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Innovation and utilization of coffee skin waste (Coffea robusta L) as a superior compost for the growth of pepper cuttings (Piper nigrum L.)

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AIP Conference Proceedings 2025年第1期3186卷

作者： Awalul Fatiqin Dewi Lestari R. A. Hoetary Tirta Amalia Riri Novita Sunarti Ike Apriani Anita Restu Puji Raharjeng Rr Mini Sari Wulan Thathit Suprayogi Yahya Febrianto Sudarman Rahman Shesanthi Citrariana Rokiy Alfanaar Mu’afa Purwa Arsana Yithro Serang Wansadaj Jaroenram Biology Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Biology Study Program Faculty of Science and Technology Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Education Biology Study Program Faculty of Teacher Training and Education Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Physics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Pharmacy Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Chemistry Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Mathematics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Department of applied chemistry Chaoyang University of Technology Taichung City Taiwan National center for genetic engineering and biotechnology (BIOTEC) Pathum Thani Thailand

This study aimed to determine the potential of coffee skin waste (Coffea robusta L) as a superior compost innovation for the growth of pepper-cutting seedlings (Piper nigrum L). This study used an experimental method with a completely randomized design using five treatments and five repeats, variations in the dose of coffee skin waste used P0 (control), P1 (90 g), P2 (135 g), P3 (180 g), and P4 (225 g). The shoot height, number of leaves, number of roots, and length of roots were measured. The results showed that coffee skin waste compost significantly affected shoot height, especially at P1 (90 g), resulting in an average growth height of 7.24 cm. In addition, the highest number of leaves was found at a concentration of P1 (90 g), with an average of two leaves. Although there was no significant effect on the number of roots, the concentration of P1 (90 g) yielded the best results, with an average of 33 root blades, and the best root length was obtained at the concentration of P1 (90 g), with an average of 44.6 cm. Therefore, coffee skin waste significantly influenced shoot height and the number of leaves on pepper-cut seedlings (Piper nigrum L.) in the P1 treatment (90 g).

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Early detection of the financial crisis in Indonesia, Thailand, and South Korea based on real exchange rates 2

Early detection of the financial crisis in Indonesia, Thaila...

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2nd International Conference on Lesson study of science Technology Engineering and mathematics 2020, ICOLSSTEM 2020

作者： Ratnasari, A. Sugiyanto Sulandari, W. Study Program of Statistics Faculty of Mathematics and Natural Science Universitas Sebelas Maret Surakarta Indonesia

The 1997 Asian financial crisis and the 2008 global financial crisis had a significant impact on the economies of various countries, including Indonesia, Thailand, and South Korea. Therefore, this study aims to detect early financial crises in these three countries based on real exchange rate indicators. The early detection of financial crises, relevant institutions in applying appropriate policies. Thus, financial crises can be avoided. The real exchange rate indicator contains fluctuations, and changing conditions. As a solution, the volatility models, i.e. autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) explains volatility or fluctuations, and Markov switching (MS) explains changing conditions. The combination of these models yields a smoothed probability value used for detecting financial crises. The real exchange rate of Indonesia, Thailand, and South Korea data against the United States were used from January 1990 to March 2020. The results showed that MSARCH( 2,1), MS-ARCH(2,1), and MS-GARCH(2,1,1) are for Indonesia, Thailand, and South Korea, respectively. The real exchange rate indicator can detect crises in Indonesia and South Korea in the 1997 and 2008. Whereas in Thailand, it was only able to detect crises occured in the 1997. The prediction results of the smoothed probability showed no signal of the financial crisis in Indonesia, Thailand, and South Korea one year later. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Finance

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Enhancing coastal community preparedness: The role of education, experience, and exposure to information

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Progress in Disaster science 2025年 26卷

作者： Oktari, Rina Suryani Sofyan, Hizir Hidayati, Alfy Rolanda, Randy Syamsidik Puturuhu, Ferad Sasaki, Daisuke Tsunami & Disaster Mitigation Research Center (TDMRC) Graduate Program in Disaster Science and Department of Family Medicine Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Statistics Faculty of Mathematics and Natural Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Civil Engineering Faculty of Engineering and Tsunami & Disaster Mitigation Research Center Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agricultural Cultivation Faculty of Agriculture Universitas Pattimura Ambon 97233 Indonesia International Research Institute of Disaster Science (IRIDeS) Tohoku University Sendai 980-0845 Japan

