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"Biofilm is an aggregate of consortium bacteria that adhere to each other on a surface. It is usually protected by the exopolysaccharide layer. Various invasive medical procedures, such as catheterization, endotracheal tube installation, and contact lens utilization, are vulnerable to biofilm infection. The National Institute of Health (NIH) estimates 65% of all microbial infections are caused by biofilm. Periplasmic α-amylase (MalS) is an enzyme that hydrolyzes α-1, 4- glicosidic bond in glycogen, starch, and others related polysaccharides in periplasmic space. Another protein called hemolysin-α (HlyA) is a secretion signal protein on C terminal of particular peptide in gram negative bacteria. We proposed a novel recombinant plasmid expressing α-amylase and hemolysin-α fusion in pSB1C3 which is cloned into E.coli to enable α-amylase excretion to extracellular for degrading biofilm polysaccharides content, as in starch agar. Microtiter assay was performed to analyze the reduction percentage of biofilm by adding recombinant E.coli into media. This system is more effective in degrading biofilm from gram positive bacteria i.e.: Bacillus substilis (30.21%) and Staphylococcus aureus (24.20%), and less effective degrading biofilm of gram negative i.e.: Vibrio cholera (5.30%), Pseudomonas aeruginosa (8.50%), Klebsiella pneumonia (6.75%) and E. coli (-0.6%). Gram positive bacteria have a thick layer of peptidoglycan, causing the enzyme to work more effectively in degrading polysaccharides."

"This paper presents a continuing study of the human cognitive aspect application in the technical systems. The laststudies design a human-centered design based on the German culture. The result shows a significant difference ofhuman performance between Germans and Indonesians. Therefore, this study examines the human cognitive modelbased on Indonesian culture to investigate whether the different cognitive model based on the culture aspect canimprove the human performance. The study was conducted on 60 people classified by age, young (16-34 years old) andold (older than 34 years old). Participants render predictions on an assembly activity for two interim states of twodifferent types of products which are the Builderific brick and the Pulley Release based on four types of the assemblystrategy model (Reference, Combination, Human Behavior 1, and Human Behavior 2). The dependent variables areprediction time, mental workload, and predictive accuracy. The results show that the models of human assemblystrategies and the products have significant influences on mental workload and predictive capability. The age variablesignificantly influences mental workload, performance, and prediction capabilities."

"ABSTRAKThe aim of this work was to improve physical stability of rice bran oil-in-water emulsion by heat and alkaline treated proteins from rice bran and soybean. Rice bran protein (RBP) was extracted from defatted rice bran by alkaline extraction and isoelectric precipitation. RBP and soy protein (SP) were modified by heat and alkaline treatment (pH 9 at 60 C for 60 min). The ability of modified rice bran protein (MRBP) and modified soy bean protein (MSP) to stabilize rice bran-oil-in-water emulsion was investigated. Results showed that the MRBP and MSP to form and stabilize oil-in-water emulsions were better than those of RBP and SP. Emulsions with small particle sizes diameter and creaming stability could be produced at pH 6.5 for 0.4-1.0 %wt MRBP and 0.6-1.0 %wt MSP. Improved physical stability of rice bran oil-in-water emulsion by heat and alkaline treated will enhance the utilization RBP and SP as food ingredient in the food industry."

"Dalam era perkembangan teknologi, penerapan teknologi informasi menjadi kunci untuk meningkatkan efisiensi dan efektivitas operasional perusahaan. Data science memainkan peran penting dalam mengubah data besar menjadi pengetahuan yang berguna untuk pengambilan keputusan. Skripsi ini mengembangkan platform AutoML (Automated Machine Learning) pada aplikasi Lumba.ai yang dirancang untuk mempermudah proses prediksi tanpa memerlukan keterampilan teknis khusus. AutoML menawarkan tur otomatisasi untuk memilih model terbaik berdasarkan dataset yang diberikan, serta menyederhanakan proses pemrosesan data. AutoML diimplementasikan menggunakan message queuer dan worker secara asinkron. Prediksi pada tur AutoML dilakukan menggunakan tiga jenis metode prediksi, yaitu klasi kasi, regresi, dan klaster, dengan berbagai dataset untuk menilai kinerja model yang dihasilkan. Hasil penelitian menunjukkan bahwa Lumba.ai dapat memberikan hasil prediksi yang akurat dan e sien, serta memberikan visualisasi yang informatif untuk analisis lebih lanjut. Saran dan masukan dari pengguna juga diintegrasikan untuk meningkatkan fungsionalitas dan kegunaan platform.

