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All Journal International Journal of Advances in Intelligent Informatics Ilmu Komputer untuk Masyarakat Jurnal Informatika
Lahuddin, Harlinda
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Computer Science & IT Other

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Pengenalan Media Pembelajaran Online pada Sekolah SMP PGRI Getengan Lahuddin, Harlinda; Satra, Ramdan
Ilmu Komputer untuk Masyarakat Vol 4, No 1 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v4i1.1549

Abstract

Lembang Marinding Kecamatan Mengkendek Tana Toraja merupakan salah satu desa binaan Universitas Muslim Indonesia. Lokasi pengabdian yang akan kami laksanakan yaitu salah satu SMP yang ada di lembang marinding yaitu SMP PGRI MARINDING MENGKENDEK yang berada dibawah Yayasan YPLP-PGRI, SMP PGRI MARINDING MENGKENDEK. SMP PGRI ini adalah sekolah Swasta dengan jenjang SMP yang beralamat di Marinding Kec. Mengkendek Kab. Tana Toraja Prov. Sulawesi Selatan. Pengabdian yang akan dilakukan dengan menerapkan teknologi informasi dalam proses belajar mengajar yang diharapkan dapat memudahkan dalam proses penyampaian materi pembelajaran dari guru ke siswa. Adapun teknologi yang akan gunakan yaitu learning manajemen system menggunakan google classroom, kemudian upload video pembelajaran menggunakan youtube dan juga pembelejaran jarak jauh menggunakan google meet. Pengabdian ini menghasilkan beberapa luaran diantaranya adanya draft publikasi pada jurnal ILKOMAS dan publikasi ke media masa online.
A comprehensive comparative analysis of chicken meat classification techniques through machine learning models Anraeni, Siska; Lahuddin, Harlinda; Ramdaniah, Ramdaniah; Melani, Erika Riski; Amalia, Andi Cici; Amaliah, Tazkirah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2014

Abstract

This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken and the Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification.
Data Mining Approach to Improve Minimarket Sales using Association Rule Method Lahuddin, Harlinda; Satra, Ramdan
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12249

Abstract

This research aims to provide recommendations for the placement of goods sold by the UMI Faculty of Computer Science mini supermarket. A data mining approach is used to determine the position of sales items between related items. This is done to make it easier for customers to search for items to buy based on the type of item. Another problem is determining the best-selling items and also determining the types of items that will receive promotions. The data mining approach uses association rules with a priori algorithms. Association rule mining is a data analysis technique used to find patterns and relationships in big data. This technique is widely used in business to help optimize marketing and sales strategies. The results of the rule association using an a priori algorithm show that if consumers buy 200 milli of Ultra Milk Slim Chocolate, they also buy 600 milli of LE MINERAL with a support value of 10% and confidence of 60%. This shows that these two items are related when consumers purchase.
Co-Authors Amalia, Andi Cici Amaliah, Tazkirah Melani, Erika Riski Ramdan Satra Ramdaniah, Ramdaniah Siska Anraeni