Coastal hazards create impact damage on vital assets in lowland coastal zones. While education, experience, and information exposure affect individual preparedness, the combined impact of coastal hazards on community readiness needs further exploration. This study aims to explore factors influencing coastal community preparedness, by examining relationships between preparedness and information exposure variables, and by assessing the impact of knowledge, experience, and information exposure using Structural Equation Modelling (SEM). This study involves 932 respondents in the Indonesian coastal cities of Banda Aceh, Mataram, and Ambon. The SEM analysis confirmed a well-fitting model with the following statistics: χ2 = 17.961, RMSEA = 0.017, GFI = 0.996, CFI = 0.998, AGFI = 0.986, TLI = 0.996, and Normed Chi-Square = 1.283. These results indicate strong alignment between the proposed model and the observed data. The study found positive relationships between preparedness variables and their respective indicators. Similarly, exposure variables related to information sources on coastal hazards and early warnings also showed positive associations with their indicators. Education, experience, and exposure to information were identified as significant factors influencing community preparedness, explaining 89 % of the variability in preparedness variables. These findings underscore the importance of these factors in enhancing community resilience to coastal hazards. © 2025 The Authors

关键词： Climate change Coastal hazards Community resilience Disaster Structural equation

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Implementing and evaluating an automatic centroid moment tensor procedure for the Indonesia region and surrounding areas

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Earth and Planetary Physics 2024年第4期8卷 609-620页

作者： Madlazim Muhammad Nurul Fahmi Dyah Permata Sari Ella Meilianda Sorja Koesuma Physics Department Faculty of Mathematics and Natural SciencesUniversitas Negeri SurabayaSurabaya 60231Indonesia Science Education Study Program Faculty of Mathematics and Natural SciencesUniversitas Negeri Surabaya 60231SurabayaIndonesia Disaster Science Department Faculty of Mathematics and Natural SciencesUniversitas Syiah KualaAceh 23126Indonesia Physics Department Faculty of Mathematics and Natural SciencesUniversitas Sebelas MaretSolo 57126Indonesia

The purpose of this research was to suggest an applicable procedure for computing the centroid moment tensor(CMT)automatically and in real time from earthquakes that occur in Indonesia and the surrounding *** software was used to estimate the CMT solution by selecting the velocity model that best suited the local and regional geological conditions in Indonesia and the surrounding *** data used in this study were earthquakes with magnitudes of 5.4 to ***-quality,real-time broadband seismographic data were provided by the International Federation of Digital Seismograph Networks Web Services(FDSNWS)and the European Integrated Data Archive(EIDA)Federation in Indonesia and the surrounding ***,the inversion process and filter adjustment were carried out on the seismographic data to obtain good CMT *** CMT solutions from Gisola provided good-quality solutions,in which all earthquake data had A-level quality(high quality,with good variant reduction).The Gisola CMT solution was justified with the Global CMT(GCMT)solution by using the Kagan angle value,with an average of approximately 11.2°.This result suggested that the CMT solution generated from Gisola was trustworthy and *** Gisola CMT solution was typically available within approximately 15 minutes after an earthquake *** it met the quality requirement,it was automatically published on the *** catalog of local and regional earthquake records obtained through this technology holds great promise for improving the current understanding of regional seismic activity and ongoing tectonic *** accurate and real-time CMT solution generated by implementing the Gisola algorithm consisted of moment tensors and moment magnitudes,which provided invaluable insights into earthquakes occurring in Indonesia and the surrounding areas.

关键词： centroid moment tensor Gisola International Federation of Digital Seismograph Networks Web Services(FDSNWS) real time Indonesia

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Human development index modelling in South Kalimantan province using panel regression

Human development index modelling in South Kalimantan provin...

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International Conference on Mathematical and Statistical sciences 2021, ICMSS 2021

作者： Istiqhomah, M. Salam, N. Lestia, A.S. Statistics Study Program Faculty of Mathematics and Science Lambung Mangkurat University Indonesia

Human development is a paradigm and becomes the focus and target of all development activities. Development is a way to improve welfare and a better quality of life. The Human Development Index (HDI) is one indicator to measure the success of a development. The purpose of this research is to describe the factors that are thought to influence HDI in South Kalimantan Province, estimate the parameters of the HDI panel regression model, and determine the best model. The data of this research is sourced from the Central statistics Agency (BPS) of South Kalimantan Province with a period from 2015-2018. Based on the results of data analysis it can be concluded that the Fixed Effect Model with the time effect is the best model of the HDI panel regression in South Kalimantan Province with an R-Squared value of 99, 81. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Regression analysis

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Application of the K-Means algorithm to determine poverty status in Hulu Sungai Tengah

Application of the K-Means algorithm to determine poverty st...

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International Conference on Mathematical and Statistical sciences 2021, ICMSS 2021

作者： Istiqamah, N. Soesanto, O. Anggraini, D. Statistics Study Program Faculty of Mathematics and Science Universitas Lambung Mangkurat Indonesia

Poverty is a condition of living in an inability to meet the minimum needs of life or basic needs. In Indonesia, poverty is one of the main problems that still need an optimal solution. Several government programs to address the problem of poverty have been carried out, but not infrequently the implementation is not right on target. The importance of this assistance is expected to improve the welfare of the community so it is very unfortunate if the assistance has not been right on target. This study aims to determine the status of poverty in Hulu Sungai Tengah Regency. By observing a problem above, it can be necessary to use a grouping method in determining poverty status. so that in this study using the cluster method, namely K-Means in clustering population data. Based on the results of data analysis using 353 head of family in the population data of HST Regency, it can be concluded that there are three poverty status clusters, namely low-level poverty (cluster 3) with a total of 130 head of family, medium-level poverty (cluster 2) with a total of 130 head of family. 111 head of family, and high poverty level (cluster 1) with a total of 112 head of family. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： K-means clustering

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Classification weight of new born baby at Sayang Ibu maternity Hospital with probabilistic neural network (PNN) 2

Classification weight of new born baby at Sayang Ibu materni...

