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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmu Komputer dan Informasi MATEMATIKA Jurnal Sains dan Seni ITS IPTEK Journal of Science IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Natural A Seminar Nasional Informatika (SEMNASIF) Science and Technology Indonesia Jurnal Matematika: MANTIK Syntax Literate: Jurnal Ilmiah Indonesia Business and Finance Journal Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Inferensi Proceeding ISETH (International Summit on Science, Technology, and Humanity) Sewagati PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Nur Iriawan
Departemen Statistika Institut Teknologi Sepuluh Nopember
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Bayesian Survival Mixture Model on Years of Schooling in West Papua Province Maulidiah Nitivijaya; Nur Iriawan; Heri Kuswanto
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Education could be considered as one of the basic pillars to determine the performance indicator of a respective region. Year of schooling is one of the education indexes,which becomes the government's target in the 9-year compulsory education program. This index illustrates the importance of knowledge and higher-level skills. Meanwhile, West Papua Province as one of the youngest provinces in Indonesia is challenged to improve the quality of human resources, particularly in the underdeveloped regions. Therefore, it is important to identify the variables which influence the years of schooling in the West Papua province. Statistically, the type of data such as length of time is frequently used to be the survival analysis. Nevertheless, the distribution patternof the response variables is difficult to be analyzed. For that reason, this study applied mixture model on years of schooling. Mixture model estimation leads to the complex statistical problems with a number of parameters. Bayesian methods accomplish the estimation through the simulation process of Markov Chain Monte Carlo (MCMC). The survival mixture model was formed based on the status of county. Rural areas were evidenced to give the contribution of years of schooling distribution more than urban area up to 59.87 percent. The opportunity to obtain formal education at least to junior high school in urban areas was greater than rural area had, yet it went down faster in year 12-th or in senior high school level. In general, the factors which influenced the years of schooling in urban and rural areas turned out to be different.
Bayesian Mixture Statistical Modeling Perspective in the Series of Diabetes Mellitus Disaster Mitigation in Malang Regions Ani Budi Astuti; Nur Iriawan; Suci Astutik; Viera Wardhani; Ari Purwanto Sarwo Prasojo; Tiza Ayu Virania
Science and Technology Indonesia Vol. 8 No. 1 (2023): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.1.71-83

Abstract

Statistical modeling is one of the most important activities in Statistics in order to simplify complex problems in society, to make it easy, simple, and useful. The perspective of statistical modeling is very useful for society in various fields. Probabilistic-based statistical modeling concept is strongly influenced by the shape of the data distribution, data validity, and data availability. Bayesian concept approach in the statistical modeling has advantages compared to the non-Bayesian approach, which is any sample and any distribution of the data and in this case it often occurs in data in the community. In particular, the Bayesian mixture concept discusses the Bayesian approach with data specifications having a mixture (multimodal) distribution. Diabetes Mellitus (DM) is a disease that is not contagious but the side effects are very dangerous for humans and require large costs to handle. Indonesia ranks seventh in the world for the number of DM sufferers and it is estimated that in 2045, the number of DM sufferers in Indonesia will reach approximately 16.7 million people. Mitigation of DM disease in various regions in Indonesia continues to be pursued, including Malang regions. One of the efforts made is through the statistical modeling perspective of the Bayesian approach which can be used for efforts to control, prevent, treat, and overcome DM. The purpose of the study was to build a suitable Bayesian model for DM cases in Malang regions in order to map the DM case areas in Malang. The results showed that in each district area in the city of Malang it was divided into three groups based on the severity of DM sufferers. The three groups are DM sufferers in the categories of not yet severe, moderate, and severe with the model validation indicator using the smallest Kolmogorov-Smirnov value. Sukun District and Klojen District in the Malang region are two districts that need serious attention from the local government of Malang City in dealing with DM cases. Through the perspective of Bayesian statistical modeling, DM cases in five districts in the Malang area showed a mixture distribution with a different number of mixture components as the basis for regional mapping.
Pengenalan Analisis Statistika untuk Meningkatkan Penelitian dan Publikasi Fungsional Statistisi di Jawa Timur Kartika Fithriasari; Nur Iriawan; Adatul Mukarromah; Irhamah; Heri Kuswanto; Wiwiek Setya Winahju; Ulfa Siti Nuraini
Sewagati Vol 5 No 3 (2021)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.977 KB) | DOI: 10.12962/j26139960.v5i3.90

