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Kapang amilolitik asal Biosite Hutan Pelangi, Ijen Geopark, Indonesia Sutoyo Sutoyo; Safira Isti’nafil Islam; Esti Utarti; Sattya Arimurti; Siswanto Siswanto
Metamorfosa: Journal of Biological Sciences Vol. 11 No. 01 (2024)
Publisher : Program Magister Ilmu Biologi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/metamorfosa.2024.v11.i01.p10

Vegetasi Hutan Pelangi, Ijen Geopark sudah terkonservasi sejak lama, sehingga kemungkinan di dalamnya terdapat diversitas mikrobiota yang potensial sebagai agen hayati pendegradasi berbagai substrat organik. Keberadaan dan pertumbuhan mikroba dalam lingkungan Hutan Pelangi berperan penting dalam membentuk proses antara lain siklus karbon, sehingga keberadaan mikroorganisme tersebut menarik untuk dikaji secara mikrobiologi. Penelitian ini bertujuan untuk mendapatkan isolat yang mampu mendegradasi substrat amilum secara semikuantitatif dan identifikasi secara morfologi sampai tingkat genus terhadap isolat terpilih. Penelitian diawali dengan isolasi, skrining kapang amilolitik serta identifikasinya secara fenotip terhadap isolat yang potensial aktivitas degradasinya tinggi. Hasil penelitian diperoleh 34 isolat kapang yang menunjukkan keragaman berbeda secara morfologi koloninya dan aktivitasnya dalam menghidrolisis amilum. Sejumlah 22 isolat (64,7%) menunjukkan aktivitas amilolitik dan 12 isolat (35,29%) merupakan kapang non-amilolitik. Hasil ANOVA rata-rata indeks aktivitas amilolitik isolat kapang dan uji lanjut Duncan menunjukkan bahwa isolat kapang IHP30, IHP10, dan IHP9 merupakan tiga isolat kapang amilolitik dengan indeks aktivitas amilolitik tertinggi. Indeks aktivitas masing-masing yaitu isolat kapang IHP30 sebesar 1,65 ± 0,14; IHP10 sebesar 1.47 ± 0,31; dan IHP9 sebesar 1.17 ± 0,06. Berdasarkan karakteristiknya, masing-masing teridentifikasi sebagai Penicillium sp. IHP30, Penicillium sp. IHP10, dan Aspergillus sp. IHP9

Kapang amilolitik asal Biosite Hutan Pelangi, Ijen Geopark, Indonesia Sutoyo Sutoyo; Safira Isti’nafil Islam; Esti Utarti; Sattya Arimurti; Siswanto Siswanto
Metamorfosa: Journal of Biological Sciences Vol. 11 No. 01 (2024)
Publisher : Program Magister Ilmu Biologi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/metamorfosa.2024.v11.i01.p10

Vegetasi Hutan Pelangi, Ijen Geopark sudah terkonservasi sejak lama, sehingga kemungkinan di dalamnya terdapat diversitas mikrobiota yang potensial sebagai agen hayati pendegradasi berbagai substrat organik. Keberadaan dan pertumbuhan mikroba dalam lingkungan Hutan Pelangi berperan penting dalam membentuk proses antara lain siklus karbon, sehingga keberadaan mikroorganisme tersebut menarik untuk dikaji secara mikrobiologi. Penelitian ini bertujuan untuk mendapatkan isolat yang mampu mendegradasi substrat amilum secara semikuantitatif dan identifikasi secara morfologi sampai tingkat genus terhadap isolat terpilih. Penelitian diawali dengan isolasi, skrining kapang amilolitik serta identifikasinya secara fenotip terhadap isolat yang potensial aktivitas degradasinya tinggi. Hasil penelitian diperoleh 34 isolat kapang yang menunjukkan keragaman berbeda secara morfologi koloninya dan aktivitasnya dalam menghidrolisis amilum. Sejumlah 22 isolat (64,7%) menunjukkan aktivitas amilolitik dan 12 isolat (35,29%) merupakan kapang non-amilolitik. Hasil ANOVA rata-rata indeks aktivitas amilolitik isolat kapang dan uji lanjut Duncan menunjukkan bahwa isolat kapang IHP30, IHP10, dan IHP9 merupakan tiga isolat kapang amilolitik dengan indeks aktivitas amilolitik tertinggi. Indeks aktivitas masing-masing yaitu isolat kapang IHP30 sebesar 1,65 ± 0,14; IHP10 sebesar 1.47 ± 0,31; dan IHP9 sebesar 1.17 ± 0,06. Berdasarkan karakteristiknya, masing-masing teridentifikasi sebagai Penicillium sp. IHP30, Penicillium sp. IHP10, dan Aspergillus sp. IHP9

