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All Journal INTEK: Informatika dan Teknologi Informasi Prosiding Konferensi Nasional PKM-CSR Pattimura Proceeding : Conference of Science and Technology Prosiding University Research Colloquium
Yuliana Susanti
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Implementasi Algoritma C5.0 Untuk Klasifikas Penyakit Gagal Ginjal Kronik Setyowati Nurhaningsih; Yuliana Susanti; Sri Sulistijowati Handajani
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 2 No. 1 (2019)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v2i1.89

Abstract

Chronic kidney failure is one of the deadly diseases in many countries, including in In-donesia. This disease has a prevalence value increasing with the increasing population. Method that can be used to predict chronic kidney failure in the form of classification trees, namely C5.0. The purpose of this study is to apply the C5.0 to the classification of chronic kidney failure and to calculate the accuracy. Method C5.0 is a classification method in selecting its attributes to be processed using gain information. The independ-ent variables that are influential in this study are erythrocytes, urea, creatine, and plate-lets. The results of this study are in the form of a classification tree for chronic kidney failure. The C5.0 method produces 6 classification segments with an accuracy value of 99.3%.
Penggunaan Geoda untuk Pemetaan Bencana Alam di Kabupaten Karanganyar Hasih Pratiwi; Niswatul Qona’ah; Kiki Ferawati; Sri Sulistijowati Handajani; Handajani Handajani; Yuliana Susanti; Muhammad Bayu Nirwana
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 3 (2020): Peran Perguruan Tinggi dan Dunia Usaha Dalam Pemberdayaan Masyarakat Untuk Menyongsong
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.354 KB) | DOI: 10.37695/pkmcsr.v3i0.817

Abstract

Kemampuan mengolah data menjadi kebutuhan di masa kini, apalagi dengan banyaknya data yang tersedia yang dapat diakses secara bebas. Statistika dapat digunakan untuk membantu masyarakat dalam menjelaskan dan memahami gambaran tentang kejadian bencana alam. Karanganyar, yang terletak di Provinsi Jawa Tengah, merupakan salah satu kabupaten di Indonesia yang rawan bencana alam. Oleh karena itu, diperlukan visualisasi data sebagai upaya untuk memberikan pemahaman kepada masyarakat tentang bencana alam yang terjadi di wilayah Kabupaten Karanganyar. Pemetaan bencana alam dengan Geoda dapat memberikan informasi kondisi kecamatan-kecamatan di Karanganyar yang rawan bencana alam. Untuk menyusun peta, diperlukan data bencana alam serta file peta wilayah. Setelah program Geoda terinstal, peta dapat disusun melalui menu toolbar, mengurutkan kolom kode kabupaten, create project file, dan map. Peta spasial menunjukkan bahwa tanah longsor sering terjadi di wilayah Kabupaten Karanganyar bagian timur yang berbatasan dengan Kabupaten Magetan di Jawa Timur, kebakaran di bagian tengah, dan angin ribut di bagian utara.
ANALISIS KLASTER KABUPATEN/KOTA INDONESIA BERDASARKAN INDEKS PEMBANGUNAN MANUSIA DENGAN MODEL MIXTURE SKEW-T Kristoforus Exelsis Pratama; Irwan Susanto; Yuliana Susanti
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (913.267 KB) | DOI: 10.30598/PattimuraSci.2021.KNMXX.381-388

Abstract

Indonesia merupakan salah satu negara dengan jumlah penduduk yang besar. Penduduk Indonesia yang besar dapat menjadi modal kemajuan bangsa. Indeks pembangunan manusia (IPM) merupakan ukuran yang dapat digunakan untuk mengetahui kualitas manusia di suatu wilayah. Capaian IPM Indonesia dinilai cukup rendah jika dibandingkan negara lainnya. Hal itu terjadi karena adanya disparitas pembangunan manusia antar wilayah. Diperlukan pengelompokkan wilayah sehingga terjadi peningkatan dan pemerataan dalam pembangunan manusia di Indonesia. Penelitian ini akan menggunakan data indeks pembangunan manusia kabupaten/kota di Indonesia pada tahun 2019. Model finite mixture dengan distribusi skew-t tepat digunakan karena dapat mengatasi karakteristik multimodal, kemencengan, heavy-tailed, serta outlier yang sering ditemukan pada data. Estimasi parameter model dilakukan dengan metode maksimum likelihood menggunakan algoritma Expectation-Maximization. Ukuran berbasis Akaike Information Criterion digunakan untuk memilih jumlah komponen mixture. Berdasarkan hasil penelitian diperoleh jumlah komponen optimal model finite mixture distribusi skew-t sebanyak tiga komponen mixture. Hal itu menunjukan kabupaten/kota di Indonesia berdasarkan indeks pembangunan manusia dapat dibagi menjadi tiga klaster. Klaster pertama berisi 80 kabupaten/kota dengan rata-rata IPM sebesar 78,317, klaster kedua berisi 415 kabupaten/kota dengan rata-rata IPM sebesar 70,856, dan klaster ketiga berisi 19 kabupaten/kota dengan rata-rata IPM sebesar 56,247
Robust Regression Generalized Scale (GS) Estimation On Profit Data Of Poultry Farm Companies Safira Callisa; Yuliana Susanti; Irwan Susanto
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.496 KB)

Abstract

Poultry farming is the business of cultivating poultry such as breeding chickens, laying hens, and broilers to obtain meat and eggs. Robust regression is a regression method that is used when some outlier data affect the model so that the distribution of the error is not normal. Estimates on robust regression that can overcome outliers such as Generalized Scale (GS) estimation, GS estimation is seen as an extension of S estimation. GS estimation is a solution for minimizing M estimation with paired scale error. This estimate is applied to poultry data companies in 2020 as an indicator to determine the robust regression model. It is concluded that the factors that affect the total profit of poultry farming companies in Indonesia in 2020 are wages for workers and electricity and water.
Parameter Estimation Robust Regression Method of Moment (MM) in Cases of Maternal Death in Indonesia Putri Ayu Pramesti; Yuliana Susanti; Hasih Pratiwi
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (290.348 KB)

Abstract

Regression analysis is used to determine the relationship between the dependent and independent variables with a parameter estimator. The parameter estimator that is usually used is the Least Squares Method (LSM), this requires a classical assumption test. Some cases have normality assumptions that are unfulfilled because there are outliers so the result regression parameter estimates are not accurate so that robust regression is used in the analysis. Robust regression is a regression analysis method that can withstand outliers. The purpose of this study is the application of robust regression estimation Method of Moment (MM) with Tukey Bisquare weighting in the case of data on the number of maternal deaths in Indonesia 2020 with the number of maternal deaths as a dependent variable, and with independent variables such as the number of pregnant women who experience bleeding, the number of diabetics in pregnancy, and the number of HIV positive in pregnancy. The result showed that every one unit increase of three independent variables had a positive effect on the number of cases of maternal deaths, each of which was 2,8064; 2,5014; 1,1577.
Co-Authors Assyifa Lala Pratiwi Hamid Handajani Handajani Handajani, Sri Sulistijowati Irwan Susanto Kiki Ferawati Kristoforus Exelsis Pratama Melva Hilda Stephanie Situmorang Muhammad Bayu Nirwana Niswatul Qona’ah Pratiwi, Hasih Putri Ayu Pramesti Safira Callisa Setyowati Nurhaningsih Sri Subanti