publication_id,judul_publikasi,abstrak,tahun,dosen,dosen_list,jumlah_dosen,abstract_word_count,abstract_unique_word_count,abstract_unique_ratio,abstract_max_token_ratio,abstract_quality_reason,embedding_index,topic_id,topic_id_raw,lang,is_noise a77a0b457a7173a3,(Strong) rainbow connection on the splitting of 3-path,"The rainbow connection number of a graph G, denoted rc(G), is the smallest number of colors needed to color the edges of G so that any two vertices are connected by a path whose edges all have different colors. Similarly we define the strong rainbow connection number of G, denoted by src(G), by replacing “path” with “geodesic”. n this paper, we study the rc and src of a very specific construction known as splitting. For any graph H and any m ∈ ℕ, its m-splitting is a new graph denoted by Splm (G) constructed as follows. Suppose V(H) = {h1, …, hn}. Then for each hi we introduce m new vertices and we join each new vertex to all neighbors of the original vertex hi in H. In this paper we determine the rc and src of Splm (P3) for all m ∈ ℕ, where P3 is the 3-path, i.e. path with three vertices.",2017,Fendy Septyanto,"[""Fendy Septyanto""]",1,152,78,0.5131578947368421,0.05263157894736842,keep,0,9,9,en,False 0327343ecf2ba282,25 years development of knowledge graph theory: the results and the challenge,"The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system. By the end of the 80's expert systems in medical and social science were developed successfully using knowledge graph theory. In the following stage, the goal of the project was broadened to represent natural language by knowledge graphs. Since then, this theory can be considered as one of the methods to deal with natural language processing. At the present time knowledge graph representation has been proven to be a method that is language independent. The theory can be applied to represent almost any characteristic feature in various languages. The objective of the paper is to summarize the results of 25 years of development of knowledge graph theory and to point out some challenges to be dealt with in the next stage of the development of the theory. The paper will give some highlight on the difference between this theory and other theories like that of conceptual graphs which has been developed and presented by Sowa in 1984 and other theories like that of formal concept analysis by Wille or semantic networks.",2008,Sri Nurdiati,"[""Sri Nurdiati""]",1,201,102,0.5074626865671642,0.09950248756218906,keep,1,0,-1,en,False a70f90f1400ef625,A Analisis Perbandingan Kinerja Metode Ensemble Bagging dan Boosting pada Klasifikasi Bantuan Subsidi Listrik di Kabupaten/Kota Bogor,"The classification of electricity subsidy recipients is an crucial step to ensure that the government's social assistance program is distributed in a targeted manner, so an appropriate analysis method is needed. This research compares the Bagging and Boosting ensemble methods for the classification of households receiving electricity subsidies in Bogor Regency and City using Susenas 2023 data totaling 2002 households. The bagging method uses Random Forest and Extra Trees, while boosting includes CatBoost and LightGBM. The results showed that the Extra Trees method of bagging provided the best performance with 91% accuracy, 95% F1score, and 97% sensitivity. Factors such as ownership of electronic goods and modern facilities, such as ownership of air conditioners, laptops, and televisions are the most significant variables in influencing the classification of electricity subsidy recipients. With high accuracy and minimal bias, this model effectively supports data-driven policies for electricity subsidy distribution. This research is expected to be a strategic recommendation for the government to improve the effectiveness of the electricity subsidy program to be more efficient, well-targeted, and support the improvement of people's welfare.",2024,Aulia Rizki Firdawanti,"[""Aulia Rizki Firdawanti""]",1,178,109,0.6123595505617978,0.07865168539325842,keep,2,4,4,en,False 15dd36c697641244,"A Bayesian conditional autoregressive with INLA: A case study of tuberculosis in Java, Indonesia","Most disease risk mapping models are modeled by using the Bayesian approach. In this approach, the posterior distribution is typically estimated by using Markov Chain Monte Carlo (MCMC) method. However, it is known that the MCMC method suffers from convergence issues and requires a high demand for computational resources. This study aims to identify potential covariates in tuberculosis reduction and to map the relative risk of tuberculosis disease in the Java region by using