Ibrahim Mohammed Sulaiman
Institute of Strategic Industrial Decision Modelling (ISIDM), School of Quantitative Sciences, Universiti Utara Malaysia, UUM Sintok, 06010, Kedah, Malaysia

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Forecasting Human Development Index With Double Exponential Smoothing Method And Acorrect Determination Agum Surya Ramadhan; Agung Prabowo; Rabiu Hamisu Kankarofi; Ibrahim Mohammed Sulaiman
International Journal of Business, Economics, and Social Development Vol 4, No 1 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v4i1.375

Abstract

Human development is now seen as a measure of the success of the development of a nation which is closely related to the economic, social, cultural, political and environmental fields. The success of human development is measured based on the Human Development Index (IPM). Boyolali Regency is one of the regencies in Central Java Province which has diverse and abundant natural resources. The large potential of natural resources owned should be in line with the quality of human development. However, it turns out that this is not in line with the HDI value of Boyolali Regency which is still below the average HDI value of Central Java. So that the Boyolali government continues to strive to maximize the potential and increase the HDI value. Based on this, it is necessary to do forecasting as a reference to maximize the level of human development in Boyolali Regency in the next few years. In this study, HDI forecasting in Boyolali Regency was carried out using the Double Exponential Smoothing method from Brown with the data used is HDI data in Boyolali Regency from 2011 to 2021. The data used was obtained from the Central Bureau of Statistics (BPS) Boyolali Regency. HDI forecasting was also carried out using the arithmetical method, and the best forecasting results were compared between the two methods based on the mean absolute percentage error (MAPE). Forecasting results using the Double Exponential Smoothing method produce the best alpha smoothing parameter values of 0.91 and MAPE values of 0.4061%. Meanwhile, using the arithmetic series method, the MAPE is 0.4704%. Both methods produce MAPE values with very good criteria, so that both methods can be used for forecasting. However, based on the criteria for the smallest MAPE value, the Double Exponential Smoothing method is used. The results of the HDI forecasting using the Double Exponential Smoothing method for 2022, 2023 and 2024 are 74.61, 74.81 and 75.02 respectively. While the results of forecasting with arithmetical method for the same years are 74.93, 75.45, and 75.98.
Development of Smart Farming Technology on Ginger Plants in Padamulya Ciamis Village, West Java, Indonesia Aceng Sambas; Mujiarto Mujiarto; Gugun Gundara; Gunawan Refiadi; Neneng Sri Mulyati; Ibrahim Mohammed Sulaiman
International Journal of Research in Community Services Vol 4, No 3 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v4i3.483

Abstract

In this paper, we present a comprehensive study aimed at enhancing the cultivation of ginger plants through the integration of smart farming technology. Ginger (Zingiber officinale) is an essential crop in the agriculture-based economy of Indonesia, providing numerous health benefits and culinary applications. However, traditional farming methods often face challenges such as inefficiencies, resource wastage, and unpredictable yields. The research conducted in Padamulya Ciamis Village seeks to address these issues by harnessing the potential of smart farming technology. The study involves the implementation of cutting-edge agricultural tools, including Internet of Things (IoT) devices, sensor networks, and data analytics. By utilizing these advancements, the project aims to optimize the cultivation process, ensure sustainable resource management, and enhance overall productivity. The methodology of the research encompasses a mix of experimental trials, data collection, and analysis. Smart sensors are deployed to monitor critical variables such as soil moisture, temperature, humidity, and light intensity, enabling farmers to gain real-time insights into their ginger fields. The collected data is processed using machine learning algorithms, providing predictive models and personalized recommendations for cultivation practices. The results of this study demonstrate promising advancements in ginger farming practices. By implementing smart farming technology, farmers in Padamulya Ciamis Village experience optimized irrigation schedules, precise nutrient delivery, and timely pest control measures, leading to increased crop yields and improved quality. Furthermore, resource utilization efficiency is enhanced, minimizing water and fertilizer wastage, contributing to the sustainable and eco-friendly management of ginger plantations. Beyond its local implications, this research showcases the potential of smart farming technology as a transformative force in agriculture. The findings serve as a foundation for scaling up similar projects in other regions of Indonesia and beyond, contributing to the nation's agricultural modernization and food security. Finally, the development of smart farming technology on ginger plants in Padamulya Ciamis Village presents a promising pathway towards sustainable and efficient agricultural practices. By combining traditional farming knowledge with cutting-edge technology, this study exemplifies how smart farming can elevate crop cultivation, empower farmers, and foster rural development in Indonesia.