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Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Nahdlatul Ulama Sumatera Barat
ISSN : -     EISSN : 29876362     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) adalah jurnal yang diterbitkan oleh Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat yang bertujuan untuk mewadahi penelitian di bidang Teknik Informatika dan Sistem Informasi. Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) adalah jurnal ilmiah dalam bidang teknik informatika dan sistem informasi,seperti : Kecerdasan Buatan, Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional. Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) accepts scientific articles with research scopes on: System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Image processing Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Machine Learning Other relevant study topics
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): November 2025" : 5 Documents clear
KLASIFIKASI TUTUPAN LAHAN SAWAH DAN KELAPA SAWIT MENGGUNAKAN GLCM DAN K-NEAREST NEIGHBOR PADA CITRA UDARA Nabilah Fitriani; Dano Fadilah Amelya Rizki; Soffiana Agustin
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

This study aims to automatically classify rice field and oil palm land cover based on aerial imagery by utilizing the Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction and the K-Nearest Neighbor (KNN) algorithm as the classification method. The dataset consists of 130 training images and 111 test images. The images were processed through cropping and grayscale conversion, followed by texture feature extraction including contrast, correlation, energy, and homogeneity. These features serve as the foundation for distinguishing the unique texture patterns of each land type. The test results show that the K parameter in KNN significantly affects the classification accuracy, with K=7 achieving the best result of 97.30%. Evaluation using a confusion matrix reinforces the effectiveness of the method in distinguishing the two land cover classes. The combination of GLCM and KNN proves to be both efficient and accurate, with great potential to be applied in automated mapping and monitoring systems, particularly in agricultural and plantation contexts.  
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI KEMATANGAN BUAH JERUK NIPIS Fahmi, Yusril; Qomaruzzaman; Agustin, Soffiana
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

Classification of fruit ripeness has become an important topic in agriculture, as this process often requires considerable time and effort. This study aims to develop an automatic classification system that can identify the ripeness level of lime (unripe, half-ripe, ripe) based on RGB and HSV color features using the K-Nearest Neighbor (KNN) algorithm. A total of 83 datasets were collected using a Poco M3 Pro 5G camera, followed by preprocessing, feature extraction, and classification with the KNN algorithm. Using 16 test data in classification, the highest accuracy achieved was 75% with k=5. The implementation of this method demonstrates that KNN is quite effective in classifying color features.
Penerapan Metode Weighted Moving Average dalam Meramalkan Penjualan Minuman Kekinian di Café Qomaruzzaman; Fahmi, Yusril; Agustin, Soffiana
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

The trend of modern beverages has grown significantly in recent years, in line with changes in people's lifestyles. However, despite their popularity, café business owners often face challenges due to fluctuating sales every month. This condition directly affects inventory management and daily production planning. To address this issue, accurate sales forecasting is needed. This study aims to forecast the sales of Kayyoman Macchiato using the Weighted Moving Average (WMA) method with a weight ratio of 1:1:5. The data used consists of monthly sales records from January 2020 to April 2025. After the forecasting process, the results were analyzed using three indicators: MAD, MSE, and MAPE. The findings show a MAD of 31.78, MSE of 1,496.13, and MAPE of 16.14%. These results indicate that the prediction accuracy falls into the "good" category. Therefore, the WMA method is considered suitable as a reference for managing inventory and planning production strategies in cafés.
Peramalan Pemesanan Catering Di Kedai Sering Menggunakan Metode Exponential Smoothing Dan Weighted Moving Average danofadilah; Nabilah Fitriani; Soffiana Agustin
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

This study uses a time series forecasting method, namely applying the Exponential Smoothing and Weighted Moving Average methods to predict the number of Catering orders in the following month at the SERING shop. The data used in this study were obtained from historical records of orders for 29 months and grouped into three service categories: Package 1, Package 2, and Package 3. The forecasting process is carried out by applying various parameter values, namely α = 0.1, 0.5, and 0.9 for the Exponential Smoothing method, and weights of 1, 2, and 3 for the Weighted Moving Average method with 3 data periods. To evaluate the accuracy of each method, three error measures are used: Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results of the analysis show that the Exponential Smoothing method with α = 0.9 consistently produces the smallest error value compared to other methods. Thus, this method is considered the most optimal and can be used as a basis for planning raw material procurement and making operational decisions in the future.          
Identifikasi Citra Mengandung Api Menggunakan Algoritma Random forest dengan Segmentasi Warna dan GLCM Al-Ibham; Guswanrinandi, Danang Haedar
Jurnal TEFSIN ( Jurnal Teknik Informatika dan Sistem Informasi) Vol. 3 No. 2 (2025): November 2025
Publisher : Fakultas Teknik Universitas Nahdlatul Ulama Sumatera Barat

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Abstract

Fire is a disaster that can cause significant losses if not detected early. This study aims to develop a fire detection application based on digital image processing using a combination of color segmentation, texture feature extraction, and machine learning classification in MATLAB. Images in JPG and PNG format are processed by converting the RGB color space to HSV, followed by masking the fire-colored regions for segmentation. The segmented areas are then processed for texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method, focusing on four main parameters: contrast, correlation, energy, and homogeneity. The extracted features are used as input for classification using the Random Forest algorithm with 100 decision trees. The system output is in the form of labeled images indicating either “FIRE DETECTED” or “NO FIRE.” The results show that the combination of HSV color segmentation, GLCM texture features, and Random Forest classification has strong potential as an effective approach for fire detection based on digital image.

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