Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal of Advanced in Information and Industrial Technology (JAIIT)
Amri Muhaimin
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Spatial Autocorrelation Analysis of East Java Stunting Prevalence Cases in 2023 Trimono; Amri Muhaimin; Ekacitta, Puti Cresti; Ardiani, Ardia Eva
Journal of Advances in Information and Industrial Technology Vol. 7 No. 1 (2025): May
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i1.689

Abstract

Stunting is one of the chronic nutritional problems occur in East Java. In 2022, the percentage of stunting in East Java reached 19.2% and decreased to 17.7% in 2023. The less significant decrease occurred due to various factors, including malnutrition, poor sanitation, and environmental influences. This study will analyze the spatial influence on the prevalence of stunting in East Java, especially in 2023. The methods used include the Morans Index and the Local Indicator of Spatial Association (LISA). Spatial correlation analysis will help in determining the pattern of regional grouping based on stunting cases. This model works by testing whether the values of a variable at a location are related to the values of the same variable at neighboring locations, with the nature of the relationship being positive (clustering) or negative (dispersion). Using stunting prevalence data in 2023, the Moran Index = 0.3233 was obtained with a Zvalue = -1.0776. This value indicates that there is positive spatial autocorrelation, but is not significant enough. Then, through the Moran Scatterplot analysis, there are indications of regional grouping in four spatial quadrants. The results of the LISA analysis show that there are five cities/regencies included in the High-High cluster (Jember, Probolinggo City, Lumajang, Malang, and Probolinggo), one area in the Low-High cluster (Situbondo), and one area in the Low-Low cluster (Gresik). These findings indicate the existence of a spatial concentration of stunting problems that can be used as a basis for developing appropriate handling strategies by the provincial government based on regions.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Damaliana, Aviolla Terza; Muhammad Nasrudin; Riyantoko, Prismahardi Aji; Nabilah Selayanti; Putri, Shafira Amanda
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
Co-Authors Ardiani, Ardia Eva Damaliana, Aviolla Terza Muhammad Nasrudin Nabilah Selayanti Puti Cresti Ekacitta Putri, Shafira Amanda Riyantoko, Prismahardi Aji Trimono