Abstract

Jabatan fungsional statistisi memiliki tugas utama yaitu melakukan kegiatan statistik. Kegiatan statistik ini termasuk penyediaan data dan informasi statistik serta analisis dan pengembangan statistik. Jabatan fungsional statistisi dalam menjalankan tugasnya, perlunya peningkatan kemampuan dengan mengikuti pelatihan di bidang statistika. Pelatihan ini juga perlu keluaran yang sesuai dengan yang dibutuhkan fungsional statistisi. Oleh karena itu, pengabdian ini bertujuan untuk membantu meningkatkan kompetensi fungsional statistisi pada berbagai instansi di Jawa Timur dalam mengolah data dan publikasi, diharapkan dengan adanya pelatihan ini bisa meningkatkan kebergunaan informasi yang diperoleh agar dapat tersalurkan dengan baik. Materi yang disampaikan yaitu Statistika Data Driven, Visual dan Analisis Data, serta Karya Ilmiah dan Publikasi. Setelah adanya pelatihan, dilanjutkan dengan pendampingan terhadap fungsional statistisi dalam pengolahan data dan pembuatan karya ilmiah yang disusun oleh fungsional statistisi. Selain itu, manfaat yang dapat diperoleh yaitu terjalinnya kerja sama yang baik antara fungsional statistisi di Jawa Timur dan Departemen Statistika di Institut Teknologi Sepuluh Nopember.
Implementasi Model Riset Statistika untuk Peningkatan PTK bagi Kelompok Kerja Pengawas PAI/Madrasah Kankemenag Kabupaten Jombang Irhamah; Nur Iriawan; Adatul Mukarromah; Wiwiek Setya Winahju; Kartika Fithriasari; Pratnya Paramitha O
Sewagati Vol 2 No 2 (2018)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.945 KB)

Abstract

Salah satu kompetensi yang harus dimiliki oleh pengawas PAI/Madrasah berdasarkan Peraturan Menteri Agama Republik Indonesia No.2 tahun 2012 adalah melakukan penelitian dan pengembangan. Metode Statistika memegang peranan penting dalam penyusunan dan pelaksanaan suatu penelitian. Pada umumnya pengawas PAI/Madrasah belum mempunyai dasar Statistika yang kuat. Penulisan karya ilmiah juga hal yang penting dalam pencapaian kompetensi bagi pengawas. Oleh karena itu, pengabdian ini bertujuan untuk memberikan workshop metode statistika ke pengawas PAI/Madrasah di Kementerian Agama Kabupaten Jombang sehingga dapat memberikan hasil Peneltian Tindakan Kelas (PTKp) yang lebih baik setelah mengimplementasikan ilmu statistika didalamnya. Materi yang diberikan antara lain Pendahuluan tentang Statistika dan Penelitian Tindakan Kepengawasan, Sekilas Penulisan Ilmiah dan Penyusunan Dokumen Hasil Penelitian Tindakan Kepengawasan, Statistika Deskriptif dan Estimasi Parameter, Pengujian Hipotesis, Korelasi, Tabulasi Silang dan Analisis Regresi. Setelah workshop, dilakukan pendampingan terhadap penulisan artikel ilmiah hasil PTKp yang disusun oleh pengawas, baik dari segi pengolahan dan analisis data maupun penyajian dalam artikel berupa jurnal maupun seminar nasional/ internasional. Selain itu modul workshop tentang metode statistika untuk PTKp dapat dimanfaatkan oleh peserta pengabdian untuk mengembangkan kemampuan mengolah dan menganalisis data.
Workshop Implementasi Statistika dalam Penelitian Tindakan Kelas di SMKN 5 Surabaya Adatul Mukarromah; Kartika Fithriasari; Nur Iriawan; Irhamah; Heri Kuswanto; Wiwiek Setya Winahju
Sewagati Vol 6 No 6 (2022)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.981 KB) | DOI: 10.12962/j26139960.v6i6.434