Bayesian Conditional Autoregressive (CAR) with Integrated Nested Laplace Approximation (INLA). The posterior distribution of Bayes was estimated analytically using INLA that resulting in a more efficient, fast, and accurate inference. The results showed that Besag York Mollié (BYM) has the smallest Deviance Information Criterion (DIC) and Mean Absolute Deviance (MAD), so this model gives the best prediction. The significant covariates in reducing the number of TB cases in Java are percentages of healthy homes, household clean and healthy behavior, non-smokers, and complete tuberculosis treatment. In addition, 52.1% of districts/cities in Java were found to have a relative risk greater than one, and most of them were located in West Java.",2022,Rahma Anisa,"[""Rahma Anisa""]",1,186,120,0.6451612903225806,0.05913978494623656,keep,3,6,6,en,False 08702d1b1fa110f8,"A Comparative Study of GARCH, LSTM, and Hybrid GARCH-LSTM Models for Analyzing the Dynamical Pattern of Stock Price Volatility","Most of the statistical methods used in time series analytics assume that the data properties be stationary. In real cases, this assumption may be violated. The stock price is one of the highly volatile data or has a time-dependent variance. GARCH model is a model that overcomes the limitation of the stationary assumption on most statistical methods. In addition, artificial intelligence methods are widely used and have promising performance. This study aims to examine the performance of the GARCH, LSTM, and hybrid GARCH–LSTM models in analyzing the dynamical pattern of volatility data on simulation and empirical data. This study uses two kinds of data, simulation and empirical data. The simulation data are obtained from the simulation process by generating data from the GARCH model with ten scenarios. The scenarios are the combination of two factors: (1) variance with two levels, high and low; (2) dataset with five levels, GARCH (1,1), GARCH (1,3), GARCH (3,1), GARCH (3,3), and Contaminated GARCH. The design for the simulation study is the randomized complete block factorial design with three factors (i.e., variance, dataset, and model), and the response is the MAPE value. The effects of the factors on the MAPE values are analyzed using ANOVA and Tukey's multiple comparisons. Meanwhile, the stock closing prices of PT Bumi Resources Minerals Tbk. in 2021 are used as the empirical study. For the modeling process in empirical study, data are transformed into returns and then modeled using three proposed models. Seven days ahead, predictions are carried out after the model validation process. The ANOVA results show that …",2022,Laily Nissa Atul Mualifah,"[""Laily Nissa Atul Mualifah""]",1,265,129,0.4867924528301887,0.1018867924528302,keep,4,2,2,en,False 3f1648cc24d2d737,A FAST COMPUTATION FOR EIGENVALUES OF CIRCULANT MATRICES WITH ARITHMETIC SEQUENCE,"In this article, we derive simple formulations of the eigenvalues, determinants, and also the inverse of circulant matrices whose entries in the first row form an arithmetic sequence. The formulation of the determinant and inverse is based on elementary row and column operations transforming the matrix to an equivalent diagonal matrix so that the formulation is obtained easily. Meanwhile, for the eigenvalues formulation, we simplify the known result of formulation for the general circulant matrices by exploiting the properties of the cyclic group induced by the set of all roots of as the set of points in the unit circle in the complex plane, and also by considering the specific property of arithmetic sequence. Then, we construct an algorithm for the eigenvalues formulation. This algorithm shows a better computation compared to the previously known result for the general case of circulant matrices.",2023,Teduh Wulandari Mas’oed,"[""Teduh Wulandari Mas’oed""]",1,142,75,0.528169014084507,0.14084507042253522,keep,5,9,9,en,False 7719b48a3dcfe7ee,A Hybrid Decision Tree and K-Means Approach for Classifying Community Happiness in Bogor Regency,"Happiness is one of the key indicators for measuring the quality of life in a community. This study aims to classify the level of happiness among residents of Bogor Regency using a hybrid approach that combines Decision Trees and K-means. The research procedure consisted of data preprocessing, clustering using K-Means to form preliminary groups, and further classification through a Decision Tree to interpret the determinants of happiness. The analysis revealed that the residents of Bogor Regency can be categorized into two groups: those who are fairly happy and those who are less happy. The hybrid model achieved its best