Sepuluh Nopember Institute of Technology

The world is under pressure from the novel COVID-19 pandemic. Indonesia is the fourth most populous country in the world and predicted to be affected significantly over a longer time period. Our paper aims to provide detailed reporting and analyses of the present rapid responses to COVID-19, between January and March 2020, in Indonesia. We particularly highlight responses taken by the governments, non-government organisations and the community. We outline gaps and limitations in the responses, based on our rapid analysis of media contents, from government speeches and reports, social and mass media platforms. We present five recommendations toward more rapid, effective, and comprehensive responses.

In this study we estimated the costs of back pain to society in The Netherlands in 1991 to be 1.7% of the GNP. The results also show that musculoskeletal diseases are the fifth most expensive disease category regarding hospital care, and the most expensive regarding work absenteeism and disablement. One-third of the hospital care costs and one-half of the costs of absenteeism and disablement due to musculoskeletal disease were due to back pain. The total direct medical costs of back pain were estimated at US$367.6 million. The total costs of hospital care due to back pain constituted the largest part of the direct medical costs and were estimated at US$200 million. The mean costs of hospital care for back pain per case were US$3856 for an inpatient and US$199 for an outpatient. The total indirect costs of back pain for the entire labour force in The Netherlands in 1991 were estimated at US$4.6 billion; US$3.1 billion was due to absenteeism and US$1.5 billion to disablement. The mean costs per case of absenteeism and disablement due to back pain were US$4622 and US$9493, respectively. The indirect costs constituted 93% of the total costs of back pain, the direct medical costs contributed only 7%. It is therefore concluded that back pain is not only a major medical problem but also a major economical problem.

The ubiquitous problem of pesticide in aquatic environment are receiving worldwide concern as pesticide tends to accumulate in the body of the aquatic organism and sediment soil, posing health risks to the human. Many pesticide formulations had introduced due to the rapid growth in the global pesticide market result from the wide use of pesticides in agricultural and non-agricultural sectors. The occurrence of pesticides in the water body is derived by the runoff from the agricultural field and industrial wastewater. Soluble pesticides were carried away by water molecules especially during the precipitation event by percolating downward into the soil layers and eventually reach surface waters and groundwater. Consequently, it degrades water quality and reduces the supply of clean water for potable water. Long-time exposure to the low concentration of pesticides had resulted in non-carcinogenic health risks. The conventional method of pesticide treatment processes encompasses coagulation-flocculation, adsorption, filtration and sedimentation, which rely on the phase transfer of pollutants. Those methods are often incurred with a relatively high operational cost and may cause secondary pollution such as sludge formation. Advanced oxidation processes (AOPs) are recognized as clean technologies for the treatment of water containing recalcitrant and bio-refractory pollutants such as pesticides. It has been adopted as recent water purification technology because of the thermodynamic viability and broad spectrum of applicability. This work provides a comprehensive review for occurrence of pesticide in the drinking water and its possible treatment.

Purpose Increasingly, companies need to be vigilant with the risks that can harm the short‐term operations as well as the long‐term sustainability of their supply chain (SC). The purpose of this paper is to provide a framework to proactively manage SC risks. The framework will enable the company to select a set of risk agents to be treated and then to prioritize the proactive actions, in order to reduce the aggregate impacts of the risk events induced by those risk agents. Design/methodology/approach A framework called house of risk (HOR) is developed, which combines the basic ideas of two well‐known tools: the house of quality of the quality function deployment and the failure mode and effect analysis. The framework consists of two deployment stages. HOR1 is used to rank each risk agent based on their aggregate risk potentials. HOR2 is intended to prioritize the proactive actions that the company should pursue to maximize the cost‐effectiveness of the effort in dealing with the selected risk agents in HOR1. For illustrative purposes, a case study is presented. Findings The paper shows that the innovative model presented here is simple but useful to use. Research limitations/implications In the proposed framework, the correlations between risk events are ignored, something that future studies should consider including. Practical implications The framework is intended to be useful in practice. For the calculation processes, a simple spreadsheet application would be sufficient. However, most of the entries needed in the model are based on subjective judgment and hence cross‐functional involvement is needed. Originality/value The paper adds to the SC management literature, a novel practical approach of managing SC risks, in particular to select a set of proactive actions deemed cost‐effective.

