bluuebunny/crossref_metadata_2025_split huggingface dataset:
Root-Mean Adjustment Threshold for the Improvement of Anomaly Detection Algorithm in Hyperspectral Images
Authors: Zaini M , Rizkinia M 
Published: March 2020

Abstract
Hyperspectral anomaly detection aims at identifying unique objects with different spatial and spectral appearances from its surrounding pixels. One of the widely used conventional methods is Collaborative-Representation-Based Detector (CRD). CRD approximates each pixel on the background that can be represented by neighboring pixels, while the anomaly pixels cannot be. The detection image generated from this method is quite satisfying since it can detect anomaly pixels quite accurately. However, the resulting detection images still contain a lot of pixels which are not anomaly even with tiny intensity values. In this study, we apply the Root-Mean (RM) adjustment threshold to filter the false-positive pixels in the output images of CRD, so that it gives more accurate results. In the experiment, six hyperspectral data with the corresponding ground truth images are used. The results show that after applying the RM threshold, the accuracy increases with the decrease of the six RMSEs in the range of 31.51 – 63.58%.
Analysis of Arduino Uno Application on Control System Based on Industrial Scale
Authors: Taufiq A J , Kurniawan I H , Nugraha T A Y 
Published: March 2020

Abstract
This research analyses the use of Arduino Uno as a controller, replacing the industrial based controller on the oil and gas industry with the plant: one of the control loops in the Refinery Fuel Oil Complex I PT. Pertamina (Persero) Refinery Unit IV Cilacap, Indonesia. Research is done by making a simulation plant using industrial-scale instrumentation with the Arduino Uno as the controller, and compare the results with the controller for the actual plant. The result is Arduino Uno is able controlling industrial-scale plant-based instrumentation for one control loop that was tested and was able to eliminate the difference between Process Variables and Sets Points at different control modes are proportional to industrial class controllers by using the Proportional, Integral and Derivative (PID) algorithms together with the combination.
Feature Selection Using Rough Set Theory Algorithm for Breast Cancer Diagnosis
Authors: Hardani D N K , Nugroho H A 
Published: March 2020

Abstract
Feature selection is one of the pre-processing stages of classification carried out by selecting relevant features that affect the results of classification. The advantage of feature selection is that it increases the value of accuracy. Data mining in the medical world has excellent potential for knowing hidden patterns in medical data sets. However, medical data sets often have large dimensions and have irrelevant features that can decrease the performance of the algorithm. This study aims to analyse the performance of the rough set approach as an algorithm used for feature selection in breast cancer diagnosis cases. This study conducted a feature selection process on the Wisconsin Breast Cancer (Diagnostic) Data Set provided by the UCI machine learning repository. There are several steps taken in research to realize these goals, such as data pre-processing, feature selection, data randomization, classification and performance evaluation. The result shows that feature selection using the rough set of methods has proven to be effective in reducing a large number of features in the data set.
Eliciting Requirements of Order Fulfilment in A Company
Authors: Andry Johanes Fernandes , Tannady Hendy , Nurprihatin Filscha 
Published: March 2020

Abstract
The Order Fulfillment Process (OFP) is one of the core business processes in an organisation. OFP in a manufacturing company, the wide range of activities involved is carried out by people from different functional units. OFP is complicated and demands activity elicitation and coordination. Hence, it important for companies to re-engineer their OFPs to support their activities for the better. Oil distribution manufacturing is a company that acts as a manufacturer of Coolant Brake, and Radiator in Indonesia needs an application to fulfilled all its business process activities. In this research, the authors have elicited requirements of order fulfilment in this company used elicitation and collaboration methodology to gain knowledge about the user’s requirement or need. From this research, an order fulfilment application that has been computerised has been produced following their needs to support organisation activities for the better.
Rice Productivity Analysis by Province Using K-Means Cluster Algorithm
Authors: Supriyatna A , Carolina I , Widiati W , Nuraeni C 
Published: March 2020

AbstractRice(Oryza sativaL.) is a very important food crop in the world after wheat and corn. It is also a staple food for most of the world’s population, especially in Asia, like in Indonesia until now. In 2014 to 2018, rice productivity tended to change dynamically. In 2018, rice productivity in Indonesia was 51.92 (Ku/Ha). This research was conducted to classify rice productivity in 34 provinces in Indonesia in 2018. The data used were sourced from Statistics Indonesia. The method or approach used in this study is the K-Means cluster algorithm to classify rice productivity data by province in 2018. The results of the research are; (1) There are 19 provinces included in cluster 0 (Medium), (2) There are 4 provinces included in cluster 1 (High), and (3) There are 11 provinces that are included in cluster 2 (Low). Based on the results of the study, it was proven that there were 4 provinces in cluster 1 (High) they are West Java, Central Java, East Java and South Sulawesi, with the highest rice productivity. Three of them were on Java Island. It shows that Java still dominates the productivity of rice plants.
Stress Detection from Multimodal Wearable Sensor Data
Authors: Indikawati Fitri Indra , Winiarti Sri 
Published: March 2020

