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All Journal Bioma : Berkala Ilmiah Biologi Media Statistika SAINS DAN MATEMATIKA Jurnal Gaussian E-Dimas: Jurnal Pengabdian kepada Masyarakat

Moch. Abdul Mukid

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 324112

Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Education Mathematics Social Sciences Other

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Makrobenthos Sebagai Indikator Tingkat Kesuburan Tambak Di Pantai Utara Jawa Tengah Muhammad, Fuad; Izzati, Munifatul; Mukid, Moch. Abdul
Bioma : Berkala Ilmiah Biologi Vol. 19, No. 1, Tahun 2017
Publisher : Departemen Biologi, Fakultas Sains dan Matematika, Universitas Diponegoro

Makrobenthos that live in the mangrove forest can be used to predict the role or contribution of mangrove ecosystems as a source of natural food for the environment. The aims of research to determine the structure, composition, abundance, diversity makrobenthos of the mangrove ecosystem. The study was conducted by comparing the community structure makrobenthos in pond ecosystem with mangrove vegetation constituent . The location of this study include three areas , Mangunharjo (Semarang), Surodadi (Demak) andÂ Pasarbangi (Rembang). The result can shows makrobentos species composition is dominated by gastropods ( 18 species) , Bivalvia ( 13 species ) , Polychaeta ( 3 types ) , and crustaceans ( 2 types ) . Cerithium and Littorina scabra is a type that has a high density of the mangrove ecosystem . There are differences in the abundance and diversity of plankton and makrobenthos at three study sites. In general Pasarbangi Coast has the highest abundance and diversity . Macrozoobenthos community structure in mangrove ecosystems that exist in the three study sites in a stable state , species diversity and distribution of the number of individuals of each type of uniform . Pasarbangi area with mangrove vegetation polyculture farms , have high primary productivity . This shows the level of primer productivity at the site is also high .Â Keywords: community structure, macrobenthos, pond ecosystem

ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS Wilandari, Yuciana; Mukid, Moch. Abdul; Megawati, Nurhikmah; Sutarno, Yulia Agnis
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the fundamental problems that always exist in a regions in Indonesia is the problem of poverty. Various poverty reduction efforts initiated by the Central Government and the Regions is now experiencing growth and significant shifts in accordance with the direction and context of poverty reduction targets. To overcome poverty, one of the things done by the Central Java provincial government is to help livestock. Livestock types cultivated in Central Java, is a large livestock, namely cattle (beef / dairy), buffalo and horses, while small livestock consists of goats, sheep and pigs. For that conducted the study to classifyÂ cities in Central Java into groups based on livestock population. The grouping using fuzzy cluster analysis means. From this study showed that of the three kinds of clusters obtained many tried to do the most accurate cluster is 3 clusters with Xie-Beni index 0,3279177, with cluster 1 are 20 city, cluster 2 are 12 City and cluster 3 there are 3 City.Â Keywords: Classification, Fuzzy Cluster Means, Livestock

BAGGING CLASSIFICATION TREES UNTUK PREDIKSI RISIKO PREEKLAMPSIA (Studi Kasus : Ibu Hamil Kategori Penerima Jampersal di RSUD Dr. Moewardi Surakarta) Mukid, Moch. Abdul; Wuryandari, Triastuti; Ratnaningrum, Desy; Sri Rahayu, Restu
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Preeclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy. Classification Trees is a statistical method that can be used to identify potency of expectant women suffering from preeclampsia. This research aim to predict the risk of preeclampsia based on some individual variables. They are parity, work status, history of hypertension of preeclampsia, body mass index, education and income. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification Trees method. By the method, classification accuracy reach to 86%.Keywords : Pre-eclampsia, Bagging CART, Classification Accuracy

