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Teknomatika: Jurnal Informatika dan Komputer
Published by Universitas Jenderal Achmad Yani Yogyakarta
ISSN : 19797656     EISSN : 30310865     DOI : 10.30989
Core Subject : Science,
Computer Science & IT
Teknomatika: Jurnal Informatika dan Komputer ISSN: 3031-0865 (Online), 1979-7656 (Print) is a free and open-access journal published by Fakultas Teknik dan Teknologi Informasi Universitas Jenderal Achmad Yani Yogyakarta, Indonesia. Teknomatika publishes scientific articles from scholars and experts worldwide related to the computer science, informatics, computer systems and information systems. This journal accommodates articles covering: Mathematics and Statistics Algorithms and Programming Intelligent System Artificial Intelligence Software Engineering Computer Architecture Distributed System Cyber Security Electronics and Embedded Systems Data and Information Management Information Systems Enterprise System All published articles will have a Digital Object Identifier (DOI). The Journal publication frequency is twice a year (sixth monthly: Maret and September).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 17 No 2 (2024): TEKNOMATIKA" : 4 Documents clear
Sistem Pakar Penyakit Sapi Menggunakan Rule Based Reasoning dengan Forward Chaining Alfian, Muhammad; Aprianti, Winda; Rhomadhona, Herfia; Permadi, Jaka
Jurnal Teknomatika Vol 17 No 2 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i2.1541

Abstract

Tanah Laut Regency is a district in South Kalimantan known as a livestock farming hub, particularly for beef cattle farming. The beef cattle population in this regency reached 88,420 heads in 2023. This large number of beef cattle requires greater attention from the local government. The Livestock and Animal Health Office of Tanah Laut Regency has Field Extension Officers (PPL) stationed in every village to monitor and inspect the condition of beef cattle owned by farmers. The issue is that monitoring results are still recorded manually, and farmers lack sufficient knowledge about beef cattle diseases. As a result, diseases are often not detected early, leading to severe conditions and even death. An expert system with forward chaining reasoning is necessary to reduce farmers' dependence on PPL due to their limited knowledge of beef cattle diseases. The expert system was developed based on a knowledge base derived from literature studies and interviews with an expert (one of the PPL officers). Data collected includes 21 diseases and 62 symptoms. The database used for the system consists of six tables. The system's functionality was tested using Blackbox Testing, and all functionalities performed well. The accuracy of the expert system was tested using 10 test data samples, achieving an accuracy rate of 90%.
Klasifikasi Data Tak Seimbang Menggunakan Algoritma Random Forest dengan SMOTE dan SMOTE-ENN: (Studi Kasus pada Data Stunting) Anju Fauziah; Julan Hernadi
Jurnal Teknomatika Vol 17 No 2 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i2.1530

Abstract

Algoritma random forest merupakan salah satu metode klasifikasi pembelajaran mesin yang banyak digunakan karena memiliki keunggulan dalam mengurangi resiko overfitting sekaligus meningkatkan kinerja prediksi secara umum. Namun untuk data dengan kelas tidak seimbang, algoritma ini tidak mampu mencapai performa maksimal khususnya dalam memprediksi data pada kelas minoritas. Untuk itu artikel ini menawarkan dua metode resampling untuk menyeimbangkan data, yaitu Synthetic Minority Oversampling Technique (SMOTE) dan Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN). Untuk klasifikasi data diterapkan algoritma random forest terhadap data asli dan hasil resampling baik menggunakan SMOTE maupun SMOTE-ENN. Studi kasus diterapkan pada data stunting yang berjumlah 421 pada kelas mayoritas dan 79 pada kelas minoritas. Diperoleh akurasi 89% pada data asli, 90% pada data hasil resampling dengan SMOTE-ENN, dan 91% pada data resampling dengan SMOTE. Walaupun tidak terlalu signifikan, teknik resampling dengan SMOTE memberikan akurasi terbaik.
Curve Fitting Kurva Tegangan Keluaran Sensor GP2Y0A02YK untuk Unjuk Kerja yang Lebih Akurat Priyanto, Agung; Buntoro Irawan; Titik Rahmawati; Ari Cahyono
Jurnal Teknomatika Vol 17 No 2 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i2.1540

Abstract

Sensors in the internet of things (IoT) and wireless sensor networks (WSN) for various purposes often require recalibration. The datasheet that accompanies the sensor usually displays a table of sensor’s output with minimum, typical and maximum values. This means that there is a tolerance of the values ​​that are still allowed by the manufacturer. For sensors with variable output voltage, it is usually displayed graphically. If the graph is not linear, curve fitting is needed to obtain the curve equation. In this research, curve fitting will be carried out using the least sum of squared errors method using a matrix. The curve equation resulting from curve fitting is needed for data processing or if it is needed to predict fixed variables if the independent variables are known. One of the sensors that has an output voltage curve that is not a straight line is the distance sensor from Sharp, the GP2Y0A02YK series. According to the datasheet, this sensor can be used to measure distances from 20 centimeters to 150 centimeters. The graph of the output voltage measuring distances from 20 cm to 150 cm is in the form of a curved curve. With calibration using curve fitting, it is hoped that the accuracy of sensor measurements and the processing of its output data will increase.
Perbandingan Deteksi Objek Kemeja Putih dan Hitam menggunakan ANN dan CNN.: Indonesia Jane Arnecia, Zahra; Wiliani, Ninuk
Jurnal Teknomatika Vol 17 No 2 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i2.1552

Abstract

This study discusses the comparison of object detection of white shirts and black shirts using the Artificial Neural Network and Convolutional Neural Network methods. The purpose of this study is to analyze the performance of the two algorithms in recognizing color differences in objects and characteristics of shirts. The dataset used is a dataset of white and black shirts from various angles. In this study, it is known that the CNN method is superior in detecting black and white shirts with an accuracy of 41% compared to ANN, which reaches an accuracy of 29%.

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