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All Journal International Journal of Applied Sciences and Smart Technologies Jurnal Teknik Informatika (JUTIF)
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).
Multi-Class Real-Time Color Classification of Coffee Beans via Fine-Tuned EfficientNetB0 and Post-Training Quantization Yuliyanti, Siti; Maarip, Syamsul
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5400

Abstract

The first problem faced in coffee bean classification is that the manual grading or sorting process still relies heavily on human labor, making it subjective, time-consuming, and prone to errors. Secondly, existing deep learning-based systems often require substantial computing resources, rendering them inefficient for industrial-scale implementation or on limited hardware. The research objective is to develop an efficient, lightweight, and accurate automatic classification model to recognize coffee bean color and support the automation of quality control processes in the coffee post-harvest chain. This study develops an automated system for coffee bean classification based on four color classes: light, medium, green, and dark, utilizing the lightweight EfficientNet model with fine-tuning of smaller versions of EfficientNet (B0–B3). The research stages consist of dataset acquisition, pre-processing, modeling and fine-tuning, as well as model evaluation on the detection system on low-end devices. The main innovation of this research is the efficiency and speed of real-time classification of coffee bean color images using a lightweight CNN model optimized through quantization, which supports field applications with hardware limitations without sacrificing accuracy. Fine-tuning EfficientNetB0 by unfreezing the last 30 layers achieved 97.17% training accuracy and 99.25% validation accuracy with consistent loss reduction, supported by Test-Time Augmentation (TTA) which improves prediction stability to >80% confidence against variations in field image quality. Deployment to TensorFlow Lite (TFLite) with 8-bit quantization resulted in a lighter model that maintained 99.50% accuracy and accelerated inference by up to 6x compared to the original H5 model, and excelled at multi-object detection without sacrificing classification confidence.
Co-Authors Addinsyah, Yuldan Nur Hadraeni, Sopi Nuryani Hermansyah, Aam Miqdad, Salman Nuryadin, Ardhan Dimas Pangestu, Pandu Purwayoga, Vega Siti Yuliyanti, Siti