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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Komputer dan Informasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer UNEJ e-Proceeding Jurnal Aplikasi Statistika & Komputasi Statistik TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Pattimura Proceeding : Conference of Science and Technology Prosiding University Research Colloquium Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Computing Science and Applied Mathematics-IJCSAM
Irwan Susanto
Universitas Sebelas Maret
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PENGELOMPOKAN DAERAH PENGHASIL BAHAN DASAR TEPUNG KOMPOSIT DI INDONESIA MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS (LCCA) Budiati, Shinta; Susanto, Irwan; Wibowo, Supriyadi
MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.365 KB) | DOI: 10.14710/medstat.7.1.21-28

Abstract

Wheat as a base substance of flour, is a source of carbohydrate which is most used for the manufacturing of variety of foodstuffs. Substitution a part of flour with composite flour for manufacturing food will decrease dependency of imported wheat.This research aims to classify the area which produce base substance of composite flour in Indonesia.For this research we will know a group of provinces which become center of production and development target of local resources potency. One way that is used to grouping the object is cluster analysis. In development, there is another grouping technique used, namely Latent Class Cluster Analysis (LCCA).The results show that the selected model from grouping using LCCA is 3groups. The first group is the enough potential area as a production development center. While the second group have the greatest potential area. Meanwhile the last group is the less potentially area.   Keywords: Composite Flour, Cluster Analysis, Latent Class Cluster Analysis (LCCA)  
Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification NUR Iriawan; Kartika Fithriasari; Brodjol Sutija Suprih Ulama; Wahyuni Suryaningtyas; Irwan Susanto; Anindya Apriliyanti Pravitasari
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.777 KB) | DOI: 10.21609/jiki.v11i2.536

Abstract

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of  the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model
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
PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE Irwan Susanto; Sri Sulistijowati Handajani
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.616 KB) | DOI: 10.14710/medstat.13.1.13-24

Abstract

In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., gamma, lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-lognormal mixture model.
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.
PEMODELAN INDEKS PEMBANGUNAN KESEHATAN MASYARAKAT DENGAN METODE GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Quraini Septy Wardhani; Sri Sulistijowati Handajani; Irwan Susanto
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 14 No 1 (2022): Jurnal Aplikasi Statistika dan Komputasi Statistik
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i2.333

Abstract

Geographically weighted logistic regression (GWLR) adalah metode yang dapat memodelkan data bersifat kategorik dengan mempertimbangkan efek spasial. Pada penelitian ini, metode GWLR digunakan untuk memodelkan indeks pembangunan kesehatan masyarakat (IPKM) di Provinsi Jawa Timur. Variabel yang digunakan adalah prevalensi balita stunting, hipertensi, pneumonia, persalinan ditangani nakes, pengguna KB MKJP, dan penduduk dengan perilaku cuci tangan benar. Hasil penelitian ini menunjukkan bahwa pemodelan GWLR dengan pembobot adaptive Gaussian Kernel lebih baik daripada pembobot lain dengan faktor yang berpengaruh signifikan secara lokal adalah prevalensi balita stunting dan hipertensi. Nilai akurasi, sensitivity, dan specitivity yang dihasilkan berturut-turut sebesar 97,4% , 100% dan 85,71%.
PENGELOMPOKAN NEGARA BERDASARKAN KASUS STUNTING DENGAN MODEL FINITE MIXTURE NORMAL MENGGUNAKAN PENDEKATAN BAYESIAN Adella Okky Herashanti; Irwan Susanto; Isnandar Slamet
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting (short stature) is a chronic condition characterized by stunted growth due to malnutrition over a long period of time. Stunting needs special attention because it can hamper children's physical and mental development and is associated with an increased risk of illness and death. The high incidence of stunting in the world is still a health problem that is still being faced by WHO [7]. Therefore, it is necessary to group countries based on stunting cases in order to facilitate appropriate policy making in overcoming and preventing stunting. In this study, 151 countries were grouped based on stunting cases with a normal finite mixture model. The model is estimated using Bayesian estimation through Birth-Death Markov Chain Monte Carlo (BD-MCMC) method. The results of the study show that there are five groups of countries based on stunting cases, where the first group consists of 30 countries and the second group consists of 32 countries which are groups with low stunting rates. While the third group has 33 members, the fourth group has 34 countries and the fifth group has 22 countries which is a group with a high stunting rate. Keywords: BD-MCMC, Finite Mixture, Pengelompokan, Stunting.
PENINGKATAN KEMAMPUAN GURU PEMBINA KOMPETISI SAINS NASIONAL MATA PELAJARAN MATEMATIKA SMP DI KABUPATEN KARANGANYAR Etik Zukhronah; Winita Sulandari; Sugiyanto Sugiyanto; Isnandar Slamet; Sri Subanti; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 8: Januari 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i8.4526

