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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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Journal : Jurnal Gaussian

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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
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