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All Journal International Journal of Science and Engineering (IJSE) Media Statistika Jurnal Ilmu Dasar Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang E-Dimas: Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Surya Masyarakat Jurnal Varian Jurnal Surya Masyarakat
Alan Prahutama
Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Public Health Social Sciences Other

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ANALISIS PENGARUH KEPUASAN TERHADAP LOYALITAS KONSUMEN SMARTPHONE SAMSUNG MENGGUNAKAN METODE PARTIAL LEAST SQUARE PADA MAHASISWA UNIVERSITAS DIPONEGORO SEMARANG Jefferio Gusti Putratama; Alan Prahutama; Suparti Suparti
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.30948

Abstract

Smartphones are one of the electronic devices that are capable of experiencing fairly rapid development. The existence of this Smartphone is considered to be the most important item for used everyday. Samsung is one of the most popular smartphone brand in Indonesia. Based on data from the website of the Statcounter survey institute, it was found that the Samsung market share in Indonesia until August 2020 was in the top position, namely 24.19%. Samsung continues to make various innovations in order to continue to dominate the top of the smartphone sales segment. In addition, to provide consumer's satisfication so that consumer’s loyalty to the Samsung brand will be maintained. The purpose of this study is to make measurement models and structural models, as well as to test the relationship of customer satisfaction to consumer loyalty of Samsung smartphones using the SEM – PLS (Partial Least Square) method. This research was conducted on Diponegoro University students who have purchased and used a Samsung smartphone. This research was conducted on Diponegoro University students who have purchased and used a Samsung smartphone. This research has produced 4 latent variables with 18 measurement models and 2 structural models. Based on the 2 structural models formed, the result shows that the R2 value in the customer satisfaction model is 0.670. This indicates that the variable customer satisfaction can be explained by the variable product quality and price by 67%. Meanwhile, in the consumer loyalty model, the R2 value is 0.478. This indicates that the consumer loyalty variable can be explained by the consumer satisfaction variable of 47.8%. Keywords:    Samsung Smartphone, Consumer’s Satisfaction, Consumer’s Loyalty, Partial Least Square.
ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING Ratna Kurniasari; Rukun Santoso; Alan Prahutama
Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i4.33101

Abstract

Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members. 
HISTORICAL SIMULATION UNTUK MENGHITUNG VALUE AT RISK PADA PORTOFOLIO OPTIMAL BERDASARKAN SINGLE INDEX MODEL MENGGUNAKAN GUI MATLAB (Studi Kasus: Kelompok Saham JII Periode Juni - November 2017) Tresno Sayekti Nuryanto; Alan Prahutama; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28869

Abstract

The essence of investment is a placement of a number of funds at one time in hope of gaining profits in the future. One of the most traded forms of investment is stocks. When investing in stocks, investors often run the risk of loss. This loss risk can be overcome by forming a portfolio consisting of several shares. To form an optimal portfolio, investors must first determine an efficient portfolio that produces a certain level of profit with the lowest risk, or a certain level of risk with the highest level of profit. One method for determining the optimal portfolio is to use the Single Index Model method. Whereas to calculate Value at Risk (VaR) using the Historical Simulation method. In this study, researcher used data from the daily closing price of shares incorporated in the Jakarta Islamic Index (JII) stock group in the period of June - November 2017. The shares which will be used were 9 shares in the JII stock group. According to the research result, there are three stocks that go into an optimal portfolio that is SMGR, UNTR, and KLBF with the value of each of its shares respectively by 48,54%, 46,18%, and 5,28%. While the value of the Value at Risk with initial capital of Rp100.000.000, 1 day holding period and a trust level of 95% for optimal portfolio and each stock that goes into optimal portfolio amounted Rp2.090.283, Rp2.258.600, Rp3.403.000, and Rp2.564.200. Keywords: Share, Portofolio, Single Index Model, Value at Risk, Historical Simulation, JII.
PEMILIHAN INPUT MODEL ANFIS UNTUK DATA RUNTUN WAKTU MENGGUNAKAN METODE FORWARD SELECTION DILENGKAPI GUI MATLAB (Studi Kasus: Jumlah Penumpang Kereta Api di Wilayah Jawa Non Jabodetabek) Tiara Sukma Valentina; Tarno Tarno; Alan Prahutama
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (976.728 KB) | DOI: 10.14710/j.gauss.v8i2.26668

Abstract

One of the methods that is commonly used to identify a time series model and input ANFIS (Adaptive Neuro Fuzzy Inference System) model is PACF plot. The PACF plot shows the correlation between current observations and previous observations visually. Formally there are several methods that are known to effectively identify ANFIS inputs, one of which is the Forward Selection regression method. With the same concept as PACF, the process of selecting ANFIS inputs using the Forward Selection method is based on the order of the correlatiom between the predictors of the response which is indicated by the magnitude of the correlation coefficient. This study discusses the Forward Selection method in simulation data that has stationary characteristics, stationary with outliers, non stationary, non stationary with outliers and implements data on the number of train passengers in the Non Jabodetabek Java region. ANFIS modeling on data of the number of train passengers in the Non Jabodetabek Java region produces AIC of 15,5617, MAPE of 8,5093% and RMSE of 571,33691. The result of this study is equipped with a GUI which is useful as a tool to facilitate users in processing data.Keywords : PACF Plot, Forward Selection, ANFIS, non stasionary, outlier
ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA DENGAN KLASIFIKASI NAÏVE BAYES UNTUK MODEL BERNOULLI DAN MULTINOMIAL Nabila Surya Wardani; Alan Prahutama; Puspita Kartikasari
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.27963

