Articles

PENERAPAN LEARNING MANAGEMENT SYSTEM (LMS) PADA SEKOLAH MENENGAH PERTAMA DAN SEKOLAH MENENGAH ATAS Wirawan, Panji Wisnu; Mukid, Moch. Abdul
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Learning Management System (LMS) is a software package  which providesvirtual classes for both teacher and student. LMS can be deployed to localschool networks and can be installed on the school's website. Hence it is easierfor students and teachers in Junior and Senior High School to access learningmaterials. Junior High School and Senior High Schools have many classes andeach class has many subjects in each academic year. To apply the learningsupported by the LMS, a good understanding of LMS features is required, sothat LMS can be used to support many of the classes and subjects. This studyproposes an LMS implementation scheme in Junior High School and SeniorHigh School so that LMS can support many classes and subjects. Specifically,Moodle was selected as LMS product in this study. Firstly, we identify howclass was organized in both junior and senior high school. We continued withidentification moodle features that could support multi class organization. Theresult of this study was a scheme of how Moodle can be implemented to supportmulti class organization in junior and senior high school. Keywords: Learning Management System (LMS), Moodle, multi class organization
PERBANDINGAN METODE K–MEANS DAN SELF ORGANIZING MAP (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2015) Kusumah, Rachmah Dewi; Warsito, Budi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. In this study used two method to classify data of  district / city in Central Java based on indicators of Human Development Index (HDI) 2015 are K-Means and Self Organizing Map (SOM) with the number of groups as much as two to seven. Furthermore, the results of both methods were compared using the Davies-Bouldin Index (DBI) values to determine which method is better. Based on the research that has been conducted found that the K-Means (K=4) method works better than SOM (K=2) to classify district / city in Central Java based on indicators of Human Development Index (HDI) as evidenced by the value of the Davies-Bouldin Index (DBI) on K-Means (K=4) of 0.786 is smaller than the value at SOM (K=2) Davies-Bouldin Index (DBI) which is equal to 0.893. Keywords: clustering, HDI, K-Means, SOM, DBI
ANALISIS KESEHATAN BANK MENGGUNAKAN LOCAL MEAN K-NEAREST NEIGHBOR DAN MULTI LOCAL MEANS K-HARMONIC NEAREST NEIGHBOR Assegaf, Alwi; Mukid, Moch. Abdul; Hoyyi, Abdul
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

The classification method continues to develop in order to get more accurate classification results than before. The purpose of the research is comparing the two k-Nearest Neighbor (KNN) methods that have been developed, namely the Local Mean k-Nearest Neighbor (LMKNN) and Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN) by taking a case study of listed bank financial statements and financial statements complete recorded at Bank Indonesia in 2017. LMKNN is a method that aims to improve classification performance and reduce the influence of outliers, and MLM-KHNN is a method that aims to reduce sensitivity to a single value. This study uses seven indicators to measure the soundness of a bank, including the Capital Adequacy Ratio, Non Performing Loans, Loan to Deposit Ratio, Return on Assets, Return on Equity, Net Interest Margin, and Operating Expenses on Operational Income with a classification of bank health status is very good (class 1), good (class 2), quite good (class 3) and poor (class 4). The measure of the accuracy of the classification results used is the Apparent Error Rate (APER). The best classification results of the LMKNN method are in the proportion of 80% training data and 20% test data with k=7 which produces the smallest APER 0,0556 and an accuracy of 94,44%, while the best classification results of the MLM-KHNN method are in the proportion of 80% training data and 20% test data with k=3 which produces the smallest APER 0,1667 and an accuracy of 83,33%. Based on APER calculation shows that the LMKNN method is better than MLM-KHNN in classifying the health status of banks in Indonesia.Keywords: Classification, Local Mean k-Nearest Neighbor (LMKNN), Multi Local Means k-Harmonic Nearest Neighbor (MLM-KHNN), Measure of accuracy of classification
PENENTUAN MODEL RETURN HARGA SAHAM DENGAN MULTI LAYER FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA RESILENT BACKPROPAGATION (STUDI KASUS : HARGA PENUTUPAN SAHAM UNILEVER INDONESIA TBK. PERIODE SEPTEMBER 2007 – MARET 2015) Priantoro, Riza Adi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Determination of a return of stock price model is often associated with a process of forecasting for future periods.  A method that can be used is neural network. The use of neural network in the field of forecasting can be a good solution, but the problem is how to determine the network architecture and the selection of appropriate training methods. One possible option is to use resilent back propagation algorithm. Resilent back propagation algorithm is a supervised learning algorithm to change the weights of the layers. This algorithm uses the error in the backward direction (back propagation), but previously performed advanced stage (feed forward) to get the error. This algorithm can be used as a learning method in training model of a multi-layer feed forward neural network. From the results of the training and testing on the share return of stock price PT. Unilever Indonesia Tbk. data obtained MSE value of 0.0329. This model is good to use because it provides a fairly accurate prediction of the results shown by the proximity of the target with the output.Keywords : return, neural network, back propagation, feed forward, back propagation algorithm, weight, forecasting.
PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAN TAHUN 2008-2013 DENGAN MENGGUNAKAN REGRESI DATA PANEL Rizki, Muhammad; Rusgiyono, Agus; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Human Development Index (HDI) is a way to measure the success of human development based on a number of basic components quality of life. HDI is formed by three basic variables namely health, education and decent living standards. This study aims to identify factors that influence the Human Development Index in Central Java Province and get a model Human Development Index in Central Java province in 2008-2013. The data used in this study is a combination of cross section data and time series data are commonly called panel data, then this HDI modeling using panel data regression. There are three estimation of panel data regression model namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM).  Estimation of panel data regression model used is the Fixed Effects Model (FEM). FEM estimation results show the number of health facilities, school participation rate and Labor Force Participation Rate significantly affect the HDI by generating  for 93.58%.Keywords : Fixed Effect Model, panel data regression, HDI in Central Java Province
ANALISIS ANTREAN DAN KINERJA SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO (STUDI KASUS: GARDU TOL OTOMATIS GERBANG TOL MUKTIHARJO) Sihotang, Erna Fransisca Angela; Sugito, Sugito; Mukid, Moch. Abdul
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Queue process is a process related to the arrival of customers in a service facility, waiting in line queue if it cannot be served, get service and finally leaves the facility after being served. Research on the queue process can be seen directly through the queue system at the automatic toll booth Muktiharjo. Queue models and their distribution were obtained using the Sigma Magic program. The model of the vehicle queue system at the Muktiharjo Automatic Toll Gate is (GAMM/ GAMM/ 4): (GD/ ?/ ?). Based on the values of the queue system performance measures obtained through the MATLAB GUI program as a whole it can be concluded that the queue of vehicles at the Muktiharjo Automatic Toll Gate has a condition where the average number of vehicles estimated in the system every 15 minutes is 25,5646 vehicles. The average number of vehicles in the queue system every 15 minutes is 24,5639 vehicles. The waiting time in the system is estimated to be around 7,99332 seconds. The estimated waiting time in line is around 7,68042 seconds. The queue system has a busy opportunity of 63.2849% and the remaining 36.7106% is a chance the queue system is not busy. The simulation of the vehicle queue system at the Automatic Toll Gate of Muktiharjo Toll Gate by using ARENA is optimal with the number of service points as many as 4 automatic toll booths. Keywords: Automatic Toll Booth, Queue, Gamma Distribution, Performance Size, Queue Simulation
PERAMALAN HARGA SAHAM DENGAN METODE EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE (ESTAR) (STUDI KASUS PADA HARGA SAHAM MINGGUAN PT UNITED TRACTORS) Rahmayani, Dwi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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The stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 %  are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPEThe stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 %  are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPE
ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA LAYANAN INTERNET SPEEDY DI KOTA SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) Devi, Bella Cynthia; Hoyyi, Abdul; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Persepsi konsumen terhadap tuntutan kebutuhan layanan internet Speedysangat beragam. Terdapat beberapa faktor yang dipertimbangkan konsumen sebelum menggunakan layanan akses internet, faktor tersebut diantaranya harga, merek dan kualitas. Di lain pihak, konsumen akan merasa puas jikalayanan internet Speedy melebihi harapan konsumen. Faktor-faktor yang mempengaruhi keputusan pembelian dan kepuasan layanan internet Speedy diungkapkan secara komprehensif dengan persamaan struktural berbasis komponen, Partial Least Square (PLS). PLS mengestimasi model hubungan antar variabel laten dan antar variabel laten dengan indikatornya. Dari hasil analisis diperoleh kesimpulan bahwa keputusan pembelian layanan internet Speedy dipengaruhi oleh harga, merek dan kualitas, sedangkan kepuasan konsumen dipengaruhi oleh keputusan pembelian dan kualitas.  Kata kunci : Partial Least Square, Speedy, keputusan pembelian, kepuasanANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA LAYANAN INTERNET SPEEDY DI KOTA SEMARANGMENGGUNAKAN PARTIAL LEAST SQUARE (PLS)
ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT Maharunti, Ungu Siwi; Mukid, Moch. Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank ?X? in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk.  The classification using the Fisher multiple discriminant analysis in data of credit client from bank ?X? in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis
PEMODELAN REGRESI 2-LEVEL DENGAN METODE ITERATIVE GENERALIZED LEAST SQUARE (IGLS) (STUDI KASUS: TINGKAT PENDIDIKAN ANAK DI KABUPATEN SEMARANG) Krismala, Dyan Anggun; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. Multilevel regression analysis is the most simple regression analysis 2-levels. 2-level regression analysis will be used to construct a regression model the education level of children in Semarang where children (level-1) nested on the distrits (level-2) with the factors that influence. Estimation of parameter in 2-level regression model can use some methods, one of them is Iterative Generalized Least Square (IGLS). From the results of the discussion indicates that the factors which affect the level of education of children in Semarang is the mother?s education, father? education, and percentage of farm families. The diversity level of the education of children in Semarang caused more variation among children than the variation between districts.
Co-Authors Abdul Hoyyi Agung Waluyo Agus Rusgiyono Alan Prahutama Alvita Rachma Devi Ana Kartikawati Anastasia Arinda, Anastasia Angela Sihotang, Erna Fransisca Anifa Anifa Anissa Pangastuti Arief Rachman Hakim Assegaf, Alwi Bella Cynthia Devi, Bella Cynthia Bramaditya Swarasmaradhana Budi Warsito Bunga Maharani, Bunga Catra Aditya Wisnu Aji Dani Al Mahkya Dedy Douglas Harianja, Dedy Douglas Desy Ratnaningrum, Desy Desy Trishardiyanti Adiningtyas, Desy Trishardiyanti Diah Safitri Dini Anggreani Dwi Ispriyanti Dwi Rahmayani, Dwi Dyan Anggun Krismala Elyas Darmawan Endah Cahyaningrum, Endah Erna Puspitasari Etik Setyowati, Etik fatimah Fatimah Fatma Septy Deviana Finisa, Husnul Fitra Ramdhani, Fitra Fuad Muhammad Hasbi Yasin Hasibuan, Chainur Arrasyid Hasimah, Inas Herwindhito Dwi Putranto Ilham Muhammad Kiki Febri Azriati Kusumah, Rachmah Dewi Laili Isna Nur Khiqmah, Laili Isna Nur Landong Panahatan Hutahaean Lies Kurnia Irwanti Maharsi, Iantazar Rezqitullah Mardison Purba Marta Widyastuti, Marta Maulida Azkiya, Maulida Mesra Nova Muhammad Mujahid Muhammad Rizki Munifatul Izzati Mustafid Mustafid Naomi Rahma Budhianti Niken Anggraini Dewi Novia Agustina, Novia Noviana Nurhayati Nunung Hanurowati, Nunung Nurhikmah Megawati Panji Wisnu Wirawan Restu Sri Rahayu, Restu Restu Sri Rahayu, Restu Sri Rita Rahmawati Riza Adi Priantoro, Riza Adi Saraswati, Mei Sita Shaumal Luqman, Shaumal Sihotang, Erna Fransisca Angela Sitomurang, Rosalina Aprilda Sri Wahyuningrum Sudarno Sudarno Sugesti, Annisa Sugito Sugito Suparti Suparti Syaifudin Karyadi, Syaifudin Syarah Widyaningtyas, Syarah Tarno Tarno Tatik Widiharih Trianita Resmawati Triastuti Wuryandari Ungu Siwi Maharunti, Ungu Siwi Utami, Cyntia Surya Walidaini, Nur Wetty Anggun Werti, Wetty Anggun Yosi Dhyas Monica Yuciana Wilandari Yudha Subakti, Yudha Yulia Agnis Sutarno Zulfa Wahyu Mardika, Zulfa Wahyu