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All Journal FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Statistika Journal of Tropical Soils Journal of the Indonesian Mathematical Society Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications
Mohammad Masjkur
Department Of Statistics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Other

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Comparison of Chi-Square Automatic Interaction Detector (CHAID) and Random Forest Methods in the Classification of Household Poverty Status in Central Java: Perbandingan Metode Chi-Square Automatic Interaction Detector (CHAID) dan Random Forest dalam Klasifikasi Status Kemiskinan Rumah Tangga di Jawa Tengah Fatkhul Izzati; Mohammad Masjkur; Farit Mochamad Afendi
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i1p1-13

Abstract

Central Java was in second position as the province with the highest number of poor people in Indonesia in March 2020. Poverty alleviation efforts have been carried out, but many are still not yet on target. The purpose of this study was to model the classification of household poverty status in Central Java using CHAID and random forest methods and compare the two methods. The data used in this study is data from the 2020 National Socioeconomic Survey (SUSENAS) conducted by the Central Bureau of Statistics (BPS) for Central Java. The number of poor households is much less than non-poor households. Therefore, Synthetic Minority Oversampling Technique (SMOTE) was performed to handle unbalanced data. The random forest method produced better classification performance than the CHAID method with accuracy, sensitivity, specificity, and AUC of 93,95%, 98,43%, 89,92%, and 0,9417, respectively. The important variables that build the random forest model are the floor area of the house, the age of the head of the household, cooking fuel, the place for the final disposal of feces, and ownership of the place to defecate.
Grouping Provinces in Indonesia Based on the Causes of Stunting Variables using Hierarchical Clustering Analysis: Pengelompokan Provinsi di Indonesia Berdasarkan Peubah Penyebab Stunting Menggunakan Analisis Cluster Hierarki Detia Meilani; Mohammad Masjkur; Farit M Afendi
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i1p32-43

Abstract

Stunting is a condition due to chronic malnutrition that causes children to be shorter in height compared to their age. The prevalence of stunting in Indonesia still exceeds the standards set by WHO. This study aims to classify provinces in Indonesia based on the characteristics of the causes of stunting. Cluster analysis is a statistical method used to group objects with similar characteristics. Province grouping is done using hierarchical cluster analysis consisting of Single Linkage, Complete Linkage, Average Linkage, Ward's method, and Centroid method. The Cophenetic correlation coefficient was used to determine the best cluster method and the optimal number of clusters using the Silhouette coefficient. The results show that the centroid method has the highest Cophenetic correlation coefficient with four clusters. The first cluster consists of 1 province with low stunting characteristics, the second cluster consists of 3 provinces with high stunting characteristics, the third cluster consists of 22 provinces with very high stunting characteristics, and the fourth cluster consists of 8 provinces with moderate stunting characteristics.
Acne Severity Classification Study Using Convolutional Neural Network Algorithm with MobileNetV2 Architecture: Kajian Klasifikasi Tingkat Keparahan Jerawat Menggunakan Algoritma Convolutional Neural Network Faadiyah Ramadhani; Septian Rahardiantoro; Mohammad Masjkur
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p112-128

Abstract

Data classification is a key technique in machine learning that maps patterns and features of input data into a target class. Significant developments in data classification occur in deep learning with neural networks and Convolutional Neural Networks (CNN) that are able to extract image features automatically. CNN can classify the level of a condition based on image data, one of which is the severity of acne. Acne (acne vulgaris) is a common skin disease with varying severity. This study aims to apply the CNN MobileNetV2 model to classify acne severity based on acne input images. The data consists of 1457 acne images at 4 severity levels divided into 80% training data and 20% test data. MobileNetV2 was used as a feature extractor through transfer learning. Fine-tuning and classification were performed using fully connected layers with ReLU and softmax activation functions. The model was evaluated with a confusion matrix and classification report. The model with a combination of hyperparameter batch size 16 and a learning rate of 0.00001 was the best model that achieved 87.29% accuracy with 89% precision, 84% recall, and 86% F1 score for classifying acne severity.
Co-Authors . Aunuddin . Erfiani Akbar Rizki Anistia Iswari annida marsa salsabila Arini Annisa Adi Bambang Sumantri Budi Waryanto Dea Fisyahri Akhilah Putri Detia Meilani Dinar Arga Prasetyo Erfiani Erfiani Faadiyah Ramadhani Farit M Afendi Farit Mochamad Afendi Fatkhul Izzati Febie Tri Lestari Folmer, Henk Henk Folmer Ibrahim Arif Muhammad Indahwati Indahwati Kusman Sadik M. Sjarkani Musa Maman Rusman Mutaqin, Ahdan Darul Niken Dyah Septiastuti Oetit Koswara Ragsa Endahas Ahmad Rahardiantoro, Septian Rahma Anisa Ririn Fara Afriani Septian Rahardiantoro Siti Raudlah Utami Dyah Syafitri Yenni Angraini