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All Journal REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Alfin Alfarizi
Universitas Islam Negeri Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

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Discrete Event Simulation untuk Analisis Sistem Antrian Konsumen Outlet Mixue Simpang Tuntungan Marini; Alfin Alfarizi; Dinda Ayu Ningsih; Fajar Al Fahri
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15819

Abstract

Pelayanan yang efisien merupakan faktor penting dalam meningkatkan kepuasan pelanggan pada industri minuman cepat saji. Mixue Simpang Tuntungan termasuk outlet dengan intensitas kunjungan tinggi sehingga berpotensi menimbulkan antrean pada proses pemesanan dan pembayaran. Penelitian ini bertujuan untuk menganalisis kinerja pelayanan kasir dan memberikan rekomendasi perbaikan sistem antrean menggunakan pendekatan pemodelan dan Discrete Event Simulation (DES). Data dikumpulkan melalui observasi langsung terhadap 30 pelanggan, dengan parameter waktu antar kedatangan, waktu pelayanan kasir, proses penyajian, serta waktu tunggu pelanggan. Pemodelan sistem menggunakan model antrian M/M/1 untuk kondisi aktual dan M/M/2 sebagai skenario usulan. Simulasi dilakukan menggunakan Python melalui 10 replikasi untuk memperoleh rata-rata performa sistem. Hasil penelitian menunjukkan bahwa sistem pelayanan dengan satu kasir masih efisien, dengan waktu tunggu 0,59 menit dan tingkat utilisasi 30,12%. Penambahan satu kasir mampu menurunkan waktu tunggu hingga 91% menjadi 0,055 menit, namun menurunkan utilisasi menjadi 15,85% per kasir sehingga tidak optimal pada kondisi trafik pelanggan normal. Dengan demikian, penambahan kasir disarankan hanya diterapkan pada kondisi puncak, sedangkan pada kondisi normal cukup menggunakan satu kasir. Pendekatan DES terbukti efektif dalam mengevaluasi performa pelayanan dan menghasilkan rekomendasi operasional berbasis data.
Classification of Herbal Plants Based on Leaf Images Using Gray Level Co-Occurrence Matrix and K-Nearest Neighbor Fahmi Nur Alimsyah Purba; Fathi Athallah Z; Alfin Alfarizi; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2291

Abstract

Herbal plants have long been used as traditional medicine. However, many people struggle to tell different herbal leaves apart because they look quite similar. This study tries to build a system that can recognize two types of herbal leaves, Moringa and Katuk, simply from their photos. We used GLCM to extract texture features from the leaves, then classified them using KNN. The dataset came from Kaggle, with 480 leaf images in total. Before processing, we cropped the images, resized them to 256x256 pixels, and converted them to grayscale. GLCM features were taken from four angles (0°, 45°, 90°, 135°) and then averaged. This gave us four texture values: contrast, correlation, energy, and homogeneity. We tested KNN with k values from 1 to 15 and five different distance metrics. The best result we got was 94% accuracy, using Manhattan distance with k=1. This system could help everyday people identify medicinal plants more easily without needing lab tests.
Comparative Analysis of Sobel, Prewitt, and Canny Methods in Detecting Object Edges in Betta Fish Images Alfin Alfarizi; Cici El Dirrah Syafitri Simanungkalit; Fahmi Nur Alimsyah Purba; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2293

Abstract

Edge detection is a crucial stage in digital image processing for recognizing the shape and structure of an object. The application of edge detection to betta fish images presents a unique challenge due to their layered, intricately textured, and often semi-transparent fin morphology. This study aims to analyze and compare the performance of three edge detection algorithms, namely Sobel, Prewitt, and Canny, in extracting shape features from betta fish images. The research methodology involved converting the dataset images into a grayscale format and subsequently implementing the three algorithms using the OpenCV library in the Python programming language. The evaluation was conducted visually by observing the sharpness of the edge lines, object continuity, and the occurrence of noise. The results indicate that the Canny algorithm provides the most optimal performance, as it is capable of detecting the thin edge lines of the fish fins with greater detail and continuity due to its hysteresis thresholding process. Meanwhile, the Sobel and Prewitt methods produced thicker edge lines but were less sensitive to the details of the transparent fins. This study is expected to serve as a reference in selecting the appropriate segmentation method for biological objects with complex morphologies.
Co-Authors Cici El Dirrah Syafitri Simanungkalit Dinda Ayu Ningsih Fahmi Nur Alimsyah Purba Fajar Al Fahri Fathi Athallah Z Lailan Sofinah Harahap Marini