The utilization of metal-based conventional coagulants/flocculants to remove suspended solids from drinking water and wastewater is currently leading to new concerns. Alarming issues related to the prolonged effects on human health and further pollution to aquatic environments from the generated nonbiodegradable sludge are becoming trending topics. The utilization of biocoagulants/bioflocculants does not produce chemical residue in the effluent and creates nonharmful, biodegradable sludge. The conventional coagulation-flocculation processes in drinking water and wastewater treatment, including the health and environmental issues related to the utilization of metal-based coagulants/flocculants during the processes, are discussed in this paper. As a counterpoint, the development of biocoagulants/bioflocculants for drinking water and wastewater treatment is intensively reviewed. The characterization, origin, potential sources, and application of this green technology are critically reviewed. This review paper also provides a thorough discussion on the challenges and opportunities regarding the further utilization and application of biocoagulants/bioflocculants in water and wastewater treatment, including the importance of the selection of raw materials, the simplification of extraction processes, the application to different water and wastewater characteristics, the scaling up of this technology to a real industrial scale, and also the potential for sludge recovery by utilizing biocoagulants/bioflocculants in water/wastewater treatment.

There are two major problems that we are facing currently. Firstly, a growing human population continues to contribute to the increased food demand. Secondly, the volume of organic waste produced will threaten human health and the quality of the environment. Recently, there is an increasing number of efforts placed into farming insect biomass to produce alternative feed ingredients. Black soldier fly larvae (BSFL), Hermetia illucens have proven to convert organic waste into high-quality nutrients for pet foods, fish and poultry feeds, as well as residue fertilizer for soil amendment. However, better BSFL feed formulation and feeding approaches are necessary for yielding a higher nutrient content of the insect body, and if performed efficiently, whilst converting waste into higher value biomass. Lastly, this paper reveals that BSFL, in fact, thrives in various ranges of organic matter composition and with simple rearing systems.

Oilfield produced water (OPW) has become a primary environmental concern due to the high concentration of dissolved organic pollutants that lead to bioaccumulation with high toxicity, resistance to biodegradation, carcinogenicity, and the inhibition of reproduction, endocrine, and non-endocrine systems in aquatic biota. Photodegradation using photocatalysts has been considered as a promising technology to sustainably resolve OPW pollutants due to its benefits, including not requiring additional chemicals and producing a harmless compound as the result of pollutant photodegradation. Currently, titanium dioxide (TiO2) has gained great attention as a promising photocatalyst due to its beneficial properties among the other photocatalysts, such as excellent optical and electronic properties, high chemical stability, low cost, non-toxicity, and eco-friendliness. However, the photoactivity of TiO2 is still inhibited because it has a wide band gap and a low quantum field. Hence, the modification approaches for TiO2 can improve its properties in terms of the photocatalytic ability, which would likely boost the charge carrier transfer, prevent the recombination of electrons and holes, and enhance the visible light response. In this review, we provide an overview of several routes for modifying TiO2. The as-improved photocatalytic performance of the modified TiO2 with regard to OPW treatment is reviewed. The stability of modified TiO2 was also studied. The future perspective and challenges in developing the modification of TiO2-based photocatalysts are explained.

