bluuebunny/crossref_metadata_2025_split huggingface dataset:
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK MENGGUNAKAN GUI MATLAB
Authors: Tarigan Rizki Brendita Br , Yasin Hasbi , Prahutama Alan 
Published: September 2020

Capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment to strengthen its financial position. The recent years, Jakarta Composite Index (JCI) in Capital Market tend to strengthen. JCI data are the time series data obtained from the past to predict the future with caracteristics of JCI data are non stationary and non linier. Neural network is a computational method that imitate the biological neural network. There are several types of methods that can be used in neural network that is: Radial Basis Function Neural Network (RBFNN) Generalized Regression Neural Network (GRNN), dan Probabilistic Neural Network (PNN). Model of Radial Basis Function Neural Network is suitable for time series data. This model has a network architecture in the form of input layer, hidden layer and output layer. This research is done with the help of GUI as a computation tool. The results of analysis by using GUI conducted on the size sample of data as much as 1211 taken as 100 the data thus obtained value of 2315,6 MSE training and training MAPE value of 0,72%, while for the testing of 28886,7 MSE and MAPE testing value is 0,70%. Based on the results of forecasting, JCI values on January 02, 2018 until January 08, 2018 at 6499,922 every day. Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.
PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL
Authors: Alvitiani Siska , Yasin Hasbi , Mukid Mochammad Abdul 
Published: September 2020

Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model
PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT
Authors: Sugesti Annisa , Mukid Moch. Abdul , Tarno Tarno 
Published: September 2020

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.
PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH
Authors: Siswanto Eko , Yasin Hasbi , Sudarno Sudarno 
Published: September 2020

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall
PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019)
Authors: Rinjani Silvia Nur , Hoyyi Abdul , Suparti Suparti 
Published: September 2020

The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%. Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting
PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI KUADRAT TERKECIL PARSIAL PADA INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR
Authors: Sinaga Vetranella .T.R.A. , Safitri Diah , Rahmawati Rita 
Published: September 2020

The multiple regression classic assumptions are used to give linear unbiased and minimum variance estimator. In Human Development Index (HDI) and influencing factors in East Java, there are two variables with VIF more than 10 so the assumption of non-multicollinearity is not fulfilled. Principal component regression (PCR) and partial least squares regression (PLS-R) can solve this problem. By doing principal component analysis, there are two linear combinations to take as the new independent variables which are free from collinearity. In the PLS-R, NIPALS algorithm is used to calculate the components and other structures and to estimate the parameter. While in PCR all independent variables are significant, the percentage of households with drinking water is feasibles is not significant in the model. PLS-R’s is 95,85% is greater than PCR’s = 93,42%. PCR’s PRESS = 81,78 is greater than PLS-R’s PRESS = 61,0595.Keywords: Human Development Index (HDI), Multicollinearity, Principal Component Regression, Partial Least Squares Regression, , PRESS
OPTIMASI REGRESI LOGISTIK MENGGUNAKAN ALGORITMA GENETIKA UNTUK PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGOLONGAN KREDIT BANK (Studi Kasus: Debitur di PT BPR Gunung Lawu Klaten Periode Tahun 2017)
Authors: Wardhani Reno Penggalih Surya , Sudarno Sudarno , Maruddani Di Asih I 
Published: September 2020

Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%. Keywords: credit classification, logistic regression, genetic algorithms
PERAMALAN HARGA CABAI MERAH MENGGUNAKAN MODEL VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (STUDI KASUS: HARGA CABAI MERAH PERIODE JANUARI 2012 SAMPAI DENGAN DESEMBER 2019 DI PROVINSI JAWA BARAT)
Authors: Nisa Aulia Rahmatun , Tarno Tarno , Rusgiyono Agus 
Published: September 2020

Chili is one of the vegetable commodities that has high economic value, because of it’s role is large enough to supply domestic needs as an export commodity in the food industry. The price of red chilliesalways increase in the month of Eid al-Fitr. This is due to the large number of people who use Red Chili as food they consume. Shifting the moon during the Eid al-Fitr will form a seasonal system with different periods, which became known as the Moving Holiday Effect. One of the calendar variation models used to eliminate the Moving Holiday Effect and has a simple processing flow is the RegARIMA model. The RegARIMA model is a combination of linear regression with ARIMA. In the regression model the weighting matrix is used as an independent variable and the price of red chili as the dependent variable. The weight value is obtained based on the number of days that affect Eid, which is 14 days. Based on the analysis the red chili price data in West Java Province with the period of January 2012 to December 2018, the RegARIMA model (1.0,0)(0,1,1) 12 is the best model because it has the smallest AIC. Forecasting results in 2020 showed an increase in the price of red chili in West Java occurred in May to coincide with the Eid al-Fitr holiday which fell on May 24, 2020, the sMAPE value obtained by 24.96%. It means, the forecast still in the level of reasonableness.
Detection of icaAD Gene of Biofilm-Producing Staphylococcus aureus Carriage Isolates Obtained from Health Care Workers and Healthy Communities in Banyumas, Indonesia
Authors: Mahardhika Gembong Satria , Susanti Metta Ayu , Rujito Lantip , Anjarwati Dwi Utami 
Published: September 2020

