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All Journal Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Limnology BACA: Jurnal Dokumentasi dan Informasi
Ika Atman Satya
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Library & Information Science

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The Irradiance Optimation for Growing Spirulina fusiformis: Biomass, Phycocyanin, and Protein Production Awalina Satya; Tjandra Chrismadha; Ika Atman Satya
Indonesian Journal of Limnology Vol. 2 No. 1 (2021): Indonesian Journal of Limnology
Publisher : Indonesian Society of Limnology

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3206.32 KB) | DOI: 10.51264/inajl.v2i1.7

Abstract

The optimization of cyanobacterium microalgae cultivation technology to provide the need for food or feedstocks has recently attracted many investigators. An optimum operation on microalgae cultivation is important to reduce the excessive workload on the aquatic environment. Therefore, this study describes how the varied irradiance (2000 lux, 4000 lux, 6000 lux, 8000 lux, and 10,000 lux) treatments on a bubble column photobioreactor system affected biomass production, phycocyanin, and protein from cyanobacterium Spirulina fusiformis. The objective of this study was to obtain the optimum irradiance for producing maximum biomass, phycocyanin, and protein simultaneously. The results demonstrated some findings those were: 1) irradiance 10,000 lux made doubling time of growth earliest (only 24 hours) while 2,000 lux doubled within five days later; 2) light response curve showed that the increase of biomass concentration was linear with the increasing of irradiance; 3) a predictive model (Response Surface Method) proof that the most optimum quantity of the biomass (0.58 ±0.035 gL-1 dry weight), chlorophyll-a (0.090 ±0.023 % dry weight), and phycocyanin (2.44 ±0.00 gL-1 dry weight) were obtained on 10,000 lux, while protein contents of 79.18±5.47 % dry weight attained on the irradiance of  6000 lux. The maximum productivity of the biomass, chlorophyll-a, phycocyanin, and protein was ~Pbiomass of 24.95 mgL-1day-1; Pchl-a of 2.25E-02 mgL-1day-1; Pphycocyanin of 1.88E-02 mgL-1day-1; and Pprotein of 17.56 mgL-1day-1. Enhancement of irradiance up to 5 folds lead to the increasing of biomass chlorophyll-a, phycocyanin, and protein productivity, attained to 1.7, 5.01, 4.13, and 2.81 folds, respectively. The irradiance had a significant influence on the production of the metabolites; therefore, the irradiance must be optimized.
Chili Leaf Health Classification using Xception Pretrained Model Yestika Dian Wulandari; Lulu Chaerani Munggaran; Foni Agus Setiawan; Ika Atman Satya
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3943

Abstract

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.
PENYEBARAN INFORMASI MENGGUNAKAN WWW (WORLD WIDE WEB) Ika Atman Satya
BACA: Jurnal Dokumentasi dan Informasi Vol. 20 No. 5 (1995)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Media Informasi secara tradisional telah kita kenai dengan menggunakan koran, televisi, radio dan buku referensi. Media informasi tersebut untuk penyebarannya memerlukan penunjang agar informasi tersebut dapat disebarkan secara lutis. Selain penggunaan media tradisional tersebut penyebaran informasi dengan menggunakan jaringan komputer Internet juga berkembang. Salah satu cara penyebaran informasi dengan menggunakan aplikasi WWW (World Wide Web) yang mempunyai kemampuan menggabungkan gambar, text dan suara secara interaktif. Pada tulisan ini akan dibahas tentang kemampuan, penggunaan dan pengembangan server WWW.
Co-Authors Awalina Satya Lulu Chaerani Munggaran Setiawan, Foni Agus Tjandra Chrismadha Yestika Dian Wulandari