Deep Learning adalah sebuah bidang keilmuan baru dalam bidang Machine Learning yang akhir-akhir ini berkembang karena perkembangan teknologi GPU accelaration. Deep Learning memiliki kemampuan yang sangat baik dalam visi komputer. Salah satunya adalah pada kasus klasifikasi objek pada citra. Dengan mengimplementasikan salah satu metode machine learning yang dapat digunakan untuk klasifikasi citra objek yaitu CNN. Metode CNN terdiri dari dua tahap. Tahap pertama adalah klasifikasi citra menggunakan feedforward. Tahap kedua merupakan tahap pembelajaran dengan metode backpropagation. Sebelum dilakukan klasifikasi, terlebih dahulu dilakukan praproses dengan metode wrapping dan cropping untuk memfokuskan objek yang akan diklasifikasi. Selanjutnya dilakukan training menggunakan metode feedforward dan backpropagation. Terakhir adalah tahap klasifikasi menggunakan metode feedforward dengan bobot dan bias yang diperbarui. Hasil uji coba dari klasifikasi citra objek dengan tingkat confusion yang berbeda pada basis data Caltech 101 menghasilkan rata-rata nilai akurasi mencapai. Sehingga dapat disimpulkan bahwa metode CNN yang digunakan pada Tugas Akhir ini mampu melakukan klasifikasi dengan baik.

Penelitian yang berjudul Revolusi Industri dan Tantangan Perubahan Sosial berisi tentang kajian sosial tentang pengaruh sosial yang terjadi dalam revolusi industri 4.0. Metode yang dipakai dalam penelitian ini adalah kualitatif. Data yang diperoleh berasal dari kajian studi pustaka yang dianalisis secara hermene u tik filosofis. Hasil yang dicapai dalam penelitian ini adalah bahwa revolusi industri tidak hanya mendisrupsi bidang teknologi saja, namun juga bidang lainnya, seperti hukum, ekonomi, dan sosial, Untuk mengatasi era disrupsi tersebut maka diperlukan revitalisasi peran ilmu sosial humaniora sebagai dasar acuan pengembangan teknologi agar teknologi tidak tercerabut dari nilai-nilai kemanusiaan.

Personality is a fundamental basis of human behavior. Personality affects the interaction and preferences of an individual. People are required to take a personality test to find out their personality. Social media is a place where users express themselves to the world. Posts made by users of social media can be analyzed to obtain their personal information. This experiment uses text classification to predict personality based on text written by Twitter users. The languages used are English and Indonesian. Classification methods implemented are Naive Bayes, K-Nearest Neighbors and Support Vector Machine. Testing results showed Naive Bayes slightly outperformed the other methods.

Online food delivery service (OFDS) has been widely utilized during the new normal of the COVID-19 pandemic, especially in a developing country such as Indonesia. The purpose of this study was to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of the COVID-19 pandemic in Indonesia by utilizing the extended theory of planned behavior (TPB) approach. A total of 253 respondents voluntarily participated and answered 65 questions. Structural equation modeling (SEM) indicated that hedonic motivation (HM) was found to have the highest effect on customer satisfaction, followed by price (P), information quality (IQ), and promotion (PRO). Interestingly, this study found out that usability factors, such as navigational design (ND) and perceived ease of use (PEOU) were not significant to customer satisfaction and loyalty in OFDS during the new normal of COVID-19. This study can be the theoretical foundation that could be very beneficial for OFDS investors, IT engineers, and even academicians. Finally, this study can be applied and extended to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of COVID-19 in other countries.

Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters.

The novel coronavirus pandemic (COVID-19) has massively disrupted supply chains at the global and local scales resulting in economic slowdown and social issues. To respond to these changes, supply chains need to quickly adapt to the new situation. This paper presents a review of literature that addresses supply chains under disruptions due to COVID-19 pandemic. Papers are classified based on issues addressed. The major findings or recommendations are discussed. These include the rising importance of safety, digitalisation, localisation, the need to revisit the meaning of efficiency, and the production and distribution of COVID-19 vaccine. We show that most mitigation actions proposed prior to COVID-19 such as redundancy and flexibility are still considered as possible strategies to mitigate supply chain disruptions due to COVID-19, but there are stronger pressures for digitalisation and supply-based localisation. The research agenda is also outlined at the end of the paper.