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2nd International Conference on mathematics, science and Computer science 2018, ICMSC 2018

作者： Yunanda, F.A. Wahyuningsih, S. Nasution, Y.N. Study Program of Statistics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia

The purpose of this research is to classify baby weight before baby was born by considering maternal factors such as maternal weight, maternal height, maternal hemoglobin (Hb), maternal blood pressure, maternal age, total parity and fetal factors such as twin status using PNN (Probabilistic Neural Network). PNN is one of the models in Artificial Neural Networks (ANN) that exists for classification. PNN can be used in classification because it can classify with optimal results, higher accuracy and faster than other neural network models. PNN is structured by four layers, namely the input layer, pattern layer, summation layer and output layer. From classification with PNN on weight of new born baby using 7 factors, we obtained that the best proportion of accuracy classification on weight of new baby born is 90:10 with σ value is 0,2. The accuracy of classification within training data is 100% whereas on testing data is 92%. Based on classification on testing data we obtain eleven data are classified into the same as original class and one data os predicted to the other class. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Classification (of information)

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Application Seasonal Autoregressive Integrated Moving Average to Forecast the Number of East Kalimantan Hotspots

Application Seasonal Autoregressive Integrated Moving Averag...

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Universitas Riau International Conference on science and Environment 2019, URICSE 2019

作者： Wahyuningsih, S. Goejantoro, R. Siringoringo, M. Saputra, A.R. Aminah, S. Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University East Kalimantan Samarinda Indonesia

The objective of this research is to determine the best time series model for forecasting the number of hotspots in East Kalimantan. Seasonal time series model is applied to the data. The results of this research is the best model for the number of hotspots in East Kalimantan is SARIMA (0,1,1)(0,1,1)12. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Generalized Linear Mixed Models by penalized Lasso in modelling the scores of Indonesian students 2

Generalized Linear Mixed Models by penalized Lasso in modell...

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Santi, V.M. Notodiputro, K.A. Sartono, B. Rahyu, W. Statistics Study Program Faculty of Mathematics and Natural Sciences Universitas Negeri Jakarta Jakarta13220 Indonesia Department of Statistics Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia

The Generalized linear mixed model (GLMM) is an extension of the generalized linear model by adding random effects to linear predictors to accommodate clustered or over dispersion. Severe computational problems in the GLMM modelling cause its use restricted for only a few predictors. When many predictors are available, the estimators become very unstable. Therefore, the procedure for selecting relevant variables is essential in modelling. The use of penalty techniques for selecting variables in mixed models is still rarely applied. In this article, the penalized Lasso approach proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously in GLMM. Based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and standard error criteria, it was found that glmmLasso has a better performance than GLMM. For the factors affecting Indonesian's student scores, where glmmLasso produces three significant covariates for the GLMM model while GLMM without penalized Lasso has five covariates, which means that the GLMM model is more complicated than glmmLasso. Gender, school quality based on National Examination (UN) scores and the opportunity for students to investigate to test their ideas are essential covariates as factors that influence the rating of Indonesian students. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Modeling of West Java inflation with Spatial Durbin Model (SDM) 2

Modeling of West Java inflation with Spatial Durbin Model (S...

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Santi, V.M. Djuaraidah, A. Sumertajaya, I.M. Fitrianto, A. Rahyu, W. Statistics Study Program Faculty of Mathematics and Natural Sciences Universitas Negeri Jakarta Jakarta13220 Indonesia Department of Statistics Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia

Inflation in Indonesia, in general, can be caused by a combination of various factors, both in terms of demand, supply, and expectations. The contribution of each element that influences inflation is not always the same from time to time. Gordon (1997) explains the determinants of inflation from these three sides, namely demand, supply, and expectations. The importance of expectations in influencing increase was also stated earlier by Chopra (1985). In this study, spatial Durbin for panel data with fixed effects in modeling inflation in the West Java province. The results show that Minimum Work Wage, percentage of poor population, Gross Regional Domestic Product (GRDP), spatial lag of poor population, and spatial lag of GRDP have a significant influence on the Durbins panel spatial model. There is an adjacent regional effect on inflation in the West Java province based on the value of the lambda obtained. Based on the criteria R-square of 94.29%, it can be concluded that the SDM panel data model has an excellent performance in explaining the diversity of data. The AIC and BIC criteria also show that the SDM panel data model well applied in modeling Regency/City inflation in West Java province. © Published under licence by IOP Publishing Ltd.

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