performance with a balanced accuracy of 84%, an F1-Score of 37%, and a Kappa score of 28%. Socioeconomic factors, such as marital status, family status, occupation, and the number of cigarettes smoked, were identified as the primary determinants influencing happiness levels. The …",2025,Aulia Rizki Firdawanti,"[""Aulia Rizki Firdawanti""]",1,141,92,0.6524822695035462,0.07801418439716312,keep,6,10,10,en,False 6cb0c2cdb1152b4a,A Lightweight CNN for Multi-Class Classification of Handwritten Digits and Mathematical Symbols.,"Recognizing handwritten digits and mathematical symbols remains a nontrivial challenge due to handwriting variability and visual similarity among classes. While deep learning, particularly Convolutional Neural Networks (CNNs), has significantly advanced handwriting recognition, many existing solutions rely on deep, resource-intensive architectures. This study aims to develop a lightweight and efficient CNN model for multi-class classification of handwritten digits and mathematical symbols, with an emphasis on deployability in resourceconstrained environments such as educational platforms and embedded systems. The proposed model, implemented in Julia using the Flux. jl library, features a compact architecture with only two convolutional layers and approximately 55,000 trainable parameters significantly smaller than typical deep CNNs. Trained and evaluated on a publicly available dataset of over 10,000 …",2025,Elis Khatizah,"[""Elis Khatizah""]",1,119,91,0.7647058823529411,0.058823529411764705,keep,7,3,3,en,False 7086fc23b830102d,A SIR mathematical model of dengue transmission and its simulation,"The Mathematical model that was developed is a SIR model human-mosquito-mosquito eggs, the rate of displacement of latent mosquitoes become infected mosquito was assumed constant and non-infected eggs were produced by infected mosquitoes and susceptible mosquitoes, while infected eggs were produced by infected mosquitoes. In addition, the temperature factor used in producing susceptible mosquitoes and infected mosquitoes from eggs. The analysis shows two equilibrium state, disease-free equilibrium and endemic equilibrium. The simulation was conducted to show dynamic population where R o< 1 and R o> 1. The result shows the disease-free equilibrium which is stable when R o< 1 and the endemic equilibrium which is stable when R o> 1. This also shows mosquito mortality rate towards the desease in population. If mosquito mortality rate is increased, the basic reproduction number is decreasing, so it can prevent spread in population.",2014,Paian Sianturi,"[""Paian Sianturi""]",1,141,72,0.5106382978723404,0.07092198581560284,keep,8,7,7,en,False 5c38eb84f887a0b9,A Stochastic Spatial Model of the Spread of Dengue Hemorrhagic Fever,"The dengue virus is spread the Aedes aegypti mosquito that can breed well in the area of human settlements. The dengue virus has the ability to transmit vertically, from mosquitoes to their eggs. This research applies stochastic model study the spread of dengue virus. The purpose of this research were: to modify the Otero and Solari models, to determine the transition probability of the modified model, calculate the probability of disease outbreaks and to carry out computer simulate of the modified model. This simmulation was used to know the effect of temperature and availability the breeding sites of mosquitos on the spread of dengue virus. The results of this research had been constructed the modification of Otero and Solari models by adding four new subpopulations ie infected eggs, infected larvae, infected pupae and infected first adult mosquito, in which the transition probability of this model was analyzed by …",2017,Paian Sianturi,"[""Paian Sianturi""]",1,148,79,0.5337837837837838,0.12162162162162163,keep,9,7,7,en,False 43cabcbab4dfaf63,A Survey of Digital Nudges for Fostering Responsible Consumption Behavior in Indonesia Digital Services,"Digital nudging is a promising method to promote responsible consumption habits in Indonesian society. This research aims to address a significant gap in knowledge about the adaptation and successful use of nudging techniques. This survey covers 15 of Indonesia’s most commonly used digital services, such as super applications, transport and navigation, shopping, food and drink, and grocery. The study identifies 16 digital nudges in these services, showing a notable trend in environmental efforts in the super app. These digital services include carbon offsetting, electric vehicle usage, recycling services, and reducing single-use cutlery. The study examines social responsibility methods on different platforms, such