Increasing environmental awareness among societies is motivating consumers to use green cosmetic products. Green skincare products are the fastest growing sector in the worldwide market compared with other green cosmetic products. However, compared with general cosmetic products, the market share of green cosmetic products in Indonesia is relatively low. The present research investigated consumers’ purchasing intentions toward green skincare products in Indonesia using the pro-environmental reasoned action (PERA) model. A total of 251 female consumers participated in this study. Structural equation modeling was conducted to reveal the relationships between the five factors in the PERA model. The results indicated that perceived authority support (PAS) has a positive effect on perceived environmental concern (PEC). PAS and PEC have positive effects on attitude (AT) and subjective norms (SN), and AT and SN have positive effects on behavioral intention (BI) to purchase green skincare products, with the key factor being attitude. The PERA model was able to describe 62.6% of the BI to purchase green skincare products. Green skincare companies are recommended to produce more green skincare products and market the products by involving public figures and emphasizing the green attributes. Furthermore, we recommend that green skincare companies produce quality and sustainable products using quality processes, and be involved in pro-environmental activity to increase consumer attention to the green skincare products.

Around the globe, surges of bacterial diseases are causing serious health threats and related concerns. Recently, the metal ion release and photodynamic and photothermal effects of nanomaterials were demonstrated to have substantial efficiency in eliminating resistance and surges of bacteria. Nanomaterials with characteristics such as surface plasmonic resonance, photocatalysis, structural complexities, and optical features have been utilized to control metal ion release, generate reactive oxygen species, and produce heat for antibacterial applications. The superior characteristics of nanomaterials present an opportunity to explore and enhance their antibacterial activities leading to clinical applications. In this review, we comprehensively list three different antibacterial mechanisms of metal ion release, photodynamic therapy, and photothermal therapy based on nanomaterials. These three different antibacterial mechanisms are divided into their respective subgroups in accordance with recent achievements, showcasing prospective challenges and opportunities in clinical, environmental, and related fields.

Research on the digital economy continues to develop but is limited to one country and/or field. From a bibliometric retrospective review, this study purposes to visually research mapping and research trends in the field of the digital economy on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 2,784 scientific documents published from 1984 to 2019. This study proposes a grouping of digital economy research themes: Information systems, Digitization, E-commerce, Education, Engineering, Marketing, Industrial revolutions, and Information technology, abbreviated as IDEEEMII research themes.

Supply chain risk management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published since a few years ago. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2007 are analysed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. We also looked at the papers in terms of the types of risks, the unit of analysis, the industry sectors, and the risk management process or strategies addressed. The literature review will provide the basis for outlining future research opportunities in this field.

A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.

Assessment of service quality has been widely utilized in after-sales service, especially in the automotive industry. The purpose of the study was to determine factors affecting customer satisfaction in an automotive after-sales service at Toyota Dasmarinas-Cavite Philippines by utilizing the SERVQUAL approach. Several SERVQUAL dimensions such as tangibles, reliability, responsiveness, assurance, and empathy were analyzed simultaneously to the customer satisfaction. Structural equation modeling (SEM) indicated that among the five SERVQUAL dimensions, reliability and empathy were found to have significant relationships to the satisfaction of customers at Toyota Dasmarinas-Cavite Philippines. Interestingly, tangibles, responsiveness, and assurance were found to have no significant relationships to satisfaction. The servicing dealer must deliver a high quality of service to meet customer expectations and achieve high customer satisfaction, which subsequently builds customer trust towards the company. With these, customer retention and loyalty can be attained by the company that can also increase the company’s profit and competitive advantage.

Melanoma is one of the most common types of cancer that can lead to high mortality rates if not detected early. Recent studies about deep learning methods show promising results in the development of computer-aided diagnosis for accurate disease detection. Therefore, in this research, we propose a method for classifying melanoma images into benign and malignant classes by using deep learning model and transfer learning. MobileNetV2 network is used as the base model because it has lightweight network architecture. Therefore, the proposed system is promising to be implemented further on mobile devices. Moreover, experimental results on several melanoma datasets show that the proposed method can give high accuracy, up to 85%, compared with other networks. Furthermore, the proposed architecture of the head model, which uses a global average pooling layer followed by two fully-connected layers, gives high accuracy while maintaining the network’s efficiency.