Abstract

Salah satu kompetensi yang harus dimiliki oleh guru berdasarkan Peraturan Menteri Pendidikan Nasional Republik Indonesia Nomor 16 Tahun 2007 Tentang Standar Kualifikasi Akademik dan Kompetensi Guru adalah melakukan penelitian dan pengembangan. Metode Statistika memegang peranan penting dalam penyusunan dan pelaksanaan suatu penelitian. Pada umumnya guru belum mempunyai dasar Statistika yang kuat. Penulisan karya ilmiah juga hal yang penting dalam pencapaian kompetensi bagi guru. Oleh karena itu, pengabdian ini bertujuan untuk memberikan workshop metode statistika ke guru di SMKN 5 Surabaya sehingga dapat memberikan hasil Penelitian Tindakan Kelas (PTK) yang lebih baik setelah mengimplementasikan ilmu statistika didalamnya. Materi yang diberikan antara lain Pendahuluan tentang Statistika dan Penelitian Tindakan Keguruan, Sekilas Penulisan Ilmiah dan Penyusunan Dokumen Hasil Penelitian Tindakan Keguruan, Statistika Deskriptif dan Estimasi Parameter, Pengujian Hipotesis, Korelasi, Tabulasi Silang dan Analisis Regresi. Setelah workshop, dilakukan pendampingan terhadap penulisan artikel ilmiah hasil PTK yang disusun oleh guru, baik dari segi pengolahan dan analisis data maupun penyajian dalam artikel berupa jurnal maupun seminar nasional/ internasional. Selain itu modul workshop tentang metode statistika untuk PTK dapat dimanfaatkan oleh peserta pengabdian untuk mengembangkan kemampuan mengolah dan menganalisis data.
Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification Wiyli Yustanti; Nur Iriawan; Irhamah Irhamah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1705-1715

Abstract

The contribution of this study is to offer suggestions for coding techniques for categorical predictor variables and comprehensive test scenarios to obtain significant performance results for imbalanced multiclass classification problems. We modify scenarios in the data mining process with the sample, explore, modify, model, and assess (SEMMA) framework coupled with statistical hypothesis testing to generalize the model performance evaluation conclusions as enhanced-SEMMA. We selected four open-source data sets with unequal class distributions and categorical predictors. Ordinal, nominal, dirichlet, frequency, target, leave one, one hot, dummy, binary, and hashing encoder methods are used. We use the grid-search technique to find the best hyperparameters. The F1-Score and area under the curve (AUC) are evaluated to select the optimal model. In all datasets with 10-fold stratified cross-validation and 95% to 99% accuracy for each dataset, the results show that support vector machine (SVM) outperforms the decision tree (DT) K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), and random forest (RF) algorithms. Probability-based or binary encodings, such as target, Dirichlet, dummy, one-hot, or binary, are best for situations with less than 3% of minor class proportions. Nominal or ordinal encoders are preferred for data with a minor class proportion of more than 3%.
VARIASI VARIABEL PENGARUH FIX DAN RANDOM TERHADAP PRODUKSI GULA DAN TETES I Nyoman Latra; Nur Iriawan; Purhadi Purhadi; Suhartono Suhartono
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 4 No 1 (2012): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2012.4.1.2946

Abstract

This paper presents the modeling of the amount of sugar and molasses production in Pabrik Gula Candi Baru Sidoarjo (PGCBS), East Java, by using multivariate mixedlinear models. Estimation of parameters will be done by using maximum likelihood coupled with restricted maximum likelihood methods. The amount of sugar and molasses products which have strong linear correlation, will be set as responses and are supposed to be affected by seven fixed effect variables and four random effect variables. This paper demonstrates that the seven fixed effect variables and only one random effect variable have significant influence on a single response. In the multivariate response modeling, however, all of variables fail to explain the variability of these two responses simultaneously. It is due to the factors matrix has no full rank. As a result, the model response of molasses can be explained by using a model of the amount of sugar obtained.
Classification of Paddy Growth Phase with Machine Learning Algorithms to Handle Imbalanced Multi-Class Big Data Hady Suryono; Heri Kuswanto; Nur Iriawan
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.45