IDENTIFIKASI POLA DISTRIBUSI CURAH HUJAN MAKSIMUM DAN PENDUGAAN PARAMETERNYA MENGGUNAKAN METODE BAYESIAN MARKOV CHAIN MONTE CARLO Mukid, Moch. Abdul; Wilandari, Yuciana
MEDIA STATISTIKA Vol 5, No 2 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

especially for the management of regional water resources. In this study, we not only identify the distribution of maximum rainfall,Â but also estimate the parameter of its distribution. The research was conducted in theÂ Grobogan District. Maximum rainfall in the district of Grobogan from 2006 to July 2012 was very varied, but over the years have a pattern unlikely to change.Â Highest maximum rainfall ranged in December, January, February and March while the lowest rainfall maskimum normally be in June, July and August.Â By using the Kolmogorov-Smirnov test on the significance level of 5% is known that the maximum rainfall from 2006 to 2012 in the District Grobogan follow a normal distribution with a value ofÂ D statistics is 0.089.Â This statistic produces a significance value â€‹â€‹of 0.518. By using the Bayesian Markov Chain Monte Carlo obtained the value for the parameter mean of normal distribution is 46.269 mm with a standard error reach into 4.005 mm.

MODEL PENILAIAN KREDIT MENGGUNAKAN ANALISIS DISKRIMINAN DENGAN VARIABEL BEBAS CAMPURAN BINER DAN KONTINU Mukid, Moch. Abdul; Widiharih, Tatik
MEDIA STATISTIKA Vol 9, No 2 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit scoring models is an important tools in the credit granting process. These models measure the credit risk of a prospective client. This study aims to applied a discriminant model with mixed predictor variables (binary and continuous) for credit assesment. Implementation of the model use debitur characteristics data from a bank in Lampung Province which the used binary variables involve sex and marital status. Whereas, the continuous variables that was considered appropriate in the model are age, net income, and length of work. By using the data training, it was known that the misclassification of the model is 0.1970 and the misclassification of the testing data reach to 0.3753.Â Keywords: discriminant analysis, mixed variables, credit scoring

PEMODELAN GRAFIK PENGENDALI TOTAL DAN RATAAN DISKRIT UNTUK GENERALISASI DISTRIBUSI GEOMETRIK Sudarno, Sudarno; Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 9, No 1 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Total events which do by counting will be obtained discret data type. The discret data type and geometric distribution could be drawn by total number of events chart (G chart) and average number of event chart (H chart). In this research result upper control limit, center line, and lower control limit, both G chart and H chart. Data processing of the case, resulting G chart that upper control limit is 80.77 and center line is 39.8, meanwhile by H chart obtained that upper control limit and center line, respectively, 11.54 and 5.8. The results of G chart and H chart could be used for prediction events at the future to anticipate the real problems. Therefore, the systems have no problem and their activities will be dynamic, stable and best perform.Â Keywords:Geometric Distribution, Total Number of Event Chart, Average Number of Event Chart

PERBANDINGAN KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES) Waluyo, Agung; Mukid, Moch. Abdul; Wuryandari, Triastuti
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the greatest asset managed the bank and also the most dominant contributor to the bankâ€™s revenue. Debtors to pay their credit to the bank may smoothly or jammed. This study aims to identify the variables that affect a debtorâ€™s credit status and compare the acuration of classification method both classification and regression trees (CART)Â and logistic regression. The variables used were debtorâ€™s gender, education level, occupation, marital status, and income. By using logistic regression, it was known that only the debtorâ€™s income effect their credit status with the classification accuration reach into 80%. By using CART, there were some variables affect the credit status and the classification accuration 80,9%. This paper showed that the performance of CART in classifying the credit status of debtors was better than logistic regression.Â Keywords: Credit Status, Logistic Regression, CART Â

PEMODELAN REGRESI PROSES GAUSSIAN PEMODELAN REGRESI PROSES GAUSSIAN MENGGUNAKAN FUNGSI PERAGAM EKSPONENSIAL KUADRAT Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 3, No 1 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gaussian Process is a collection of random variables where any finite subset of that has a joint multivariate Gaussian distribution. A Gaussian Process is fully specified by its mean and its covariance function. One of the popular covariance functions is squared exponential that has two hyperparameters. In this paper Gaussian Process is used to made a prediction of Â the number of clothes produced by PT. APAC INTI CORPORA based on the number of attending employes, the number of overtime employes, the number of brokendown machines and used materials. Â Â Keywords: Gaussian Process, Covariace Functions, Squared Exponential

