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All Journal TEKNIK Jurnal Ilmu Perairan (Aquatic Science) Jurnal Ilmu-Ilmu Kehutanan
Maulana, Fikri Arkan
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Engineering Environmental Science Immunology & microbiology

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Identification of Invasive Understolans in Nganggring Tourism Village, Girikerto Village, Sleman Regency, Special Region of Yogyakarta Kusuma, Alvina Novelinda; Maulana, Fikri Arkan; Jabbar, Sa'ad Abdul
JURNAL ILMU-ILMU KEHUTANAN Vol 8, No 1 (2024)
Publisher : Fakultas Pertanian, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jiik.8.1.1-9

Abstract

Abstract: This study identifies and analyzes invasive understory plants posing a threat to the ecological balance in Nganggring Village, Sleman, Yogyakarta Special Region, particularly within the prominent Salak Pondoh plantation. Employing direct observation and interpreting remote sensing imagery from Google Earth, the research collected data using a systematic grid approach at intervals of 10x10 and 5x5 meters. The findings reveal the presence of various invasive species, including Legetan, Elephant Grass, Thunbergia erecta, Israeli Grass, and others. These plants exhibit high adaptability, rapid growth, and competitive capabilities, posing a significant risk to the local ecosystem. The study provides insights into the diversity of invasive understory plants in Nganggring Village, serving as a foundational step in minimizing adverse environmental impacts. Sustainable environmental protection and management efforts are crucial for mitigating invasion risks and preserving ecological balance in the region. This research contributes to a better understanding of invasive flora, supporting environmental sustainability amid the prominent Salak Pondoh plantation.
Identifikasi Biota di Sungai Mejing dan di Lokasi Perairan Pertambangan Pasir Sleman, Jawa Tengah Candraningtyas, Callista Fabiola; Maulana, Fikri Arkan; Wijayanti, Sovia; Mardianto, Muhammad Bondan
Jurnal Ilmu Perairan (Aquatic Science) Vol. 12 No. 1 (2024): Maret
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/

Abstract

Penelitian ini dilakukan untuk mengidentifikasi biota hewan di Sungai Mejing dan lokasi perairan pertambangan pasir Girikerto. Dengan kondisi geografis Indonesia sebagai negara Maritim, Sungai Mejing dan perairan pertambangan pasir memberikan gambaran keragaman biota perairan terestrial. Penelitian ini dilakukan mulai tanggal 30 Oktober 2023 s/d 2 November 2023. Metode yang digunakan dalam penelitian ini adalah Visual Encounter Survey (VES). Pengumpulan sampel biota di perairan menggunakan jaring, kamera handphone, dan alat tulis. Hasil penelitian menunjukkan perbedaan yang signifikan terhadap jumlah biota di kedua lokasi dimana di Sungai Mejing ditemukan 15 jenis hewan dan di kolam bekas tambang hanya ditemukan 1 jenis hewan. Kondisi ini dipengaruhi oleh asal, kualitas air, distribusi, dan ketersediaan makanan.
IoT and Machine Learning-Based Electric Vehicle Development Strategy to Maximize Vehicle Life and Promote Green Mobility Candraningtyas, Callista Fabiola; Maulana, Fikri Arkan; Achmad, Alles Anandhita
TEKNIK Vol 46, No 1 (2025) January 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v46i1.67383

Abstract

This research explores innovative strategies for developing electric vehicles based on the Internet of Things (IoT) and Machine Learning with the aim of maximizing service life and encouraging green mobility. In the face of the climate crisis and the increasing need for sustainable energy, electric vehicles offer a potential solution to reduce carbon emissions in the transportation sector. However, the challenges of optimizing battery life and energy efficiency require new, smarter and more connected approaches. This research integrates IoT technology with machine learning to create a more efficient electric vehicle ecosystem. This technology enables extended battery life through better usage management, increased energy efficiency through operational optimization, and predictive maintenance that reduces vehicle downtime. The research methodology includes testing prototypes of electric vehicles equipped with IoT technology, field trials to collect performance data, comprehensive analysis, and data processing to evaluate the effectiveness of the implemented strategies. The research results show that the integration of IoT and Machine Learning in electric vehicles can significantly increase battery life, energy efficiency, and make a positive contribution to green mobility. This development strategy is expected to advance electric vehicle technology in Indonesia, reduce dependence on fossil fuels, and create a cleaner and more sustainable environment.
Co-Authors Achmad, Alles Anandhita Candraningtyas, Callista Fabiola Jabbar, Sa'ad Abdul Kusuma, Alvina Novelinda Mardianto, Muhammad Bondan Wijayanti, Sovia