Search

Jamu is an herbal medicine commonly used before the advent of modern medicine. Generally, the herbal formula is obtained empirically and passed down from generation to generation. However, the healing process with herbs is also influenced by such as myths and local customs. This influence causes differences in the use of herbal ingredients to cure the same disease. The result is a collection of herbal recipes that overlap each other without any supporting evidence of its validity. Protein-protein interaction (PPI) is a biological process that is influenced by drugs in the healing process. Therefore, PPI due to the consumption of herbs can be used as evidence of the effectiveness of herbal medicine. PPI analysis needs to be done to study how proteins interact with other proteins. PPI analysis with an experimental method (wet lab) cannot be carried out on extensive data and only covers a portion of protein interaction networks. Therefore, a computational approach needs to be done. In previous studies, predictions of PPIs were proven to be carried out using only protein sequence information. The advantage of using this protein sequence information is that this method is more universal. Information that can be obtained from protein sequences includes Discrete Cosine Transform, Multi-scale Local Descriptor, Autocovariance, and Conjoint Triad. The study with the sequence information has been done using different machine learning approaches, such as Support Vector Machines, Random Forest, and Probabilistic Neural Networks. A deep learning approach has also been done with Stacked-Autoencoder, which tried to construct a hidden structure of protein sequences. Previously, deep learning has also been proven to be able to handle raw and complex data on a large scale and learn the useful and abstract features of perceptual problems such as image recognition and voice. The method proposed in this study is deep neural networks that were trained using stacked-autoencoder and stacked-randomized autoencoder. The extracted features used are conjoint-triad. This study compares both methods which have different characteristics in the construction of layers in deep neural networks. We conducted experiments with k-Fold cross-validation which became the gold standard for most predictive model testing. Our experiments with 5 cross-validations and 3 hidden layers gave an average validation accuracy of 0.89 ± 0.02 for the SAE method and 0.51 ± 0.003 for the ML-ELM. [ABSTRACT FROM AUTHOR]

Flood disaster is one of the most common disasters in Jember District. One that is often affected is in 10% of the area of Jember District, namely the Tanggul watershed with an area of 345,875 km2. The condition of Tanggul River, with a length of 23.50 km, which tends to narrow and meander can reduce flow especially when the river discharge is enlarged due to rain. If rainfall is above 100 mm on the upstream part of the watershed, then the downstream of the Tanggul River with a length of ± 10.19 km that passes through Paseban Village, Kraton Village, and Kencong Village are often unable to withstand the discharge which will overflow the surrounding area. The history of flood occurrences in the Tanggul watershed in the last 25 years had occurred 27 times, including 9 times which resulting in breakdown of the embankment. According to records from the Lumajang Public Works Office, the latest flood event in Tanggul River was occurred on December 22, 2018 with water elevation reaches 3.5 m and a discharge of 749,153 m3. This resulted in a broken embankment with a length of 60 m and a height of 7.2 m from the riverbed. Mitigation efforts such as flood routing to get an overview and information on overtopping points is needed to be done in order to minimize losses. This research aims to simulate flood using integration of the main programs DELFT3D and GIS. The flood routing model is obtained by utilizing a spatial model of analysis in Geographic Information Systems (GIS) to determine critical embankment or overtopping points and hydraulic flow simulation with DELFT3D program. The modeling results are visually compared with the coordinates of the flood events in the field that prepared by the Lumajang Public Works Office. Based on the 1000-year return period, the overtopping point occurs at ± 12.8 km from the downstream, namely Kencong District and ± 11.8 km from the downstream, namely Paseban District. In the end, this model can be used as a framework for the Tanggul watershed flood routing model to look for the effectiveness of various non-technical flood control alternatives such as land conservation and technical such as river normalization. [ABSTRACT FROM AUTHOR]