as promoting paperless transactions, environmental initiatives, and encouraging donations for social causes. The survey suggests incorporating digital nudges in transport, navigation services …",2024,Ahmad Ridha,"[""Ahmad Ridha""]",1,125,86,0.688,0.048,keep,10,15,15,en,False 95b701969069ed17,A Time-Delay Cascading Neural Network Architecture For Modeling Time-Dependent Predictor In Onset Prediction,"The occurrence of rain before the real start of a rainy season often mislead farmers into thinking that rainy season has started and suggesting them to start planting immediately. In reality, rainy season has not started yet, causing the already-planted rice seed to experience dehydration. Therefore, a model that can predict the onset of rainy season is required, so that draught disaster can be avoided. This study presents Time Delay-Cascading Neural Network (TD-CNN) which deals with situations where the response variable is determined by a number of time-dependent inter-related predictors. The proposed model is used to predict the onset in Pacitan District Indonesia based on Southern Oscillation Index (SOI). The Leave One Out (LOO) cross-validation with series data 1982-2012 are used in order to compare the accuracy of the proposed model with the Back-Propagation Neural Network (BPNN) and Cascading …",2014,Mushthofa,"[""Mushthofa""]",1,139,93,0.6690647482014388,0.07913669064748201,keep,11,2,2,en,False 4ee5c04da701c3c0,A bootstrap simulation for comparison of group risk plan and multi-peril crop insurance policy,"Agricultural insurance is one of the solutions for farmers to avoid risks such as climate change, pest, disease and price fall which increase the risk of crop failure. Indonesian Ministry of Agriculture (MoA) and Jasindo have been using subsidized Multi-Perils Crop Insurance (MPCI) and faced some disadvantages that mainly due to high risk moral hazard, adverse selection, and high administrative costs. Our research provides an alternative policy area yield index insurance known as Group Risk Plan (GRP) to handle this problem. To find homogeneous basis risk in GRP, the data was classified into two categories: up to and above two hectares. Bootstrapping approach was used to find MPCI and GRP indemnity amount for each term of harvesting period. In addition, both parametric estimation and goodness of fit test indicated appropriate distribution used by MPCI and GRP in each term of harvesting period as well. To …",2019,Rahma Anisa,"[""Rahma Anisa""]",1,145,100,0.6896551724137931,0.05517241379310345,keep,12,14,14,en,False 8b8f906a8cbb9283,A classification system for jamu efficacy based on formula using support vector machine and k-means algorithm as a feature selection,"Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research that using SVM method without feature selection. This study used variances to evaluate the results of clustering. The total of 3138 data herbs and 465 plant species were grouped into 100 clusters with the variance of 0.0094. The managed group succesfully reduced the data dimension into 3047 of jamu sample and 236 species of herbs and plants as features. The result of SVM classification using feature selection yielded the accuracy of 71.5%.",2015,Aziz Kustiyo,"[""Aziz Kustiyo""]",1,139,85,0.6115107913669064,0.08633093525179857,keep,13,0,0,en,False 23c8f8b0bc6499f4,A comparative study of CatBoost and double random forest for multi-class classification,"Multi-class classification has its challenge compared to binary classification. The challenges mainly caused by the interactions between explanatory and responses variable are increasingly complex. Ensemble-based methods such as boosting and random forest (RF) have been proven to handle classification problems. We conducted this research to study multi-class classification using CatBoost, a method developed with gradient boosting and double random forest (DRF), RF’s development that is good to be used when the resulting RF model is underfitting. Analysis was carried out using simulation and empirical data. In the simulation study, we generate data based on the distance between classes: high, medium, and low. The empirical data used is the industrial classification code, namely KBLI. CatBoost and DRF can rightly solve the multi-class classification problem at a high distance, measured by a 100 …",2023,Khairil Anwar Notodiputro,"[""Khairil Anwar Notodiputro""]",1,132,87,0.6590909090909091,0.06060606060606061,keep,14,4,4,en,False 8e32c339be162761,A comparative study of approximation methods for maximum likelihood estimation in generalized linear mixed models (GLMM),"Maximum likelihood estimates in GLMM are