Abstract

Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Indonesian President affirmed that the capital would be moved to East Kalimantan on August 26, 2019. That planning would receive pros and cons from public. Sentiment analysis is part of text mining that typically involves taking data from opinion, comment, or response. Sentiment analysis is the choice to do on this topic to get results about the public’s opinion. As the most used social media in Indonesia, Youtube is able to be data source by crawling the comments on a video uploaded by Kompas TV channel. Those comments were crawled on October 15, 2019, and selected 1500 latest comments (August 26 – October 12, 2019). The selected comments get transformed by using data pre-processing technique that involves case folding, removing mention, unescaping HTML, removing numbers, removing punctuation, text normalization, stripping whitespace, stopwords removal, tokenizing, and stemming. Labeling of sentiment class uses the sentiment scoring technique. The number of negative comments is 849, while the number of positive comments is 651. The ratio between training data and testing data is 80%: 20%. The classification method used to do sentiment analysis is the Naive Bayes Classifier for Bernoulli and Multinomial model. Bernoulli model only uses occurrence information, whereas the multinomial model keeps track of multiple occurrences. The results show that Bernoulli Naïve Bayes has a 93,45% level of sensitivity (recall) and Multinomial Naïve Bayes has a 90,19% level of sensitivity (recall). It means that both Bernoulli and Multinomial have a good result for this research.  
PEMODELAN REGRESI POISSON BIVARIAT PADA JUMLAH KEMATIAN IBU HAMIL DAN NIFAS DI JAWA TENGAH TAHUN 2017 Arbella Maharani Putri; Alan Prahutama; Budi Warsito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (988.358 KB) | DOI: 10.14710/j.gauss.v8i3.26677

Abstract

The maternal mortality rate is one of the indicators that determine the prosperity level of society in a country. Most of the maternal mortality caused by pregnancy maternal mortality and postpartum maternal mortality. Central Java is one of the provinces with the biggest number of pregnancy maternal mortality and postpartum maternal mortality in Indonesia. The number of pregnancy maternal mortality and postpartum maternal mortality follow Poisson Distribution and it has a significant correlation. Therefore, the writer analyzed factor that influences the number of pregnancy maternal mortality and postpartum maternal mortality using Univariate and Bivariate Poisson Regression method. Results from this study obtained that in the Univariate Poisson Regression variables that significantly influence pregnancy maternal mortality and postpartum maternal mortality are the percentage of pregnant women implementing K1 (X1), percentage of childbirth women that has puerperal health service (X6) and percentage of household with clean and healthy behavior (X7). In the Bivariate Poisson, the best model is the second model which assuming that covariance is an equation.Keywords: Pregnancy of Maternal Mortality, Postpartum Maternal Mortality, Bivariate Poisson Regression.
PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG Arafa Rahman Aziz; Budi Warsito; Alan Prahutama
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30933

Abstract

Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test  known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation
Aplikasi Teknologi Ulir Filter (TUF) dengan Media Geomembrane sebagai Upaya Peningkatan Kualitas dan Kuantitas Produksi Garam di Kabupaten Pati Jawa Tengah Hasbi Yasin; Sugito Sugito; Moch. Abdul Mukid; Alan Prahutama
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 10, No 2 (2019): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v10i2.3015

Abstract

Kebutuhan akan garam semakin meningkat, baik kebutuhan garam rumah tangga apalagi kebutuhan terhadap garam industri. Kabupaten Pati sebagai salah satu pusat produksi garam di Jawa Tengah diharapkan mampu untuk memenuhi permintaan garam yang semakin meningkat. Upaya yang dapat dilakukan adalah dengan peningkatan kualitas dan kuantitas garam melalui teknologi yang tepat, murah, dan mudah diaplikasikan oleh para petani garam. Salah satu metode yang dapat digunakan adalah produksi garam dengan sistem Teknolgi Ulir Filter (TUF) dengan media Geomembrane. Metode ini mampu meningkatkan efisiensi waktu produksi dan juga mampu meningkatkan kualitas garam yang dihasilkan. Oleh karena itu, Tim Pengabdian PKUM Undip bekerja sama dengan Kelompok Usaha Garam Rakyat (KUGAR) Karya Makmur dan KUGAR Garam Mulya dalam upaya meningkatkan produksi kuantitas dan kualitas garam di Kabupaten Pati, Jawa Tengah. Kegiatan ini dilakukan dalam bentuk perbaikan Standar Operasional Prosedur (SOP) tentang teknik produksi garam dan pemberian bantuan alat produksi untuk meningkatkan kapsitas produksi dan kualitas garam yang dihasilkan. Hasil kegiatan ini mampu meningkatkan produksi garam mencapai 30-40% dengan kualitas garam yang lebih baik (Kualitas K1/Garam Super).
Diversifikasi Olahan Ikan Bandeng oleh UKM Primadona dalam Program Pengabdian IbPE 2016-2018 Sugito Sugito; Alan Prahutama; Tarno Tarno; Abdul Hoyyi
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 10, No 1 (2019): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v10i1.3556