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In the era of technology development, the application of information technology is crucial for enhancing operational ef ciency and effectiveness. Data science plays a vital role in transforming big data into useful knowledge for decision-making. This thesis develops an AutoML (Automated Machine Learning) feature on Lumba.ai application, designed to facilitate prediction processes without requiring specialized technical skills. Lumba.ai offers automated features for selecting the best model based on the given dataset and simpli es data preprocessing. This feature is implemented by using asynchrnous worker and message queuer. Predictions from AutoML feature involves three types of prediction methods, that is classi cation, regression, and clustering, using various datasets to assess model performance. The results demonstrate that Lumba.ai provides accurate and ef cient predictions and offers informative visualizations for further analysis. User feedback is integrated to enhance the platform’s functionality and usability."

"Dalam era perkembangan teknologi, penerapan teknologi informasi menjadi kunci untuk meningkatkan efisiensi dan efektivitas operasional perusahaan. Data science memainkan peran penting dalam mengubah data besar menjadi pengetahuan yang berguna untuk pengambilan keputusan. Skripsi ini mengembangkan platform AutoML (Automated Machine Learning) pada aplikasi Lumba.ai yang dirancang untuk mempermudah proses prediksi tanpa memerlukan keterampilan teknis khusus. AutoML menawarkan tur otomatisasi untuk memilih model terbaik berdasarkan dataset yang diberikan, serta menyederhanakan proses pemrosesan data. AutoML diimplementasikan menggunakan message queuer dan worker secara asinkron. Prediksi pada tur AutoML dilakukan menggunakan tiga jenis metode prediksi, yaitu klasi kasi, regresi, dan klaster, dengan berbagai dataset untuk menilai kinerja model yang dihasilkan. Hasil penelitian menunjukkan bahwa Lumba.ai dapat memberikan hasil prediksi yang akurat dan e sien, serta memberikan visualisasi yang informatif untuk analisis lebih lanjut. Saran dan masukan dari pengguna juga diintegrasikan untuk meningkatkan fungsionalitas dan kegunaan platform.

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In the era of technology development, the application of information technology is crucial for enhancing operational ef ciency and effectiveness. Data science plays a vital role in transforming big data into useful knowledge for decision-making. This thesis develops an AutoML (Automated Machine Learning) feature on Lumba.ai application, designed to facilitate prediction processes without requiring specialized technical skills. Lumba.ai offers automated features for selecting the best model based on the given dataset and simpli es data preprocessing. This feature is implemented by using asynchrnous worker and message queuer. Predictions from AutoML feature involves three types of prediction methods, that is classi cation, regression, and clustering, using various datasets to assess model performance. The results demonstrate that Lumba.ai provides accurate and ef cient predictions and offers informative visualizations for further analysis. User feedback is integrated to enhance the platform’s functionality and usability."

"Dalam era perkembangan teknologi, penerapan teknologi informasi menjadi kunci untuk meningkatkan efisiensi dan efektivitas operasional perusahaan. Data science memainkan peran penting dalam mengubah data besar menjadi pengetahuan yang berguna untuk pengambilan keputusan. Skripsi ini mengembangkan platform AutoML (Automated Machine Learning) pada aplikasi Lumba.ai yang dirancang untuk mempermudah proses prediksi tanpa memerlukan keterampilan teknis khusus. AutoML menawarkan tur otomatisasi untuk memilih model terbaik berdasarkan dataset yang diberikan, serta menyederhanakan proses pemrosesan data. AutoML diimplementasikan menggunakan message queuer dan worker secara asinkron. Prediksi pada tur AutoML dilakukan menggunakan tiga jenis metode prediksi, yaitu klasi kasi, regresi, dan klaster, dengan berbagai dataset untuk menilai kinerja model yang dihasilkan. Hasil penelitian menunjukkan bahwa Lumba.ai dapat memberikan hasil prediksi yang akurat dan e sien, serta memberikan visualisasi yang informatif untuk analisis lebih lanjut. Saran dan masukan dari pengguna juga diintegrasikan untuk meningkatkan fungsionalitas dan kegunaan platform.

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In the era of technology development, the application of information technology is crucial for enhancing operational ef ciency and effectiveness. Data science plays a vital role in transforming big data into useful knowledge for decision-making. This thesis develops an AutoML (Automated Machine Learning) feature on Lumba.ai application, designed to facilitate prediction processes without requiring specialized technical skills. Lumba.ai offers automated features for selecting the best model based on the given dataset and simpli es data preprocessing. This feature is implemented by using asynchrnous worker and message queuer. Predictions from AutoML feature involves three types of prediction methods, that is classi cation, regression, and clustering, using various datasets to assess model performance. The results demonstrate that Lumba.ai provides accurate and ef cient predictions and offers informative visualizations for further analysis. User feedback is integrated to enhance the platform’s functionality and usability."