EXPLORATION OF AMPLITUDE CODING CAPACITIES FOR Q-ML MODEL Unang Achlison; Dendy Kurniawan; Toni Wijanarko Adi Putra; Siswanto Siswanto
Journal of Engineering, Electrical and Informatics Vol. 2 No. 3 (2022): Oktober: Journal of Engineering, Electrical and Informatics:
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i3.916

Quantum computing implements computation adopting environmental phantasm and the foundation of quantum mechanics to clear up the issues. This design of calculation has been demonstrated to serve the acceleration of some modern processing issues. Current evolution in quantum technology is emerging, and the application of learning design to this current instrument is developing. With enough prospects, the application of quantum development in the area of Machine Learning has come clear. This research develops a TensorFlow Quantum (TF-Q) software framework model for machine learning functions. The two models advanced the application of material coding techniques from amplitude coding to constructing a case in the quantum learning model. This study aimed to explore the scope of amplitude coding to serve enhanced case establishment in learning techniques and in-depth investigation of data sets that bring insight into the practice data adopting the “Variational Quantum Classifier” (VQ-C). The emergence of this current method raises the investigation of how best this tool can be adopted, the aim is to provide several analysis explanations for the element of quantum machine learning that can be applied given the constraints of the actual device. The results of this study indicate there are clear advantages to adopting amplitude coding over another technique as demonstrated by adopting the combination of quantum-humanistic neural networks in TF-Q. In addition, the different preprocessing steps can generate more aspect-affluent data while using VQ-C the no-charge lunch assumption dominance for quantum learning technique for humanistic models. The material even though conceal in quantum by unadvanced data preparation steps but involves new ways of understanding and appreciating these new methods. Future studies will lack expansion into multi-type of analysis models that are sufficiently advanced to be relevant in work similar to this.

Hybrid Federated Ensemble Learning Approach for Re-al-Time Distributed DDoS Detection in IIoT Edge Compu-ting Environment Danang Danang; Siswanto Siswanto; Widya Aryani; Priyo Wibowo
Journal of Engineering, Electrical and Informatics Vol. 5 No. 1 (2025): Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i1.5099

Development rapid from the Industrial Internet of Things ( IIoT ) and edge computing have revolutionize modern industry through distributed data processing with latency low . However , progress this also enlarges risk security cyber , in particular Distributed Denial of Service (DDoS) attacks can to disable operation industry that is critical . System Detection Conventional Intrusion (IDS) own limitations in matter scalability , data privacy , and capabilities generalization to environment Heterogeneous IIoT . For answer challenge said , research This propose A framework Hybrid Federated–Ensemble Learning (FL–EL) work to improve efficiency detection real -time DDoS attacks on networks IIoT edge -based . This model utilizing the Edge -IIoTset dataset which reflects pattern Then cross real in system industry . Federated learning is used For train the model collaborative across multiple edge nodes without need move data to center , so that guard data privacy . Each node performs training local using the basic model such as Random Forest (RF), XGBoost , and Support Vector Machine (SVM). Then , the central server do aggregation use ensemble techniques such as soft voting and stacking. The preprocessing process includes SMOTE technique and Z-score normalization for handle imbalance class and improve performance .Evaluation results show that This FL–EL hybrid approach capable reach performance high (F1-score > 99.5%) and significantly significant reduce level error positive as well as burden communication , compared with approach centralized . Framework this also shows ability detection fast with latency low , making it suitable For implementation in the system IIoT that requires resilience time real . Development advanced will covers Explainable AI integration for model interpretation and blockchain for secure and transparent logging .

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