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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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Application of Univariate and Multivariate Long Short Term Memory for World Crude Palm Oil Price Prediction : Penerapan Long Short Term Memory Peubah Tunggal dan Ganda untuk Prediksi Harga Minyak Kelapa Sawit Dunia Izzany, Nabil; Masjkur, Mohammad; Rizki, Akbar
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, 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.v9i1p10-20

Abstract

Time series analysis is essential for predicting economic and other important factors; it can be done univariately or multivariately. Technological developments created long short term memory that can handle vanishing gradients and long-term dependencies. This research will predict the world price of crude palm oil because Indonesia, as the world's largest crude palm oil producer, is strongly influenced by the world crude palm oil price. This study uses monthly data on crude palm oil, soybean oil, and crude oil prices from January 2002 to May 2024 obtained from the World Bank Commodity Price Data. This research applies univariate and multivariate long short term memory to predicting crude palm oil prices. The use of long short term memory is because the data shows non-linear elements and high volatility. The input used for univariate long short term memory is the crude palm oil price, while multivariate long short term memory uses crude palm oil, soybean oil, and crude oil prices. The univariate long short term memory proved to be more effective in the case of world crude palm oil price prediction. This is proven by the lower mean absolute percentage error of 6,574% compared to the multivariate long short term memory of 6,689%. This univariate long short term memory uses a combination of hyperparameters: neuron 32, epoch 100, time steps 1, batch size 64, and learning rate 0,01.
Correlation between Soil Test Phosphorus of Kaolinitic and Smectitic Soils with Phosphorus Uptake of Lowland Rice Masjkur, Mohammad
JOURNAL OF TROPICAL SOILS Vol. 14 No. 3: September 2009
Publisher : UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5400/jts.2009.v14i3.205-209

Abstract

Correlation between soil test phosphorus (P) and plant-available P parameters were affected by soil properties, such as soil pH, particle-size composition, and mineralogy. The objectives of this research were: (1) to determine P concentration extracted by several soil P test method in kaolinitic and smectitic soil, and (2) to determine correlation between soil P test and soil properties, P fractions, P sorption parameters, and P uptake of lowland rice. The soil P test in kaolinitic and smectitic soil used solutions of HCl 25%, Truog, Olsen, Bray1, Mehlich1, and Morgan Venema and were correlated with P uptake of lowland rice in field experiment. Concentration of  Truog-P in kaolinitic soil was significantly higher than smectitic soil, while concentration of  Morgan-P in kaolinitic soil was significantly lower than smectitic soil. Concentration differences of HCl 25%-P, Olsen-P, Bray1-P, and Mehlich1-P between kaolinitic and smectitic soil were not significant.  In kaolinitic soil correlation between HCl 25%-P, Olsen-P, Bray1-P, and Mehlich1-P, and Morgan-P with P uptake of lowland rice were not significant. In smectitic soil HCl 25%-P, Olsen-P, Bray1-P, and Mehlich1-P correlated significantly with P uptake of lowland rice, while Morgan-P was not significant.
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 Izzati, Fatkhul; Masjkur, Mohammad; Afendi, Farit Mochamad
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, 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 Meilani, Detia; Masjkur, Mohammad; Afendi, Farit M
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, 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 Ramadhani, Faadiyah; Rahardiantoro, Septian; Masjkur, Mohammad
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, 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.
Application of the Spatial Durbin Panel Model and Geographically Weighted Panel Regression on Poverty Data in West Java Province Anis Sulistiyowati; Masjkur, Mohammad; Budi Susetyo
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
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.v9i2p240-260

Abstract

Poverty is one of the priority issues in the Sustainable Development Goals. In 2024, West Java Province became the province with the second-highest number of people living in poverty in Indonesia. This study aims to identify the variables that significantly affect the percentage of people living in poverty in districts/cities of West Java Province from 2019 to 2023, using the spatial Durbin panel model and geographically weighted panel regression. The data used is secondary data on poverty indicators in West Java Province from 2019 to 2023, sourced from Statistics Indonesia of West Java. The spatial Durbin panel model developed in this study is a fixed-effects spatial Durbin panel model. The model shows that average years of schooling and expenditure per capita have significant effects. In addition, the spatial lags of the percentage of households living in appropriate housing, the percentage of the population covered by local health insurance, and average years of schooling also have significant effects. The geographically weighted panel regression model, estimated using a fixed effect panel regression with a Gaussian fixed kernel as the optimal weighting function, produces distinct models for each region. The average year of schooling is the dominant factor influencing the percentage of people living in poverty in districts/cities in West Java Province.
Co-Authors . Aunuddin . Erfiani Afendi, Farit M Akbar Rizki Anggrahini, Ervina Dwi Anis Sulistiyowati Anistia Iswari annida marsa salsabila Arini Annisa Adi Bambang Sumantri Budi Susetyo Budi Waryanto Dea Fisyahri Akhilah Putri Dinar Arga Prasetyo Erfiani Erfiani Farit Mochamad Afendi Febie Tri Lestari Folmer, Henk Henk Folmer Ibrahim Arif Muhammad Indahwati Indahwati Izzany, Nabil Izzati, Fatkhul Kusman Sadik M. Sjarkani Musa Maman Rusman Meilani, Detia Mutaqin, Ahdan Darul Niken Dyah Septiastuti Oetit Koswara Ragsa Endahas Ahmad Rahardiantoro, Septian Rahma Anisa Ramadhani, Faadiyah Ririn Fara Afriani Rizki, Akbar Siti Raudlah Utami Dyah Syafitri Yenni Angraini