Background: Asymptomatic biofilm-producing Staphylococcus aureus carriage play a pivotal role as a reservoir pathogen and increase the transmission rate in hospital as well as in healthy community. Biofilm- producing S. aureus which is regulated by the the ica AD gene reduce the antimicrobial ability in eliminating the pathogen. Objective: The aim of this study was to detect the icaAD gene of biofilm-producing Staphylococcus aureus carriage isolates obtained from healthcare workers and healthy Community in Banyumas, Indonesia.Methods: This descriptive observational study enrolled 60 healthcare workers and 60 healthy communities in Banyumas district. Antibiotic susceptibility test was using disc diffusion according to Clinical laboratory Standard Institute (CLSI) 2019. Biofilm-producing ability identified by using microtiter plate biofilm assay and the positivity of icaAD gene was performed by using PCR method.Results: The results showed that one of 60 healthcare workers (0,017%) showed MRSA, four of 60 healthcare workers (0,07%) were MSSA and 2 samples from community (0,03%) were MSSA. Total of 7 samples underwent biofilm examination, one sample was moderate biofilm, two samples were weak biofilm, and four samples were no biofilm. It was known that three biofilm-producing S.aureus were positive ica A/D gene.Conclusion: The ica A/D gene was found positive in both biofilm-producing MRSA and MSSA strain from both healthcare workers group and the healthy communities group. The presence of icaAD genes in both strains shows the potential for antibiotic resistance in these strains regulated by different mechanisms.
A Rare Case of 18-year-old female with Primary Sclerosing Cholangitis, Mixed Connective Tissue Disease, Short Stature
Authors: Siahaan Fransisca Yustika Dewi , Purnomo Hery Djagat , Seno Khristophorus Heri Nugroho Hario , Hellmi Rakhma Yanti 
Published: September 2020

Background: Primary sclerosing cholangitis (PSC) is a rare chronic immune-mediated liver disease affecting intra and extrahepatic bile ducts, in which inflammation and fibrosis are the main factors that lead to biliary stricture. The pathogenesis of this disease is still unclear. It happens approximately 0.5 to 1.3 cases per 100,000 person-years in Northern Europe. PSC is difficult to diagnose until complications arise. PSC can also be a manifestation of mixed connective tissue disease (MCTD) in the liver. A chronic immune disease that has occurred since childhood may result in delayed development and short stature. Case Presentation: A 18-year-old female presented with icteric, anemia, short stature, and absence of menstruation. Laboratory and radiological findings suggested obstruction of intra and extrahepatic biliary duct (conjugated bilirubin 11,80 mg/dL), severe anemia, normal sex hormone levels, increased RNP/SM antibodies. Histopathology showed liver cirrhosis suggestive for primary sclerosing cholangitis. The patient then was given biliary stenting, transfusion of packed red cell, immunosuppresive, corticosteroid, ursodeoxycholic acid. Within a year of treatment, there was a significant decrease of direct bilirubin and improvement in symptoms.Conclusion: Patient with PSC, MCTD, and short stature is a very rare case. This is the first case that has been found in dr. Kariadi Hospital, Semarang. It has high mortality rate and difficult to diagnose and treat. According to multiple guidelines, this patient should have liver transplantation , but in this case, biliary stenting and oral treatments have resulted in an excellent outcome.
HUBUNGAN ANTARA TOTAL BAKTERI DENGAN BAHAN ORGANIK, NO3 DAN H2S PADA LOKASI SEKITAR ECENG GONDOK DAN PERAIRAN TERBUKA DI RAWA PENING
Authors: Purwandari Yusrianti , Purnomo Pujiono Wahyu , Nitisupardjo Mustofa 
Published: September 2020