often difficult to be obtained since the calculation involves high dimensional integrals. It is not easy to find analytical solutions for the integral so that the approximation approach is needed. In this paper, we discuss several approximation methods to solve high dimension integrals including the Laplace, Penalized Quasi likelihood (PQL) and Adaptive Gaussian Quadrature (AGQ) approximations. The performance of these methods was evaluated through simulation studies. The ‘true’ parameter in the simulation was set to be similar with parameter estimates obtained by analyzing a real data, particularly salamander data (McCullagh & Nelder, 1989). The simulation results showed that the Laplace approximation produced better estimates when compared to PQL and AGQ approximations in terms of their relative biases and mean square errors.",2017,Anang Kurnia; Khairil Anwar Notodiputro; Kusman Sadik,"[""Anang Kurnia"", ""Khairil Anwar Notodiputro"", ""Kusman Sadik""]",3,126,87,0.6904761904761905,0.07142857142857142,keep,15,6,-1,en,False 634875d593f6567f,A comparative study of linear and nonlinear regression models for outlier detection,"Artificial Neural Networks provide models for a large class of natural and artificial phenomena that are difficult to handle using classical parametric techniques. They offer a potential solution to fit all the data, including any outliers, instead of removing them. This paper compares the predictive performance of linear and nonlinear models in outlier detection. The best-subsets regression algorithm for the selection of minimum variables in a linear regression model is used by removing predictors that are irrelevant to the task to be learned. Then, the ANN is trained by the Multi-Layer Perceptron to improve the classification and prediction of the linear model based on standard nonlinear functions which are inherent in ANNs. Comparison of linear and nonlinear models was carried out by analyzing the Receiver Operating Characteristic curves in terms of accuracy and misclassification rates for linear and nonlinear models …",2016,Anwar Fitrianto,"[""Anwar Fitrianto""]",1,140,89,0.6357142857142857,0.07142857142857142,keep,16,17,17,en,False 142be7386fa2cc7c,A comparison of artificial neural network and naive Bayes classification using unbalanced data handling,"Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test …",2023,Erfiani,"[""Erfiani""]",1,136,90,0.6617647058823529,0.08088235294117647,keep,17,4,4,en,False c452ee1ed35f1110,A comparison of fixed effect and mixed effect models in analyzing telecommunication products,"Mixed model is a model that combines fixed factors and random factors while fixed model is a model that only contains fixed factors. Observations made over time with the same object being observed are called repeated measurement. This research was conducted to determine the determinant factors of internet data quota sales which are influenced by SA (Sales Area), MC (Mutual Check), PC (Product Category), and time factors using a nested linear mixed model with repeated measurement and fixed model with repeated measurement. SA, PC, and time factors as fixed factors while the MC factor nested in SA as a random factor. The results showed that in nested linear mixed model with repeated measurement, the interaction effect between three fixed factors, namely between SA, PC, and time have a significant effect on the sales volume of internet data quota. In fixed model the analysis used the average value of …",2022,La Ode Abdul Rahman,"[""La Ode Abdul Rahman""]",1,148,71,0.4797297297297297,0.05405405405405406,keep,18,15,15,en,False c2352d4d8a63fc7c,A computation method for finding Boolean Gröbner basis using divide and conquer algorithm,"A Gröbner basis is a basis in a ring of multivariate nonlinear polynomials over a field of numbers. The basis has certain properties that allow simple algorithmic solutions for many fundamental problems in mathematics, and natural and technical sciences. In this paper, we propose an algorithm for computing Gröbner basis in binary multivariate polynomial ring 𝔽2 [x1, x2, …, xn] and for which the main goal is to develop a computation method for solving binary multivariate nonlinear systems. Here, we apply the well-known divide and conquer recurrence as the fundamental idea of the method in which case the usual Buchberger algorithm is applied only at the end of the combining phase. For data representation, we present a new approach in which every single polynomial in the ring can be considered to be a boolean object. So, in a symbolic computation perspective, the polynomial can be represented as a member …",2024,Teduh Wulandari