Abstract
Stress can be recognised by observing changes in physiological responses on the human body. Wearable sensors for stress detection are becoming more prominent in recent years due to their functionality and non-intrusive nature. By utilising data from wearable sensors, we have developed a personalized stress detection system. Our system performs classification on stress level using multimodal data from wrist-worn device Empatica E4 wearable sensor. We implemented three different classification algorithms: Logistic Regression, Decision Tree, and Random Forest and used four-class classification conditions: baseline, stress, amusement, and meditation. By evaluating the performance of the system, we demonstrate that our system can perform the best and consistent personalized stress detection using Random Forest classifier with the accuracy of 88%-99% on 15 subjects.
Classification of Farmer’s Eligibility as Recipients of Subsidized Fertilizer Assistance with C4.5 Algorithm
Authors: Yunita Diah Nur , Jones Anna Hendri Soleliza , Soyusiawaty Dewi 
Published: March 2020

Abstract
Farmers are the main actors in agricultural development, have a major contribution in realizing Indonesia’s agricultural vision. To support the realization of agricultural production and productivity targets, the government’s supports is by issuing policies to provide subsidized fertilizer assistance. The criteria for farmers who are eligible to receive subsidized fertilizer assistance are having land less than 1.9 hectares, have been members of farmer groups, and having work as a farmer or a private employee. Data collection was carried out using the method of observation and interviews with farmer groups in Gunung Kidul. Data classification is done by applying the C4.5 algorithm to 300 data of prospective farmers receiving subsidized fertilizer assistance. Application development includes interface design, making data flow diagrams, implementation and testing. From the research that has been done by applying the C4.5 Algorithm for farmer data, there are 300 data with 113 training data. Produce a data classification of farmers who are eligible to receive assistance at 95,58% accurate and targeted to the needs of farmers. This means that the C4.5 algorithm can be used as a classification for determining the eligibility of farmers to receive subsidized fertilizer.
Hanoman adventure game based on Android
Authors: Sugiyanto Sigit , Fauzan Achmad , Purwanto Lahan Adi , Hakim Dimara Kusuma 
Published: March 2020

Abstract
The use of smartphones to play mobile games is increasingly being used by everyone from children to adults. Along with the rapid development of the game industry, game engine software is now popping up, one of which is Construct 2. Construct 2 is an HTML5-based game maker software that is devoted to the 2D platform. Games can be a medium for cultural preservation in a country. One of them is Indonesian culture that needs to be preserved is wayang culture. This adventure game will raise a cultural puppet character named Hanoman, Hanoman is a figure in the form of a white monkey who acts as a character in helping Rama to save Shinta. This Hanoman adventure game in carrying out missions on each level will face the enemy characters who must be defeated to be able to open the next level. In this game, there are 3 levels where each level has different difficulties so that it can provide challenges for its users.
Case-Based Reasoning for Mortality Prediction of Catfish Farming Based on Water Quality
Authors: Suwarsito  , Mustafidah H , Mujiono S , Fitriani M A 
Published: March 2020

AbstractCatfish (Clarias gariepinus) is a species of freshwater fish that has good prospects for cultivation. This type of fish has a fast growth rate, is able to adapt to a bad environment and is easily cultivated. Water quality is the main element that influences the level of fish life. However, catfish deaths often occur when water quality is in certain conditions. Fish mortality can be predicted using an appropriate system so that it does not greatly affect the cultivation process. This research has succeeded in developing a prediction system for catfish mortality as a result of the water quality used for catfish farming. This system is based on case-based reasoning. The matching process starts by entering water quality condition variables such as Temperature, pH, Ammonia, Nitrite, and Dissolved Oxygen. Features entered by the user will be referred to the knowledge base. The system will look for a base case that has been indexed before. The similarity calculation with the highest value is used as a prediction of the effect of water quality on the mortality rate of catfish farming. The similarity of the test cases shows that the new case has a similarity level of up to 80%.
The Assessment of BRT Trans Semarang Service Quality
Authors: Adibah Amalia Nur , Manullang Okto Risdianto 
Published: March 2020