Fenomena eutrofikasi di perairan Rawa Pening dapat dikatakan telah mencapai tahap akut. Hal ini dapat dilihat dari melimpahnya populasi tanaman Eceng Gondok (Eichhornia crassipes) yang menutupi sebagian kawasan Rawa Pening. Pertumbuhan Eceng Gondok yang tidak terkendali akan mempengaruhi ekosistem danau Rawa Pening.Hal tersebut dapat mengakibatkan terjadinya defisit oksigen (O2) dan terganggunya proses nitrifikasi. Apabila hal ini terjadi secara terus-menerus dapat terjadi potensi bahaya pendangkalan. Proses nitrifikasi sendiri tidak terlepas dari bakteri, proses nitrifikasi merupakan proses yang mengubah ammonia menjadi nitrat dengan bantuan bakteri. Penelitian ini menggunakan metode studi kasus dan metode sampling menggunakan purposive sampling. Lokasi penelitian terdiri dari 2 stasiun yaitu kawasan perairan terbuka dan kawasan tutupan Eceng Gondok. Data yang diukur meliputi total bakteri, bahan organik, NO3, H2S, kedalaman, kecerahan, arus, suhu, pH dan DO. Hasil penelitian menunjukkan bahwa kandungan bahan organik tergolong tinggi. Hal ini berbanding lurus dengan total bakteri di perairan Rawa Pening yang tinggi. Kandungan NO3 pada perairan iniberkisar antara 0,088 – 0,279 mg/l. Sedangkan kandungan H2S berkisar antara 0,009 – 0,015 mg/l.
DISTRIBUSI DAN KELIMPAHAN GASTROPODA PADA HUTAN MANGROVE TELUK AWUR JEPARA
Authors: Silaen Inchan Faolo , Hendrarto Boedi , Nitisupardjo Mustofa 
Published: September 2020

Teluk Awur Jepara merupakan pesisir yang memiliki hutan mangrove yang dijadikan permodelan untuk program rehabilitasi mangrove. Untuk pengelolaan kawasan hutan mangrove secara terpadu perlu diketahui keberadaan biota yang ada di dalamnya. Gastropoda merupakan penghuni tetap hutan mangrove dengan informasi yang masih kurang sehingga perlu dilakukan penelitian tentang kelimpahan dan distribusi gastropoda di dalam hutan mangrove tersebut. Pengambilan sampel menggunakan 3 buah Line Transect sepanjang 100 m dengan jarak 30 m. Line ditempatkan tegak lurus dengan garis pantai. Pengamatan vegetasi mangrove menggunakan metode transek yang berukuran 10x10 m2 yaitu untuk pengamatan tingkat pohon, 5x5 m2 sebanyak dua buah dan 1x1 m2 sebanyak lima buah untuk pengamatan vegetasi mangrove tingkat pancang dan semai. Pengambilan data sampel gastropoda menggunakan kuadran 1x1 m2 sebanyak lima buah yang ditempatkan secara acak pada kuadran 10x10m2. Jenis gastropoda yang ditemukan di hutan mangrove Teluk Awur Jepara didapat 16 jenis yaitu Cerithidea cingulata, Cerithidea cingulata cingulata, Cerithidea quadrata, Cerithidea obtusa, Litorina carinifera, Littorina angulifera, Littorina scabra, Casidula nucleus, Casidula aurisfelis, Casidula multiflicata, Melampus nuxcastaneus, Melampus coffeus, Telescopium telescopium, Sphaerassiminea miniata, Neritina violacea dan Pythia plicata. Vegetasi mangrove yang paling mendominasi adalah Rhizophora mucronata baik pada tingkat pohon, pancang, dan semai. Jenis gastropoda yang paling melimpah dan mendominasi adalah Cerithidea cingulata dan Casidula nucleus. Cerithidea cingulata lebih mendominasi pada daerah mangrove terbuka sedangkan Casidula nucleus mendominasi pada daerah mangrove tertutup yaitu pada daerah mangrove yang lebih rapat. Distribusi gastropoda pada umumnya mengelompok. Keberadaan gastropoda pada hutan mangrove dipengaruhi oleh vegetasi hutan mangrove.
ANALISIS KELIMPAHAN PERIFITON PADA KERAPATAN LAMUN YANG BERBEDA DI PERAIRAN PULAU PANJANG, JEPARA
Authors: Novianti Merlyna , Suprapto Djoko , Widyorini Niniek 
Published: September 2020