Mas’oed,"[""Teduh Wulandari Mas’oed""]",1,148,87,0.5878378378378378,0.06756756756756757,keep,19,9,9,en,False fe07455a1a7d9301,A computation perspective for the eigenvalues of circulant matrices involving geometric progression,"In this article, the eigenvalues and inverse of circulant matrices with entries in the first row having the form of a geometric sequence are formulated explicitly in a simple form in one theorem. The method for deriving the formulation of the determinant and inverse is simply using elementary row or column operations. For the eigenvalues, the known formulation of the previous results is simplified by considering the specialty of the sequence and using cyclic group properties of unit circles in the complex plane. Then, the algorithm of eigenvalues formulation is constructed, and it shows as a better computation method.",2023,Teduh Wulandari Mas’oed,"[""Teduh Wulandari Mas’oed""]",1,99,59,0.5959595959595959,0.13131313131313133,keep,20,9,9,en,False b5c389856c93d234,A computer aided system for tropical leaf medicinal plant identification,"The objective of this paper is to develop a computer aided system for leaf medicinal plant identification using Probabilistic Neural Network. In Indonesia only 20-22% of medicinal plants have been cultivated. Generally, identification process of medicinal plants has been done manually by a herbarium taxonomist using guidebook of taxonomy/dendrology. This system is designed to help taxonomist to identify leaf medicinal plant automatically using a computer-aided system. This system uses three features of leaf to identify the medicinal plant, ie, morphology, shape, and texture. Leaf is used in this system for identification because easily to find. To classify medicinal plant we used Probabilistic Neural Network. The features will be combined using Product Decision Rule (PDR). The system was tested on 30 species medicinal plant from Garden of Biopharmaca Research Center and Greenhouse Center of Ex-situ Conservation of Medicinal Indonesian Tropical Forest Plants, Faculty of Forestry, Bogor Agriculture University, Indonesia. Experiment results showed that the accuracy of medicinal plant identification using combination of leaf features increase until 74, 67%. The comparative analysis of leaf features has been performed statistically. It showed that shape is a dominant features for plant identification. This system is very promising to help people identify medicinal plant automatically and for conservation and utilization of medicinal plants.",2013,Yeni Herdiyeni,"[""Yeni Herdiyeni""]",1,209,107,0.5119617224880383,0.06220095693779904,keep,21,3,3,en,False 8a94cfceac933a93,A computer vision for rice disease identification to support integrated pest management,"Rice is the main source of carbohydrates consumed by the Indonesian population. According to Badan Pusat Statistik, the average amount per capita rice consumption reached 113.5 kilograms in 2011. On the other hand, the population of Indonesia, which showed an increasing trend over the last decade, demands increased national rice production. In addition to quantity, the quality of rice, which is the most important factor in the consumption of rice, must be maintained. One way to achieve this is to manage rice diseases. In using the Integrated Pest Management (IPM) approach, the first step is to properly diagnose the rice diseases so that the correct treatments can be taken up as soon as possible. However, in certain regions, experts may not be available to correctly identify the diseases. Computer vision can effectively solve this problem by rapidly identifying rice diseases through the employment of image …",2014,Yeni Herdiyeni,"[""Yeni Herdiyeni""]",1,146,97,0.6643835616438356,0.10273972602739725,keep,22,3,3,en,False 3280c6a622b026ca,A decision tree based on spatial relationships for predicting hotspots in peatlands,"Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention. This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points. The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%. The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4. 5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4. 