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EKSPLORASI METAGENOMIK DARI SERANGGA BLACK SOLDIER FLY (Hermetia illucens) PENDEGRADASI SAMPAH ORGANIK DALAM UPAYA BIOREMEDIASI LINGKUNGAN irwanto; Maman Rumanta; Rony Marsyal Kunda
Jurnal Penelitian Pendidikan IPA Vol 11 No 1 (2025): January
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i1.9926

Abstract

The Black Soldier Fly (BSF) (Hermetia illucens Linnaeus, 1758) has long been recognized as an organism used in organic waste processing through bioconversion methods. H. illucens is known to digest organic materials into nutrient sources utilized for biomass formation with the assistance of decomposer microbiota. However, research on the structure and composition of its microbiota remains limited. This study aims to identify microbiota and their structural composition in both the larval and adult fly phases, based on organic waste feeding in tropical regions. Additionally, it seeks to provide recommendations for relevant stakeholders in identifying potential environmental bioremediation agents. The research method employed is a survey study with quantitative sample analysis. The amplification process in this study uses primers from the (V1-V9) regions of the 16S rRNA gene. Data analysis is conducted using the QIIME (Quantitative Insights into Microbial Ecology) method, utilizing high-throughput sequencing community data with QIIME2 software version 3.5.3. Microbiota from the families Lactobacillaceae and Morganellaceae have been identified as dominant in larvae, while Staphylococcaceae and Bacillaceae dominate in adult flies. Morganella morganii, Herbaspirillum piri, Dysgonomonas capnocytophagoides, and Clostridium intestinale are potential candidates for organic waste bioremediation from BSF larvae. Meanwhile, Sphingobacterium wenxiniae, Lachnoclostridium phytofermentans, Mammaliicoccus sciuri, and Corticicoccus populi are bioremediation candidates from BSF flies. The genera Enterococcus, Morganella, and Dysgonomonas are found in both temperate and tropical climate regions. However, Providencia, Klebsiella, Scrofimicrobium, and Actinomyces, which are found in the gut of BSF larvae in temperate regions, are absent in BSF larvae from tropical Indonesia. Conversely, Limosilactobacillus, Entomomonas, Lachnoclostridium, and Clostridium are not found in the gut of BSF larvae in temperate regions.
Design and Construction of a Real-Time Air Quality Monitoring System Using IoT-Based ESP32 to Strengthen Environmental Policies Yuli Tirtariandi El Anshori; Rony Marsyal Kunda; Fredrik Manuhutu
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.9820

Abstract

Air quality monitoring is one of the important steps in maintaining public health and the environment. With the development of Internet of Things (IoT) technology, air quality monitoring can be done in real-time and more efficiently. This study aims to environmental policy and design of an IoT-based air quality monitoring system using the ESP32 microcontroller. This system is designed to measure air quality parameters such as CO, NO2, temperature, and humidity using factory-calibrated sensors (DFRobot) connected to the ESP32 microcontroller. Data obtained from the sensors are processed by the ESP32 and sent to a cloud server via Wi-Fi, allowing real-time monitoring via the ThingSpeak platform which can be monitored via mobile devices or the web. The results of the air quality monitoring system design show that devices using electrochemical CO and NO sensors₂and the SHT30 sensor connected to the ESP32 is capable of reading and measuring CO, NOconcentrations., temperature, and humidity with good accuracy with a sample time of ± 20 seconds. In addition, this system can be connected online with the ThingSpeak platform, allowing visualization of measurement data in graphical form in real-time. Thus, the designed system not only functions optimally in detecting air quality parameters, but also supports efficient remote monitoring through Internet of Things (IoT) technology