Abstract
Semarang city is one of the metropolitan cities that has congestion problems. The existence of BRT Trans Semarang becoming an alternative to reduce city congestion problems. Besides the service performance of BRT Trans Semarang, the service quality is also important in considering the travel modes choice. Tangibles are assessed based on the condition of the facility, the existence of information boards, the presence of officers, and the availability of information. Empathy is assessed based on the existence of accident insurance. Reliability is assessed based on service personnel. Responsiveness is assessed based on officer responsiveness. Assurance is assessed based on the level of knowledge and politeness of the officers. The main purpose of this research is to assess the service quality of BRT Trans Semarang related to tangibles, empathy, reliability, responsiveness, and assurance on weekdays and weekend. This research used the Kano Model to assess the service quality of BRT Trans Semarang based on user preferences. The results of the Kano Model analysis dividing the service quality indicators into several treatments can be repaired or added, can be used as a strategy, and can be prepared for the future. The government must prioritise improvements in corridor five because that corridor has the most deficiencies and need for improvement.
Sediment Trap in the Downstream of Check Dam: The Effect of Changes in Channel Depth on Sediment Deposition
Authors: Wulandari D A , Susilowati  , Ulfiana D , Parmantoro P N 
Published: March 2020

Abstract
Reservoir sedimentation is a major problem experienced by reservoirs in Indonesia. Reservoir sedimentation can be overcome by reducing the amount of sediment that enters the reservoir, among others, by building a Check dam. Check dam deposits more sediment loads with coarse grains, while sediment loads with fine grains will escape and enter the reservoir. So it is necessary to have construction to hold and settle fine sediment loads downstream of the Check dam. Construction is planned like a sediment trap in an irrigation system with variations in the cross-sectional area. In this study, the cross-section was expanded by changing its depth. The purpose of this research is to determine the effect of changes in the channel depth with discharge and grain size distribution variation on sediment deposition that occurs. The calculation is done using the HEC-RAS software. Simulation results show the deeper the sediment trap, the smaller the amount of sediment deposit. The large discharge, the smaller of deposit percentage. The greater of fine sediment percentage, the less the deposit.
Setting Time of the Geopolymer Binder with White Soil Substitution
Authors: Priastiwi Y A , Muhrozi  , Adi R Y , Daryanto D , Salamsyah Z 
Published: March 2020

Abstract
This study analyzes the effect of white soil as a substitution of fly ash on geopolymer binders. Fly ash from Tanjung Jati Jepara, white soil from Kupang, water and NaOH as alkaline activators are mixed into binders and tested for setting time with a Vicat tool. The water factor of the binder was used 0.5. The white soil substitution in fly ash using a percentage of 0 to 30 of the weight of fly ash. Fly ash and white soil used had the particle size smaller than 0.074 mm or passed the number 200 sieve and had a moisture content of 0%. The results show that the setting time of geopolymer binder with substitution white soil in the range of 52.5 to 105 minutes for the initial setting time and is between 180 to 225 minutes for the final setting time. This value approaches the same as the setting time of Portland cement in SNI 15-2049-2004. From the 7 variations tested, the binder with 15% of white soil has a faster setting time compared to other variations, i.e 52.5 minutes for initial and 180 minutes for final setting time. The temperature when testing affects the setting time of the geopolymer binder
Concrete Performance with Ground Granulated Blast Furnace Slag as Supplementary Cementitious Materials
Authors: Cahyani R A T , Rusdianto Y 
Published: March 2020

Abstract
Supplementary Cementitious Materials (SCMs) are materials that contribute to the hardened concrete properties through hydraulic or pozzolanic activities. The use of Ground Granulated Blast Furnace Slag (GGBFS) as SMCs or partial replacement of cementitious components in concrete provides benefits both in terms of economy and performance improvement. However, GGBFS is not widely known in Indonesia, hence the available research for this product is still limited. In this study, the performance of concrete with GGBFS as a partial replacement for cement was investigated, with the percentage of GGBFS varying by 0%, 30%, 50% and 70% of cement weight. The mechanical properties of concrete being observed are compressive strength and elastic modulus. Porosity testing is carried out to assess the durability aspect of concrete. Compressive tests are carried out when the concrete age reached 7, 28, 56 and 90 days to assess the strength development of GGBFS based concrete. From the study, it was found that GGBFS can replace cement up to a percentage of 50%, with its compressive strength comparable to conventional concrete. However, the addition of GGBFS in concrete mixes slowed the development of concrete compressive strength at early age. Concrete modulus of elasticity decreases with increasing GGBFS, but not significantly. The increasing percentage of GGBFS causes the pore volume to decrease, indicating the greater durability of GGBFS based concrete compared with the conventional one.
TEM observation of two-dimensional growth of lamellar gold electroplated on copper wires
Authors: Hu Chuanjin , Gao Yubo , Li Taotao , Wang Chongen 
Published: March 2020