PENDUGAAN DATA HILANG DENGAN MENGGUNAKAN DATA AUGMENTATION Nova, Mesra; Mukid, Moch. Abdul
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing data if the observed data are known. The next step is posterior or P-step where the estimation process of parameter values â€‹â€‹from the complete data is conducted. Imputation and posterior steps on the data augmentation will continue to run until the convergence is reached. The estimate of missing data is obtained through the average of simulated values. Â Keywords: Missing Data, Data Augmentation, Imputation Step, Posterior Step

MODEL PREDIKSI CURAH HUJAN DENGAN PENDEKATAN REGRESI PROSES GAUSSIAN (Studi Kasus di Kabupaten Grobogan) Mukid, Moch. Abdul; Sugito, Sugito
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting method of rainfall has developed rapidly, ranging from the deterministic approach to the stochastic one. Deterministic approach is done through an analysis based on physical laws expressed in mathematical form, which identify the relationships between rainfall and temperature, air pressure, humidity and the intensity of solar radiation. Similarly, there are some stochastic models for the prediction of rainfall that have been commonly used, for instances, the model Autoregressive Integrated Moving Average (ARIMA), Fourier analysis and Kalman filter analysis. Some researchers about climate and weather have also developed a predictive model of rainfall based on nonparametric models, especially models based on artificial neural networks. Above models are based on classical statistical approach where the estimation and inference of model parameters only pay attention to the information obtained from the sample and ignore the initial information (prior) of parameter model. In this research, prediction model with Gaussian process regression approach is used for predicting the monthly rainfall. Gaussian process regression uses a stochastic approach by assuming that the amount of rainfall is random. Based on the value of Root Mean Square Error Prediction (RMSEP), the best covariance function that can be used for prediction is Quadratic Exponential ARD (Automatic Relevance Determination) with RMSEP value 123,63. The highest prediction of the monthly rainfall is in January 2014Â reached into 336,5 mm andÂ the lowest in August 2014 with 36,94 mm. Â Key Words: Gaussian Procces Regression, Covariance Function, Rainfall Prediction

Co-Authors Abdul Hoyyi Agung Waluyo Agus Rusgiyono Agus Setyawan Agus Subagio Alan Prahutama Alwi Assegaf Ana Kartikawati Anastasia Arinda Annisa Sugesti Azzarah, Dian Fatimah Budi Warsito Dani Al Mahkya Desy Ratnaningrum Desy Ratnaningrum, Desy Di Asih I Maruddani Dini Anggreani Djalal Er Riyanto Dwi Ispriyanti Dwi Rahmayani, Dwi Erna Fransisca Angela Sihotang fatimah Fatimah Finisa, Husnul Fuad Muhammad Hasbi Yasin I Gusti Ngurah Antaryama Ilham Muhammad Inas Hasimah Jatmiko Endro Suseno Lies Kurnia Irwanti Masithoh Yessi Rochayani Maulida Azkiya, Maulida Mesra Nova Muhammad Mujahid Muhammad Nur Munifatul Izzati Mustafid Mustafid Noviana Nurhayati Nur Walidaini Nurhikmah Megawati Qorina Rara Sartika Redemtus Heru Tjahjana Restu Sri Rahayu, Restu Ririn Sulpiani Rita Rahmawati Saraswati, Mei Sita Satriyo Adhy Siska Alvitiani Sitomurang, Rosalina Aprilda Sri Wahyuningrum Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Tatik Widiharih Triastuti Wuryandari Udi Harmoko Wetty Anggun Werti, Wetty Anggun Yuciana Wilandari Yudha Subakti, Yudha Yulia Agnis Sutarno Yundari, Yundari Zulfa Wahyu Mardika, Zulfa Wahyu

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