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Economic Evaluation on Production of ZnO Nanoparticles from Laboratory Scale to Industrial Scale
Tri Suhartono,Asep Bayu Dani Nandiyanto ASCONS 2019 INTERNATIONAL JOURNAL OF EMERGING MULTIDISCIPLINAR Vol.3 No.4
ZnO nanoparticles are used to obtain inorganic antibacterial and UV-blocking properties. The purpose of this study is to evaluate the increase in the production of ZnO nanoparticles from the laboratory scale to the industrial scale. Economic evaluation is carried out from an engineering and economic perspective. From the engineering evaluation, the results show that the production of prospective ZnO nanoparticles uses modern methods and technology. From an economic point of view, the results show that the production of ZnO nanoparticles on an industrial scale can benefit from certain conditions of raw materials, certain conditions of selling prices, and certain conditions of income tax. All evaluation parameters give a positive price. The development of this project needs to be added especially regarding additional strategies to increase profits and to attract investors, this study provides sufficient promise for the production of ZnO nanoparticles in developing countries.
Sarcasm Detection in Twitter - Performance Impact While Using Data Augmentation: Word Embeddings
Alif Tri Handoyo,Hidayaturrahman,Derwin Suhartono,Criscentia Jessica Setiadi 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.4
Sarcasm is the use of words commonly used to ridicule someone or for humorous purposes. Several studies on sarcasm detection have utilized different learning algorithms. However,most of these learning models have always focused on the contents of expression only, thusleaving the contextual information in isolation. As a result, they failed to capture the contextualinformation in the sarcastic expression. Moreover, some datasets used in several studies havean unbalanced dataset, thus impacting the model result. In this paper, we propose a contextualmodel for sarcasm identification in Twitter using various pre-trained models and augmentingthe dataset by applying Global Vector representation (GloVe) for the construction of wordembedding and context learning to generate more sarcastic data, and also perform additionalexperiments by using the data duplication method. Data augmentation and duplication impactis tested in various datasets and augmentation sizes. In particular, we achieved the bestperformance after using the data augmentation method to increase 20% of the data labeledas sarcastic and improve the performance by 2.1% with an F1 Score of 40.44% compared to38.34% before using data augmentation in the iSarcasm dataset.
Identifying Personality Traits for Indonesian User from Twitter Dataset
Nicholaus Hendrik Jeremy,Cristian Prasetyo,Derwin Suhartono 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4
Social media allows the user to convey their actual self and share their life experiences through numerous ways. This behavior in turn reflects the user’s personality. In this paper, we experiment to automatically predict user’s personality based on Big Five Personality Trait on Twitter. Our focus is towards Indonesian user. Not only word n-gram, Twitter metadata is also used in a certain combination to determine the feature that will be used to predict the personality. Our research also attempts to find optimum setting based on the number of n-gram, classifier, and twitter metadata. Our experiment yields 0.7482 at most on F-Measure. We conclude that among all scenario, twitter metadata is the least impactful feature, while word n-gram impacts the most.
Diana Nur Afifah,Muhammad Sulchan,Dahrul Syah,Yanti,Maggy Thenawidjaja Suhartono,Jeong Hwan Kim 한국식품영양과학회 2014 Preventive Nutrition and Food Science Vol.19 No.3
Bacillus pumilus 2.g isolated from gembus, an Indonesian fermented soybean cake, secretes several proteases that have strong fibrinolytic activities. A fibrinolytic enzyme with an apparent molecular weight of 20 kDa was purified from the culture supernatant of B. pumilus 2.g by sequential application of ammonium sulfate precipitation, ion-exchange chromatography, and hydrophobic chromatography. The partially purified enzyme was stable between pH 5 and pH 9 and temperature of less than 60 ℃. Fibrinolytic activity was increased by 5 mM MgCl2 and 5 mM CaCl2 but inhibited by 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM sodium dodecyl sulfate (SDS), and 1 mM ethylenediaminetetraacetic acid (EDTA). The partially purified enzyme quickly degraded the α and β chains of fibrinogen but was unable to degrade the γ hain.