Abstract

The global Sustainable Development Goals (SDGs) adopted by countries in the world have significant implications for national development planning in Indonesia in the period 2015 to 2030. The Agricultural sector is one of the most important sectors in the world and has a very important contribution to achieving the goals. Availability of accurate paddy production data must be available to measure the level of food security. This can be done by monitoring the growth phase of paddy and predicting the classification of its growth phase accurately and precisely. The paddy growth phase has 6 classes with the number of class members usually not the same (imbalanced data). This study describes the results of the classification of paddy growth phases with imbalanced data in Bojonegoro Regency, East Java in 2019 using machine learning algorithms on the Google Earth Engine (GEE) platform. Classification is done by Classification and Regression Tree, Support Vector Machine, and Random Forest. Oversampling technique is used to deal the problem of imbalanced data. The Area Sampling Frame survey in 2019 conducted by BPS was used as a label for classification model training. The results showed that the overall accuracy (OA) using the Random Forest algorithm by modifying the dataset using oversampling was 82.30% and the kappa statistic was 0.76, outperforming the SVM and CART algorithms.
COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021) Wahyuningsih, Rina; Suharsono, Agus; Iriawan, Nur
Jurnal Bisnis dan Keuangan Vol 8 No 2 (2023): Business and Finance Journal
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/bfj.v8i2.5226

Abstract

The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.
Transfer learning scenarios on deep learning for ultrasoundbased image segmentation Bani Unggul, Didik; Iriawan, Nur; Kuswanto, Heri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3273-3282

Abstract

Deep learning coupled with transfer learning, which involves reusing a pretrained model's network structure and parameter values, offers a rapid and accurate solution for image segmentation. Differing approaches exist in updating transferred parameters during training. In some studies, parameters remain frozen or untrainable (referred to as TL-S1), while in others, they act as trainable initial values updated from the first iteration (TL-S2). We introduce a new state-of-the-art transfer learning scenario (TL-S3), where parameters initially remain unchanged and update only after a specified cutoff time. Our research focuses on comparing the performance of these scenarios, a dimension yet unexplored in the literature. We simulate on three architectures (Dense-UNet-121, Dense-UNet-169, and Dense-UNet-201) using an ultrasound-based dataset with the left ventricular wall as the region of interest. The results reveal that the TL-S3 consistently outperforms the previous state-of-the-art scenarios, i.e., TL-S1 and TL-S2, achieving correct classification ratios (CCR) above 0.99 during training with noticeable performance spikes post-cutoff. Notably, two out of three top-performing models in the validation data also originate from TL-S3. Finally, the best model is the Dense-UNet-121 with TL-S3 and a 20% cutoff. It achieves the highest CCR for training 0.9950, validation 0.9699, and testing data 0.9695, confirming its excellence.
Co-Authors Achmad Nuruddin Syaifulloh Adam Fahmi Fandisyah Adatul Mukarromah Agus Suharsono Ahyudanari, Ervina Akhmad Ridho Ashariansyah Almuhayar, Mawanda Ani Budi Astuti Ani Budi Astuti Anindya Apriliyanti Pravitasari Ari Purwanto Sarwo Prasojo Bani Unggul, Didik Brodjol Sutija Suprih Ulama Dwi Atmono AW Edijatno Edijatno Eko Fajariyanto Entin Hidayah Fithriasari, Kartika Hady Suryono Heri Kuswanto I Nyoman Latra Indira Swa Buana Irhamah Irhamah - Irwan Susanto Jupita Sari Ika Hanugraheni Mahendrawathi Mahendrawathi Maulidiah Nitivijaya Mohammad Naufal Abdullah Muhammad Fajar Nadjadji Anwar Ni Luh Putu Ika Candrawengi Oktaviana, Pratnya Paramitha Pratnya Paramitha O Pratnya Paramitha Oktaviana Probo Prasetya, Andyk Purhadi Purhadi Rina Wahyuningsih, Rina Rinda Nariswari Santi Wulan Purnami Setiawan Sinta Septi Pangastuti Soehardjoepri Soehardjoepri Suci Astutik Suhartono Suhartono Sulhan Sulhan Susi Tri Wahyuni Taufik Azmi Tiza Ayu Virania Ulfa Siti Nuraini Viera Wardhani Wahyuni Suryaningtyas, Wahyuni Widiana Ferriastuti Wijaya, Elizabeth Meiliana Wirajaya Kusuma Wiwiek Setya Winahju Wiyli Yustanti Zainiyah Hilda Paramita