Abstract

Dalam upaya meningkatkan kemampuan guru pembina kompetisi sains nasional, Dinas Pendidikan dan Kebuadayaan Kabupaten Karanganyar bekerjasama dengan tim pengabdi dari Program Studi Statistika FMIPA Universitas Sebelas Maret mengadakan bimbingan teknis materi KSN mata pelajaran Matematika pada guru-guru Pembina KSN mata pelajaran Matematika SMP di Kabupaten Karanganyar. Kegiatan bimbingan teknis yang dilaksanakan selama dua hari telah mampu meningkatkan kemampuan peserta hingga 33 %. Untuk hasil yang lebih optimal, kegiatan serupa perlu dilakukan secara rutin dan terstruktur.
PENINGKATAN JIWA WIRAUSAHA SANTRI MELALUI PELATIHAN PEMANFAATAN SAMPAH PLASTIK MENJADI PRODUK BERNILAI JUAL Etik Zukhronah; Winita Sulandari; Isnandar Slamet; Sri Subanti; Sugiyanto Sugiyanto; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 9: February 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i9.4777

Abstract

Lack of public understanding about the proper handling of plastic waste can damage the environment. Based on the results of a survey conducted on students at the Darul Muttaqin Islamic Boarding School, Sragen, it can be seen that the waste management in the boarding school has not been carried out properly. In general, waste is directly disposed of in a landfill, without prior sorting between organic and inorganic waste. In this case, the residents of the cottage have not tried to process waste, especially plastic waste into useful products. For this reason, the service team for the Statistics Study Program FMIPA UNS held a socialization and training on the use of plastic waste into ornamental flower products. The purpose of this activity is to equip students with skills, as well as to foster an entrepreneurial spirit by marketing products from plastic waste to the general public. In the end, the success of product marketing will provide its own advantages as an alternative source of income for the students. In the future, the activities carried out consistently and sustainably will not only provide good benefits for the students but also the preservation of the surrounding environment.
Retinopathy Classification using Convolutional Neural Network Method with Adaptive Momentum Optimization and Applied Batch Normalization Slamet, Isnandar; Susilotomoa, Dhestahendra Citra; Zukhronah, Etik; Subanti, Sri; Susanto, Irwan; Sulandari, Winita; Sugiyanto, Sugiyanto; Susanti, Yuliana
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.309

Abstract

Retinopathy is a common eye disease in Indonesia, ranking fourth after cataracts, glaucoma, and refractive errors. It can be overcome by early diagnosis with optical coherence tomography (OCT), but this imaging technique takes much time. In this research, retinal imaging was carried out using an expert system. The expert system in this study was formed using the convolutional neural network (CNN or ConvNet) method. CNN is an algorithm of deep learning that uses convolution operations to process two-dimensional data, such as images and sounds. This research consisted of 4 stages: data collection, preprocessing, model design, and model testing. A CNN model was formed with three arrangements, consisting of two convolutional layers and one pooling layer. The ReLU activation function, zero padding, and batch normalization were used in all three formats. Then, the flattening process was carried out, and the Softmax activation function was used at the end of the architecture. The model was built using eight epochs, and optimization of Adaptive Momentum resulted in a 98.96% test data accuracy value. The result was considered good so that CNN could be used as an alternative in retinopathy diagnosis. Further research is suggested to use other optimizations or other model architectures.
Co-Authors Adella Okky Herashanti Adi Wicaksono, Nanda Adigama Tri Nugraha Aji Susanto Anindya Apriliyanti Pravitasari Aprilia, Nabila Churin Aristiarto, Rio Brodjol Sutija Suprih Ulama Fhadilla, Nahdatul Fithriasari, Kartika Handajani, Sri Sulistijowati Hidayah, Nurrul Husna Afanyn Khoirunissa Indra Raditya , Dionisius Isnaini, Bayutama Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet, Isnandar Jelita, Nadia Aulia Kadar, Jimmy Abdel Kristoforus Exelsis Pratama Nabila Churin Aprilia Nugraha, Titis Jati Nur Iriawan Pardede, Hilman Ferdinandus Quraini Septy Wardhani Safira Callisa Shinta Budiati Sri Subanti Sri Sulistijowati Handajani Sri Sulistijowati Handajani Sugianto Sugianto Sugiyanto - Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Sulandari, Winita Supriyadi Wibowo Susilotomoa, Dhestahendra Citra Wahyuni Suryaningtyas, Wahyuni Winita Sulandari Winita Sulandari Yuliana Susanti Yuliana Susanti, Yuliana Zukhronah, Etik