The investment climate that attracts investors in welcoming the industrial era 4.0 is the growth of infrastructure supported by the presence of construction companies and adequate transportation equipment. Industrial competitiveness in the field of construction and transportation are two interrelated business fields. The growth and competitiveness of the two fields are influenced by the quality of the company's performance. The quality of a company's performance is influenced by several determinants that influence the success of a business entity, namely internal factors and external factors. Internal and external factors of the company are very important and influence the company's success. The research objective discussed in this article is the analysis of business entities that affect the competitiveness of the construction industry and the shipbuilding industry. The intended business entity is an environment that influences the development of the two industries. The environment in question is the internal and external environment of the company that affects the competitiveness of the national construction and shipyard industry in Indonesia. The method used to obtain data in achieving these objectives is with literature studies and surveys to identify problems and factors that influence the success of both business fields. The approach of the analytical method used in this study is multivariate analysis. The results showed internal and external factors that affect the competitiveness of the construction industry and shipyard industry in Indonesia have many similarities, internal factors that have the highest loading factors are: financial of the company, markets, technology , and the ability of the facilities owned. External factors that influence include: economic and political stability related to financial capability, cooperation, project availability, R&D, and government policies that support the growth and development of the construction industry and shipyard industry. [ABSTRACT FROM AUTHOR]

The technology used to help blind people has been developed using various technologies such as an ultrasonic sensor in the form of sonar system in the form of belts and watches. The sonar vision works based on the principle of reflecting a wave with a frequency of 20,000 Hz so that it can be used to determine the distance of an object in a range of up to 3 meters. However, the sonar system cannot identify the object. Computer Vision emerges as one of the most promising solutions. This article focused on developing blind visual aid using a Faster R-CNN identify an object. The Faster R-CNN is instilled on the raspberry pi equipped with a camera. The output is an audio signal about the object and its position refer to the users. The results of this study are able to recognize objects automatically well and accurately. [ABSTRACT FROM AUTHOR]

The development of education today is required to reflect specificity in future development achievements, the needs of infrastructure related to Biomass and Bioenergy shall be facilitated well. Related to this goal shall be supported with facilities and infrastructure that is integrated laboratory building. The construction activity of the laboratory that develops massively made complexity of work that needs quick method IT and project management based to monitoring and minimalize mistakes in development process. The performance of integrated project management overall is obtained using Building Information Modeling (BIM) concept. This article is using one of the features from BIM program that is Revit Architecture because it is capable to give 2D work picture output, 3D modelling and the volume of each work. The aim of this article is to model the building using BIM and to arrange scheduling using Ms. Project. Modelling only consider the structure and architecture work. The study case of this research is IsDB Engineering Biotechnology. Validation is done to check the volume of work from the result of modelling. The result of validation obtained is error (%). The result of Revit Architecture modelling that is done to the whole work has volume that corresponds to DEDC volume except to the reinforcement work. This happened because of the bending and hook reinforcement settings are using default setting so that the error is more than 0%. The result of Revit Architecture modelling is used as the guidelines for scheduling using Ms. Project. The length of time needed in building the IsDB Engineering Biotechnology building is 232 days. [ABSTRACT FROM AUTHOR]

Patients with physical disabilities such as losing an arm, hand, or paralysis will have difficulty moving from one place to another. They need someone or a device that can help their mobility. One of the tools that are often used by physically disabled patients to help their activities is an electric wheelchair. The main purpose of this article is to design a wheelchair for physically disabled patients that can be controlled by voice commands using the convolutional neural network method (CNN). CNN is employed to identify voice commands embedded on raspberry Pi 3. The recorded sound data is converted to spectrogram images before being fed to CNN. This method has been proven well in voice commands recognition with an accuracy of more than 90%. There are five different voice commands for wheelchair navigation, which are forward, backward, left, right and stop. Preliminary experimental results indicate that electrically designed wheelchairs can move using speech command. [ABSTRACT FROM AUTHOR]

This paper presents a tool that would calculate traffic density through vehicle detection using image processing. Haar-Cascading Algorithm was used in vehicle detection. The developed tool used video recordings from identified Cross and T-type road junctions in the City of Mandaue. Traffic video recording were provided by the Traffic Enforcement Agency of Mandaue (TEAM). The system will classify traffic status as low, moderate or heavy based on the calculated traffic density. It is also capable of simulating traffic light control by displaying a Go signal in one part of the road in a particular road junction at a time. The duration of this Go signal will depend on the traffic status determined by the system. Results showed that the system has an average accuracy rate of 80% in vehicle detection and can correctly classify traffic status from the calculated traffic density. [ABSTRACT FROM AUTHOR]