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All Journal International Journal of Applied Sciences and Smart Technologies
Maarip, Syamsul
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Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Prediction of Life Expectancy in Indonesia by Implementing Website-Based Lagrange Polynomial Interpolation Maarip, Syamsul; Hermansyah, Aam; Hadraeni, Sopi Nuryani; Miqdad, Salman; Nuryadin, Ardhan Dimas; Yuliyanti, Siti
International Journal of Applied Sciences and Smart Technologies Volume 06, Issue 2, December 2024
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v6i2.9167

Abstract

Life Expectancy (AHH) is a measurement of the average human lifespan accepted and used to assess the quality of health and welfare of a country's population. Accepted to develop a prediction system that can be easily accessed by the general public via a web platform. The method used to predict is the Lagrange polynomial interpolation method. The Lagrange polynomial interpolation method was chosen because it can model irregular numerical data with a fairly high level of accuracy. The data used to predict AHH comes from the Indonesian Central Statistics Agency (BPS). Known data on life expectancy in Indonesia for men from 2020 to 2023 shows 69.59, 69.67, 69.93 and 70.17. Predictions for 2024, 2025 and 2026 respectively show 70.19, 69.79, 68.77 with a Root Mean Squared Error result of 0.085875 or around 8.58% of the total data tested. The results of implementing the Lagrange polynomial interpolation method into an application in the form of this website show that this method is able to provide accurate predictions for life expectancy in Indonesia and can make it easier to use.Keywords: Interpolasi, Polinom Langrange, Life Expectancy, prediction, lifespan
Clustering and Trend Analysis of Priority Commodities in the Archipelago Capital Region (IKN) using a Data Mining Approach Pangestu, Pandu; Maarip, Syamsul; Addinsyah, Yuldan Nur; Purwayoga, Vega
International Journal of Applied Sciences and Smart Technologies Volume 06, Issue 1, June 2024
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v6i1.7798

Abstract

The policy of moving the capital from Jakarta to East Kalimantan planned by the President of the Republic of Indonesia Joko Widodo has caused a lot of polemic among the public. There are quite a few positive and negative comments on social media regarding the policy of moving the capital. The process of moving the capital requires careful preparation. One thing that needs to be considered is food security in IKN. This research provides recommendations for the main food commodities in IKN by applying data mining. We collect food productivity data available on the official website for East Kalimantan province. These data are processed and grouped into two groups, namely horticulture and livestock products using the K-Means method. After grouping, we predict the increase in productivity of each group using the ARIMA method. This research produces output in the form of grouping commodities into horticulture and livestock products. Productivity results for each type of commodity are displayed from 2016 to 2020 based on data on the official East Kalimantan Province website. Based on this data, predictions are made using the ARIMA method to predict productivity results from 2021 to 2025. Commodities with total productivity are grouped into high-priority commodities. Grouping the amount of productivity is carried out using the clustering method by comparing the amount of productivity for each commodity and producing commodities that are low priority, middle priority, priority and top priority based on the highest to lowest productivity numbers. The cluster quality for grouping horticultural commodities is 99.1%, while the cluster quality for grouping livestock commodities is 87.5%. Hasil prediksi terbaik yaitu ketika memprediksi produksi salak dan slaughter cattle dengan model ARIMA (0, 1, 0) dan ARIMA (2, 2, 2).
Co-Authors Addinsyah, Yuldan Nur Hadraeni, Sopi Nuryani Hermansyah, Aam Miqdad, Salman Nuryadin, Ardhan Dimas Pangestu, Pandu Purwayoga, Vega Siti Yuliyanti, Siti