Perifiton merupakan jasad – jasad yang dapat hidup melekat pada permukaan daun lamundengan demikian penelitian ini ditekankan untuk mengetahui struktur komunitas perifiton pada komunitas lamun serta membedakannya pada setiap jenis lamun. Hasil penelitian ini diharapkan dapat memberikan gambaran tentang keberadaan perifiton pada komunitas lamun dalam menunjang fungsi-fungsinya di perairan Pulau Panjang, Jepara sebagai areal konservasi. Kegiatan penelitian dilaksanakan pada antara bulan Juni - Juli 2012 selama 1 bulan di pantai perairan Pulau Panjang, Jepara, Jawa tengah. Proses mengidentifikasi diadakan di laboratorium Fakultas Perikanan dan Kelautan. Metode yang digunakan adalah pengambilan sampel lamun dan perifiton dengan menentukkan tiga titik pengambilan sampel lamun dengan kepadatan jarang, sedang dan padat serta pengukuran . Jenis Perifiton yang paling banyak ditemui di pada perairan Pulau Panjang pada rata-rata kerapatan berasal dari kelas Bacillariophyceae yaitu berkisar antara 63.134-98.910 ind/cm2, perifiton dengan kelimpahan relatif tertinggi adalah Nizchia sp berkisar 32,26-34,18 % Persen penutupannya pada kerapatan sedang yaitu 39,08%, pada kerapatan padat yaitu 45,01% dan pada kerapatan jarang yaitu 15,89%. Pada sampel daun lamun yang diambil secara acak, ditemukan 16 spesies dengan indeks keanekaragaman (H’) perifiton pada kerapatan jarang mempunyai nilai indekskeanekaragaman sebesar 2,35 dengani keseragamannya sebesar 0,84 kerapatan sedang sebesar 2,39 dan keseragaman (e) sebesar 0,86 dan perifiton pada kerapatan padat mempunyai nilai indeks keanekaragaman 2,45 dan keseragaman 0,87.
ANALISIS PERBEDAAN MORFOLOGI DAN KELIMPAHAN KARANG PADA DAERAH RATAAN TERUMBU (Reef Flate) DENGAN DAERAH TUBIR (Reef Slope) DI PULAU PANJANG, JEPARA
Authors: Septyadi Kiai Agoes , Ruswahyuni - , Widyorini Niniek 
Published: September 2020

Pulau Panjang merupakan kawasan wisata di Jepara yang memiliki potensi salah satunya adalah ekosistem terumbu karang. Ekosistem terumbu karang mempunyai fungsi ekologis yaitu sebagai habitat untuk berkembang biak, mencari makan dan berlindung bagi biota lain. Terumbu karang juga mempunyai fungsi melindungi pantai dari hempasan ombak dan arus laut. Selain itu keindahan alam laut dan terumbu karang sendiri dapat meningkatkan taraf hidup masyarakat sebagai daerah pariwisata bahari.Tujuan dari penelitian ini adalah untuk mengetahui perbedaan morfologi karang pada zona rataan (reef flat) dengan terumbu karang daerah tubir (reef slope) di perairan pulau Panjang, Jepara. Dan mengetahui kelimpahan terumbu karang pada zona rataan (reef flat) dan terumbu karang pada zona tubir (reef slope) di perairan pulau Panjang, jepara. Metode yang digunakan dalam penelitian ini adalah metode survei. Dimana metode yang digunakan tergolong dalam metode survei yang bersifat deskriptif. Metode yang digunakan pada pengambilan data adalah Line Intercept Transect (LIT). Penelitian dilakukan pada dua lokasi yaitu daerah rataan terumbu dan daerah tubir. Panjang line transek adalah 10 m, di letakkan sejajar garis pantai, transek yang digunakan di daerah rataan terumbu sebanyak 3 line dan daerah tubir sebanyak 3 line. Jarak antara line dimasing-masing lokasi sampling 5 m.Hasil penelitian jenis karang yang ditemukan di rataan terumbu dan tubir yaitu adalah jenis Porites sp., Acropora sp., Echinopora sp., Turbinaria sp., Goniastrea sp., Pavona sp., Favites sp., Leptoseries sp., Pectinia sp., dan Goniopora sp. Prosentase penutupan karang hidup tertinggi di rataan terumbu yaitu jenis Porites sp. 26,73% , Goniastrea sp. 4,10% dan Acropora sp 3,67%. Sedangkan presentase penutupan karang hidup tertinggi di tubir yaitu jenis Porites sp. 35,67%, Echinopora sp. 6,50% dan Acropora sp. 6,33%. Nilai prosentase penutupan karang hidup di daerah rataan terumbu sebesar 36,40% yang termasuk dalam kategori sedang. Sedangkan nilai prosentase penutupan karang hidup di tubir sebesar 65,50% termasuk kategori baik. Pada rataan terdapat jenis morfologi Acropora yaitu jenis Acropora Branching (ACB) sebesar 3,67%, sedangkan pada tubir sebesar 6,33%. Jenis morfologi yaitu jenis Acropora terdapat satu jenis yaitu Acropora Branching (ACB), Baik daerah rataan dan tubir. Sedangkan untuk non Acropora yang paling banyak adalah jenis Coral Encrusting (CE) baik pada rataan dan tubir.