5 decision tree reaches 65.24%.",2014,Imas Sukaesih Sitanggang,"[""Imas Sukaesih Sitanggang""]",1,155,93,0.6,0.07741935483870968,keep,23,1,1,en,False a314ba7fa1e93161,"A fast computation for the determinant, inverse, and eigenvalues of skew circulant matrices involving Fibonacci numbers","In this article, the determinant, inverse, and eigenvalues of skew circulant matrices with entries in the first row having the formation of Fibonacci sequence are formulated explicitly in a simple form in one theorem. The method for deriving the formulation of the determinant and inverse is simply using traditional elementary row or column operations which is directed to to get a simpler equivalent matrix. For the eigenvalues, the known formulation from the case of general skew circulant matrices is simplified by considering the speciality of the sequence and using cyclic group properties of unit circles in the complex plane. Then, the algorithms of those formulations are constructed and they perform as a fast computation.",2024,Teduh Wulandari Mas’oed,"[""Teduh Wulandari Mas’oed""]",1,114,72,0.631578947368421,0.11403508771929824,keep,24,9,9,en,False 787403814d5cb586,A finite-valued solver for disjunctive fuzzy answer set programs,"Fuzzy Answer Set Programming (FASP) is a declarative programming paradigm which extends the flexibility and expressiveness of classical Answer Set Programming (ASP), with the aim of modeling continuous application domains. In contrast to the availability of efficient ASP solvers, there have been few attempts at implementing FASP solvers. In this paper, we propose an implementation of FASP based on a reduction to classical ASP. We also develop a prototype implementation of this method. To the best of our knowledge, this is the first solver for disjunctive FASP programs. Moreover, we experimentally show that our solver performs well in comparison to an existing solver (under reasonable assumptions) for the more restrictive class of normal FASP programs.",2014,Mushthofa,"[""Mushthofa""]",1,115,73,0.6347826086956522,0.06086956521739131,keep,25,5,-1,en,False 61f765f7a4f4db2f,A framework for blockchain and internet of things integration in improving food security in the food supply chain,"Food security, as defined by the United Nations' Committee on World Food Security, means that “all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their food preferences and dietary needs for an active and healthy life”. Food security is not just ensuring food sufficiency, but also ensuring the quality and the reliability of supply chains which have become more complex with diversified sources of farm productions. Some common issues in food supply chains are lacking efficient traceability of product origins and distributions. Blockchain is an emerging technology for the agricultural supply chain which could monitor food quality and safety by digitally tracking data and processes of material resources. Meanwhile, the Internet of Things (IoT) system is used for diagnosis and control in smart agriculture such as crop growth observation that can produce good quality and sustainable farm production. This work proposes a framework of the integration of blockchain with IoT to improve the reliability of food supply chain (FSC) by focusing on optimizing the consensus algorithm of blockchain. The expected outcome is to produce an efficient traceability and higher quality transactions which can integrate all procedures and dealings in real-time processes across the FSC.",2024,Irman Hermadi,"[""Irman Hermadi""]",1,204,123,0.6029411764705882,0.058823529411764705,keep,26,11,-1,en,False eec0fb11e808169d,A fuzzy based decision support system for evaluating land suitability and selecting crops,"Evaluating land suitability and selecung crops tn modem agriculture is of critical importance to every organizalion. This is because the narrower area of land, the more effectiveness in planting 1s required in accordance with the desires of the land. Process of evalunting land suitability class and selecting plants in accordance with decision marker's requirements is complex and unstructured. Approach This study presented a fuzzy-based Decision Support System (DSS) for evaluating land suitability and selecting crops to be planted. A fuzzy rules was developed for evaluating land suitability and selecting lhe appropriate crops to be planted considering the decision maker's requirements in crops selection with the efficient use of the powerful reasoning and explanation capabilities of DSS. The idea of letting the problem to be solved determines the method to be used was incorporated into the DSS development. Results As a resull, effective decisions can be made for land suilabilily evaluation and crop selecuon problem An example was presented to demonstrate the applicability of the proposed DSS for solving the problem of evaluating land suitability nnd selecting crops in real world situations. Conclusion Fuzzy based model can represent and manipulate agriculture knowledge that 1s incomplete or vague and ll can be used lo determine land limitation raung. The rating value wa~ use