"ABSTRAKto evaluate how well the use of smartphones meets the requirements of patrons in an academic library setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphones for interacting with online library systems, the need for smartphone support, and the fit of the device to tasks, as well as performance. The study used interviews and survey data to identify what are the core strengths and limitations of a smartphone construct that stimulate patrons to perform their tasks in an online library setting. Using exploratory factor analysis, preliminary findings confirmed Technology-Content, Technology-Ergonomics, Technology-Smartphone Support, Technology-Platform, and Technology-Interaction design as the core dimensions of the smartphone construct. The results of the structural model supported the overall TTF model in reflecting significant positive impact of task and technology in TTF for smartphones in a digital-library setting; it also confirmed a significant positive impact of TTF on individuals performance"

"ABSTRAKCellular symptoms present a model system for the analysis of living structures in laboratories such as metabolic studies and drug screening. In living organisms, cells are often encountered with compaction, tension, and shear. Most cells are intangible and balanced when a solid or seminal stratification (monolayer culture) is connected. In distortion reaction, cells experience intense biochemical changes. The stressed cells on the culture combine in the Vivo environment, which creates dramatic shape changes and biochemical reactions. These microsystems can meet broad applications in the biomedical research field since deformation, compression or fluid flow have been found to induce biochemical changes in cells derived from various tissues. The aim of this project is to design and to develop a microfluidic device which will allow for the culturing of adhered cells in microfluidic chambers (micro-wells) while controlling, at the micro scale, the mechanical deformation applied on the substrate on which cells are attached. During this work, a Polydimethylsiloxane (PDMS) double layered microfluidic device was designed and fabricated which enables controlled micro-sized deformation of the cell culturing microwells. Moreover, a relation was found between measured deformation values and simulations."

"Pengembangan NLP di Indonesia terbilang lambat, terutama penelitian terkait bahasa daerah Indonesia. Alasannya adalah sumber data bahasa daerah tidak terdokumentasikan dengan baik sehingga sumber daya NLP yang ditemukan juga sedikit. Penelitian ini membahas metode ekstraksi kamus-kamus bahasa daerah di Indonesia untuk menghasilkan suatu sumber daya NLP yang dapat dibaca oleh mesin. Tahap penelitian dimulai dari pengumpulan data kamus, perancangan dan eksperimen metode ekstraksi, serta evaluasi hasil ekstraksi. Hasil penelitian berupa korpus paralel, leksikon bilingual, dan pasangan kata dasar-kata berimbuhan dalam format CSV dari beberapa kamus dwibahasa di Indonesia. Beberapa bahasa di antaranya adalah bahasa Minangkabau, Sunda, Mooi, Jambi, Bugis, Bali, dan Aceh. Perancangan metode ekstraksi berfokus pada kamus Minangkabau yang kemudian dilakukan eksperimen pada kamus-kamus bahasa daerah lainnya. Evaluasi dilakukan terhadap hasil ekstraksi kamus Minangkabau dengan melakukan anotasi data. Perhitungan akurasi dilakukan terhadap penempatan kelompok kata dari hasil anotasi. Hasil perhitungan menunjukkan 99% hasil ekstraksi sudah tepat untuk penentuan kelompok kata pada leksikon bilingual dan 88% untuk korpus paralel. Tim peneliti menemukan bahwa struktur dalam kamus bahasa daerah Indonesia sangat beragam, sehingga menuntut perlakuan yang berbeda pada setiap kamus, seperti perihal penomoran halaman. Selain itu, tim peneliti menemukan banyak kamus bahasa daerah Indonesia dengan kualitas yang kurang baik. Kualitas yang kurang baik ditunjukan dengan banyaknya kesalahan baca akibat noise yang terdapat pada tampilan berkas kamus.

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The development of NLP in Indonesia is relatively slow, especially for Indonesian local languages. Indonesian local language data sources are not well-documented so that there are only few NLP resources found. This study discusses the extraction method of Indonesian local language dictionaries to produce a machine-readable NLP resource. Starting from collecting dictionary data, designing and experimentation of the extraction method, and evaluating the extraction results. The extraction results are parallel corpus, bilingual lexicon, and words’ morphological form in CSV format from several Indonesian Local Language bilingual dictionaries that are Baso Minangkabau, Sundanese, Moi, Jambinese, Buginese, Balinese, and Acehnese. The designed method is also applied to some other local language dictionaries. Data annotation has been done to evaluate the extraction results so that we can calculate its accuracy of word classification for parallel corpus and bilingual lexicon. Extraction method design focuses on the Minangkabau dictionary which is then applied to other dictionaries. Data annotation has been done to evaluate the extraction results.The evaluation results show that 99% of the extraction results are correct for word classifying in the bilingual lexicon and 88% correct for parallel corpus. We found that the structure of dictionaries varies, so it requires different approaches for each dictionary, for example regarding page numbering. We also found many dictionaries with poor quality. The poor quality is indicated by the number of reading errors due to noise contained in the original dictionary file."