Abstract

Ikan bandeng merupakan bahan makanan yang tinggi akan protein, vitamin dan mineral. Salah satu cara untuk meningkatkan pemasaran adalah mix marketing, salah satunya adalah mix marketing produk. Mix marketing produk yang dapat dilakukan adalah dengan diversifikasi produk. Olahan ikan bandeng yang terkenal adalah di kabupaten Pati. UKM Primadona merupakan UKM yang bergerak pada olahan ikan bandeng dan merupakan salah satu UKM binaan dari Universitas Diponegoro dalam program pengabdian Ipteks bagi Produk Ekspor (IbPE) 2016-2018. Dalam binaan tersebut, yang menjadi salah satu program adalah diversifikasi produk UKM Diversifikasi produk yang dilakukan oleh UKM Primadona atas binaan tim pengabdi adalah keripik kulit dan abon duri ikan bandeng. Kulit ikan bandeng merupakan hasil filet dari daging ikan bandeng. Kulit ikan bandeng dicampur dengan tepung beras, tepung tapiokan dan rempah-rempah lainnya untuk diolah menjadi keripik kulit yang renyah. Tekstur keripik kulit ikan bandeng adalah renyah, mempunyai pola sisik ikan. Kandungan protein, vitamin dan mineral keripik kulit ikan bandeng juga cukup tinggi. Untuk abon duri ikan bandeng sangat berkhasiat karena kandungan kalsiumnya cukup tinggi.
Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns Felinda Arumningtyas; Alan Prahutama; Puspita Kartikasari
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1474

Abstract

Before buying a stock, an investor must estimate the risk which will be received. VaR is one of the methods that can be used to measure the level of risk. Most stock returns have a high fluctuation, so the variant is heteroscedastic, which is thought to be caused by exogenous variables. The time series model used to model data that is not only influenced by the previous period but is also influenced by exogenous variables is ARIMAX. In contrast, the GARCHX model is used to obtain a more optimal stock return data model with heteroscedasticity cases and is influenced by exogenous variables. This study uses the ARIMAX-GARCHX model to calculate the VaR of the stock returns of PT Bank Central Asia Tbk. The exogenous variables used are the exchange rate return of IDR/USD and the return of the JCI in the period January 3, 2017, to March 31, 2021. The best model chosen is the ARIMAX(2,0,1,1)-GARCHX(1,1,1). VaR calculation is carried out with the concept of moving windows with time intervals of 250, 375, and 500 transaction days. The results obtained at the 95% confidence level, the maximum loss obtained by an investor is 1,4%.
Co-Authors Abdiel Pandapotan Manullang Abdul Hoyyi Abdul Rohman Agus Rusgiyono Alifah Zahlevi Alvita Rachma Devi Ana Kristiana, Ana Anak Agung Gede Sugianthara Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arkadina Prismatika Noviandini Taryono Ayudya Tri Wahyuningtyas Bramadita Kunni Fauziyyah Budi Warsito Dede Zumrohtuliyosi Dhea Dewanti Dini Febriani Dwi Ispriyanti Farhah Izzatul Jannah Felinda Arumningtyas Hasbi Yasin Helmi Panjaitan Izzudin Khalid, Izzudin Jefferio Gusti Putratama Jody Hendrian Laili Rahma Khairunnisa Lingga Bayu Prasetya Lutfia Nuzula Lutfiah Maharani Siniwi Moch. Abdul Mukid Muhammad Zidan Eka Atmaja Mustafid Mustafid Nabila Surya Wardani Nahdliyah, Milla Alifatun Nur Walidaini Pandu Anggara Pramestiara Dewiga, Pramestiara Puspita Kartikasari Rafida Zahro Hasibuan Ratna Kurniasari Rezzy Eko Caraka Rita Rahmawati Rizki Brendita Br Tarigan Rosyidah, Haniatur Rukun Santoso Shofi Andari Sudargo Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Suparti, S. Tamura Rolasnirohatta Siahaan Tarno Tarno Tatik Widiharih Thea Zulfa Adiningrumh Tiani Wahyu Utama Tiani Wahyu Utami Tiara Sukma Valentina Tomi Ardi Tresno Sayekti Nuryanto Tsania Faizia Ulfi Nur Alifah Wahyu Sabtika Widi Rahayu Yuciana Wilandari