Abstract
Electroplating is an essential process to produce Cu–Au complex bonding wires. Therefore, mastering the general growth behaviour of gold film plated on copper substrate has an instructive effect on the subsequent processes of pinching-out and annealing in the processing of Cu–Au complex bonding wires. In this paper, plated gold film consisted of an intermediate layer and a gold layer, which were characterised in terms of transmission electron microscope (TEM) observations, especially the techniques of selected area electron diffraction (SAED) and displaced-aperture dark field (DADF). A thin intermediate layer with polycrystalline gold is observed adjacent to the ribbon-like copper substrate, which is due to the competing mechanism between chemical and electrochemical reactions. The gold layer is detected in the outer regions of the intermediate layer, and the formation of the gold layer is dominated by the electrochemical mechanism. Lamellar gold orients randomly in the gold layer, whose outer surface is mainly enclosed by the planes of {111}. The lamellar gold grows in only two dimensions, as the vertical direction is blocked by sulphite near the cathode.
Green-synthesizing Ag nanoparticles by watermelon peel extract and their application in solar-driven interfacial evaporation for seawater desalination
Authors: Cao Hongxia , Cui Tianlei , Wang Wenyuan , Li Shilei , Tang Xueyun , Wang Hongyan , Zhu Guang 
Published: March 2020

Abstract
Ag nanoparticles displayed a more attractive application in solar-driven interfacial evaporation for seawater desalination, but their synthesis process was expensive and detrimental to the environment due to the consumption of chemicals. In this work, we green-synthesized Ag nanoparticles using watermelon peel extract as reducing agent, while the membrane with Ag nanoparticles was prepared for solar-driven interfacial evaporation. Moreover, the evaporation performance of the membranes with different Ag nanoparticles loadings was investigated, as well as the reusability and desalination application was also explored. The experimental results showed that the membrane with Ag nanoparticles loading of 0.05 g exhibited superior evaporation performance, corresponding to the evaporation rate of 1.37 kg m−2h−1 and the evaporation efficiency of 89.3%, respectively. In addition, the membrane with Ag nanoparticles possessed an excellent recyclability and concentration of ions in the desalinated seawater reached the potable water standard. Our research demonstrated that Ag nanoparticles green-synthesized by watermelon peel extract was an ideal candidate applied in solar-driven interfacial evaporation for seawater desalination.
Finite element investigation on the wave-particle interactions in ultrasonic inspection of SiCp/Al composites
Authors: Lu Shijin , Wang Xiaohui , Zhang Junjie , Zhou Zhenggan , Zhu Fengjin , Tong Zhen , Shao Haiyan , Miao Zhen 
Published: March 2020

Abstract
While particulate-reinforced metal matrix composites are composed of two phase materials with dramatically different physical and mechanical properties, sound wave-particle interactions play an important role in their ultrasonic inspection tests. In the present work, we investigate the sound wave-particle interactions in silicon carbide (SiC) particle-reinforced aluminum (Al) matrix composites under the pulse-echo mode ultrasonic inspection by means of finite element simulations. Be consistent with experimentally observed real microstructures, the simulated SiC particles have polygon shapes and are randomly dispersed in the Al matrix. In particular, the sound wave-particle interactions are revealed, and their correlations with the A-scan signals are investigated. Furthermore, the effects of extrinsic pulse frequency and intrinsic SiC particle size on the ultrasonic inspection of the composites are addressed. Simulation results indicate that the interference of sound waves with heterogeneous SiC particles leads to more pronounced deflection, scattering and conversion of sound waves than the pure Al matrix, which in turn result in higher attenuation of sound waves in SiCp/Al composites. It is also found that the sound wave-particle interactions have a strong dependence on both pulse frequency and particle size.
Machine learning the computational cost of quantum chemistry
Authors: Heinen Stefan , Schwilk Max , von Rudorff Guido Falk , von Lilienfeld O Anatole 
Published: March 2020

Abstract
Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance computer resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For 2D nonlinear toy systems, single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evidence for a toy system containing two functions and three commonly used optimizer and for thousands of organic molecular systems including closed and open shell equilibrium structures, as well as transition states. Levels of electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d), local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison to conventional indiscriminate job treatment, QML based wall time predictions significantly improve job scheduling efficiency for all tasks after training on just thousands of molecules. Resulting reductions in CPU time overhead range from 10% to 90%.
Cancer subtype classification and modeling by pathway attention and propagation
Authors: Lee Sangseon , Lim Sangsoo , Lee Taeheon , Sung Inyoung , Kim Sun 
Published: March 2020

Abstract

Motivation
Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.

Results
We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway–gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.

Availability and implementation
The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE.

Supplementary information
Supplementary data are available at Bioinformatics online.