SAHRUL SUSILA, JAYA KUNCARA ROSA