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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Pendidikan Matematika (Kudus) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika LOSARI: Jurnal Pengabdian Kepada Masyarakat Tepis Wiring : Jurnal Pengabdian Masyarakat Journal of Data Insights Prosiding Seminar Nasional Unimus
Prizka Rismawati Arum
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Jurnal Statistika Universitas Muhammadiyah Semarang

 1 
MODEL SEEMINGLY UNRELATED REGRESSION PADA DATA KEMISKINAN JAWA TIMUR MENGGUNAKAN MATRIKS PEMBOBOT QUEEN CONTIGUITY DAN ROOK CONTIGUITY Cika Awani Ayuwida; Prizka Rismawati Arum; M. Al Haris
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.64-68

Abstract

Jawa timur, salah satu provinsi yang memiliki sumbangan cukup tinggi yakni 16% dari pertumbuhan ekonomi nasional, merupakan daerah yang potensial baik dari segi ekonomi maupun geografis. Berdasarkan data Sensus Penduduk tahun 2020 mencapai 4.419,10 ribu jiwa (11,09 persen), bertambah sebesar 363,1 ribu jiwa. Tujuan dari penelitian ini adalah untuk memodelkan Seemingly Unrelated Regression (SUR) terbaik pada data Kemiskinan di Provinsi Jawa Timur dengan menggunakan matrik pembobot queen contiguity dan rook contiguity. Penerapan persamaan regresi dalam sebuah kasus seringkali memiliki keterkaitan dengan persamaan yang lain. Jika sebuah persamaan saling berkaitan dikarenakan error regresinya saling berkorelasi, maka pendekatan yang dapat digunakan adalah Seemmingly Unrelated Regression (SUR).
FORECASTING THE NUMBER OF PASSENGER AT JENDERAL AHMAD YANI SEMARANG INTERNATIONAL AIRPORT USING HYBRID SINGULAR SPECTRUM ANALYSIS-NEURAL NETWORK (SSA-NN) METHOD Tresiani Yunitasari; M. Al Haris; Prizka Rismawati Arum
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.22-33

Abstract

Transportation was an important sector of supporting the economic growth of a country. The impact of the Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted in the movement of the number of passengers decreasing quite drastically, but in mid-2020 the movement of the number of passengers had slowly increased. Forecasting was done to determine the flow of movement of the number of passengers in the future using the Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. The SSA method was expected to be able to decompose various patterns in the data into trend, seasonality and noise. Furthermore, the NN method was used to analyze nonlinear patterns in the data. The results showed that the best method was a combination of the SSA method with a window length of 40 and the NN method with a 6-8-1 network architecture (6 input neurons, 8 hidden neurons and 1 output neuron) for the trend component, 11-15-1 (11 neurons input, 15 hidden neurons and 1 output neuron) for the seasonal component, and 10-15-1 (10 input neurons, 15 hidden neurons and 1 output neuron) for the noise component. The model produces a prediction error based on a MAPE value of 0.54% or an accuracy rate of 99.46%.
MODEL COX PROPORTIONAL HAZARD dengan Metode Backward untuk Analisis Ketahanan Hidup Pasien Penderita Stroke Alfisyahrina Hapsery; Izzadin Muhamad Nur Irfan; Prizka Rismawati Arum
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 10, No 1 (2022): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.10.1.2022.7-14

Abstract

Stroke adalah penyakit yang disebabkan oleh terhentinya suplai darah ke otak yang menyebabkan otak kehilangan fungsinya. Stroke terjadi ketika pembuluh darah di otak tersumbat, mengakibatkan otak tidak mendapatkan suplai oksigen yang cukup untuk membawa darah ke seluruh tubuh, mengakibatkan kematian sel/jaringan. Faktor risiko stroke dapat dipengaruhi oleh usia, jenis kelamin, dan faktor akibat perilaku tidak sehat seperti hipertensi, diabetes. Berbagai faktor diduga mempengaruhi pasien stroke, oleh karena itu dilakukan pemodelan untuk memastikan faktor-faktor tersebut. Metode yang dapat memberikan informasi untuk menentukan faktor kelangsungan hidup seseorang adalah model regresi cox proporsional hazard. Variabel terikat ditentukan dari jumlah waktu kelangsungan hidup pasien dan jenis pasien yang termasuk data tersensor atau tidak. Dalam penelitian ini, data yang digunakan sebagai variabel terikat adalah lamanya pasien stroke dirawat inap sampai sembuh, atau mengalami suatu kejadian yaitu kekambuhan. Sedangkan variabel bebas yang digunakan dalam penelitian ini adalah umur, jenis kelamin, hipertensi, diabetes, lemas, nyeri, pelo, sulit makan, mual, muntah, demam dan pusing. Hasil pemodelan regresi cox proportional hazard diperoleh faktor-faktor yang mempengaruhi kekambuhan pasien stroke yaitu variabel diabetes dengan nilai estimasi 1,6696 dan variabel lemas.
OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION Saeful Amri; Ariska Fitriyana Ningrum; Prizka Rismawati Arum
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.44-50

Abstract

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.
Co-Authors Ach Ridoi Alambara Agi Khoerunnisa Alfisyahrina Hapsery Ali Imron Amalia Jihan Syafiqoh Anita Retno Indriani Anjelina Rafika Wulandari Ariska Fitriyana Ningrum Cika Awani Ayuwida Dannu Purwanto Dheanyta Alif Shafira Dominikus Leki Sogen Dwi Rahmadini Eko Andy Purnomo Evida Oktaviana Fatkhurrokhman Fauzi Hanief Rizky Utami Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Isna Nur Azizah Izzadin Muhamad Nur Irfan Kurnia Nur Annisa Laras Indah Setyaningsih Linda Puspitasari M. Al Haris M. Al Haris Miftakhul Haris Prissy Nusaiba Yulisa Rochdi Wasono Saeful Amri Saeful Amri Siva Alfian Sugito Sugito Tiani Wahyu Utami Tresiani Yunitasari Wisudawati, Dinda Tri Yuciana Wilandari Yulia Fitri