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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Terapan Abdimas JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Jurnal Desain dan Analisis Teknologi Bulletin of Social Informatics Theory and Application
Dwi Ely Kurniawan
Politeknik Negeri Batam
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method Permatasari, Ismi Aprilianti; Dermawan, Budi Arif; Maulana, Iqbal; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3279

Abstract

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage (F&B) Transactions Marselina, Sonia; Jaman, Jajam Haerul; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.5026

Abstract

The current business landscape has compelled many companies to compete in boosting their company's revenue, particularly in the F&B sector. Existing sales transaction data has not been fully maximized in determining the business strategy of companies. Therefore, the implementation of data mining is necessary to analyze and explore available data to discover new information that is more beneficial for the company. In this study, we analyze sales transaction data using the a priori algorithm method because this algorithm efficiently handles the data mining process on a large scale with a substantial amount of data. The results of this study indicate that the formed association rules can determine patterns of product purchases that are frequently bought together. The established association rules successfully combine sales transaction data into two-item combinations, namely green tea latte and french fries, with a support value of 16% and a confidence level of 83%. These rules can be used as a reference in determining the company's business strategy.
Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024 Vonega, Defangga Aby; Fadila, Aminudin; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4300

Abstract

Twitter can be seen as a platform for candidates and users to gain substantial reach to show their views on who the president will be elected to in 2024. The aim of this study is to explore contrasting information over time regarding whether Puan Maharani can be one of the candidates. The best according to the Indonesian people. In this study, sentiment analysis was carried out using the text mining method and several libraries such as TextBlob, VaderSentiment, and SentiWordNet to retrieve and classify the polarity of opinions from data that had been crawled. In the dataset generated with the keyword "Puan Maharani" The average negative sentiment is only 0.1%, neutral sentiment is 97.25, and positive sentiment is 2.55%. It can be concluded that Twitter users tend to be neither aggressive nor defensive in discussing issues leading to the candidacy of Puan Maharani in the upcoming 2024 Indonesian presidential election.
Development of an IoT-Based Smart Cane with Non-Invasive Health Monitoring for Elderly Care in Batam Putera, Dimas Akmarul; Adi, Roni; Kurniawan, Dwi Ely; Leman, Abdul Mutalib; Raynold, Raynold
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11107

Abstract

The rapid growth of the elderly population requires assistive technologies that support mobility, health, and safety. This study presents the development of an IoT-based smart cane designed to enhance elderly independence and health monitoring in Batam, Indonesia. The prototype integrates non-invasive health sensors (MAX30102 for heart rate and SpO₂, MLX90614 for temperature, and a non-invasive glucose sensor), a GPS module, a mini-CCTV with two-way audio, and a solar-powered energy system, all controlled by an ESP32 microcontroller connected to the Blynk IoT platform. Ergonomic design was guided by anthropometric data of Indonesian elderly to ensure user comfort and usability. Experimental results demonstrated stable performance of the integrated modules. Heart rate values ranged from 86–103 BPM (mean 89.5 ± 6.2 BPM), blood glucose estimations from 110–112 mg/dL (mean 111 ± 0.9 mg/dL), and body temperature from 36.9–37.1 °C (mean 37.0 ± 0.1 °C), all of which aligned closely with clinical references. Oxygen saturation readings, however, averaged 89 ± 0.8%, slightly below the clinical norm (≥95%), highlighting the need for sensor calibration. Dynamic testing of the GPS module across a 500-meter route achieved positional accuracy within 3–5 meters, while the CCTV system successfully streamed live video but was dependent on WiFi stability.The novelty of this research lies in the unique combination of locally adapted ergonomic design, multi-sensor non-invasive health monitoring, two-way visual and audio communication, GPS tracking, and renewable energy integration within a single portable device. These contributions not only enrich IoT-based healthcare research but also provide practical solutions tailored to elderly care in Indonesia. Future work will focus on clinical-grade validation of sensors, extended field trials, and the integration of predictive analytics using Machine Learning and Fuzzy Logic.
Co-Authors Adi, Roni Adian Fatchur Rohim Afdhol Dzikri, Afdhol Arnomo, Sasa Ani Budi Arif Dermawan Dimas Akmarul Putera Dzikri , Afdhol Fadila, Aminudin Herman, Nanna Suryana Iqbal Maulana Jaman, Jajam Haerul John Friadi Kusworo Adi Leman, Abdul Mutalib Marselina, Sonia Narupi Narupi, Narupi Permatasari, Ismi Aprilianti Raynold, Raynold Resda, Dodi Prima Thohari, Ahmad Hamim Vonega, Defangga Aby