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All Journal Prima Abdika: Jurnal Pengabdian Masyarakat Journal of Information Technology
Abdi Pandu Kusuma
Universitas Islam Balitar, Blitar, Indonesia
Arts Humanities Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pemberdayaan Kelompok Masyarakat Suryasari dalam meningkatkan pelayanan dan pemasaran Agrowisata Petik Belimbing melalui Model Eduwisata STAR (Sharing, Trying, and Adventuring) berbasis produk Nata de Averrhoa carambola Hesty Puspita Sari; Mar’atus Sholihah; Abdi Pandu Kusuma
Prima Abdika : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2022): Volume 2 Nomor 3 Tahun 2022
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v2i3.2092

Abstract

Karangsari has a starfruit picking agrotourism which is managed by the Suryasari community service. The Agrotourism of starfruit provided visitors a very simple and less varied service. The most common package taken by visitors is the starfruit picking package. Meanwhile, the implementation of the education package is still not optimal because the education provided is only a brief explanation about the cultivation of star fruit that is given while walking around the garden while picking the starfruit. Priority partners problems arises are less than optimal services in Agrotourism, especially in education packages, lack of partner skills in processing star fruit, especially star fruit that is overripe or slightly defective (damaged), and not yet optimal digital marketing so that the number of visitors is still limited from the area. local. The purpose of this activity is to empower the Suryasari community service in improving agro-tourism services and marketing through the product-based STAR edutourism model to improve tourism services in Karangsari Belimbing Picking Agrotourism. The methods applied in this activity include: (a) socialization and discussion about the product-based STAR edutourism model, (b) training on how to become an edutourism guide indoors and outdoors, (c) training on making nata de Averhoa carambola, (d) training in technical- digital marketing techniques, and (e) assisting partners in implementing product-based STAR edutourism packages to digital marketing. The results of this service could increase the partner's quality to become indoor and outdoor edutourism guides, increase the partner's ability to process starfruit into nata, and increase the partner's ability in digital marketing as shown by the results of partner responses to training activities that are quite good with 95% reaction level indicators, learning level 87%, behavior level 83%, and result level 80%. PkM activities also increase digital marketing activities by partners with the Karangsari Picking starfruit Agrotourism website. This activity has a positive impact on the Suryasari community service so that it is more qualified and able to provide maximum service for visitors to Karangsari starfruit picking agrotourism, Blitar, East Java.
Clustering of Myopia Patients Based on Severity Level, Geographic Area, Age, and Gender Using the K-Means Algorithm: A Case Study of Indra Optik Blitar Ayuna Rahim Imannur; Abdi Pandu Kusuma; Wahyu Dwi Puspitasari
J-INTECH ( Journal of Information and Technology) Vol 14 No 03 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i03.2455

Abstract

Indra Optik Blitar manages a large volume of patient medical record data that has not yet been utilized to support strategic decision-making. This study aims to cluster myopia refractive error patients based on their severity level, region, age, and gender characteristics using the K-Means algorithm, and to evaluate the quality of the resulting clusters using the Silhouette Coefficient. The research process follows the Knowledge Discovery in Database (KDD) framework, encompassing data selection, preprocessing, feature engineering, Min-Max Scaling normalization, and clustering execution. From 14,863 raw data entries, 6,821 valid records were obtained after three filtering layers: demographic, geographic, and clinical. Feature engineering was performed by converting four raw refraction variables into a single Worst_SE (Worst Spherical Equivalent) variable and transforming district address data into latitude and longitude coordinates. Feature weighting was automatically optimized using the Optuna framework with the Tree-structured Parzen Estimator (TPE) algorithm through 100 optimization trials. The optimal number of clusters was determined using a combination of the Elbow Method and Silhouette Coefficient, resulting in K = 5. The K-Means algorithm with K-Means++ initialization achieved convergence at iteration 20, producing five clusters with distinctive characteristics. Evaluation using the Silhouette Coefficient yielded an average score of 0.4144, which, given the high dimensionality and heterogeneity of the combined clinical, demographic, and geographic features, represents a moderately well-separated cluster structure suitable for practical application. The five clusters formed represent patient segments that differ clinically, demographically, and geographically, providing a data-driven foundation for Indra Optik Blitar to design more targeted marketing strategies, eye health socialization programs, and social assistance initiatives.  
Co-Authors Ayuna Rahim Imannur Sari, Hesty Puspita Sholihah, Mar'atus Wahyu Dwi Puspitasari