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Hafiz Irsyad
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Algoritme Jurnal Mahasiswa Teknik Informatika
Published by Universitas Multi Data Palembang
ISSN : -     EISSN : 27758796     DOI : https://doi.org/10.35957/algoritme.v2i2
Core Subject : Science,
Computer Science & IT
Jurnal Algoritme menjadi sarana publikasi artikel hasil temuan Penelitian orisinal atau artikel analisis. Bahasa yang digunakan jurnal adalah bahasa Inggris atau bahasa Indonesia. Ruang lingkup tulisan harus relevan dengan disiplin ilmu seperti: - Machine Learning - Computer Vision, - Artificial Inteledence, - Internet Of Things, - Natural Language Processing, - Image Processing, - Cyber Security, - Data Mining, - Game Development, - Digital Forensic, - Pattern Recognization, - Virtual & AUmented Reality,. - Cloud Computing, - Game Development, - Mobile Application, dan - Topik kajian lainnya yang relevan dengan ilmu teknik informatika.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 4 No 1 (2023): Jurnal Algoritme" : 5 Documents clear
Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Cuaca Dandy Dandy; Daniel Udjulawa; Yohannes Yohannes
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.4932

Abstract

Weather is a brief natural event concerning the atmospheric conditions that take place on Earth which are determined by pressure, wind speed, temperature, and air phenomena. This study classifies 3 weather classes, namely sunny, cloudy, and rainy using the K-Nearest Neighbor algorithm as a weather classification algorithm with K value parameters of 3, 5, 7, and 9. Weather dataset 96.453 data to be examined is data taken from the Kaggle website. The dataset is divided into training data and test data with a ratio of 80:20. The implementation of the K-Nearest Neighbor algorithm produces a confusion matrix and classification report where in the confusion matrix, the largest number of correctly predicted data is at the value K = 9, namely 13.132 correctly predicted data with the largest number of correctly predicted data in the cloudy class, namely 10.865 data. As for the classification report, the highest accuracy value for both the cloudy, rainy, and sunny weather classes is at K = 9, which is 68.073%, and the highest precision, recall, and f1-score values are found in the cloudy class at K = 9, respectively contributed 72.095%, 89.288%, and 79.775%.
Klasifikasi Tingkat Kematangan Buah Kakao Berdasarkan Fitur Warna Menggunakan Algoritma K-Nearest Neighbor Izha Mahendra; Nur Rachmat
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.5485

Abstract

So far, cocoa farmers choose the quality of the maturity level of cocoa pods manually ormake selections based on estimates from these farmers, so that the manual method is very proneto errors in sorting the quality of cocoa pod maturity with various human factors, such as fatigueand doubt. Based on these problems, this study developed an application for classification ofcocoa pods using Hue, Saturation, Value (HSV) color extraction with the classification methodusing K-Nearest Neighbor (KNN) and applying the evaluation results method using the EuclideanDistance, so that in choosing the level of maturity Cocoa pods have the same standard and ahigher level of accuracy with digital processing. Therefore this research was conducted. Theprocess of classification of ripeness into 4 classes, namely: rotten, ripe, unripe and half ripe. Withthe KNN classification method, and the dataset used is 80 databases, as well as 40 testing data.The highest value is at k=1 with 90% accuracy, 90% precision, and 90% recall. The tool used todevelop the system is matlab.
Penggunaan Metode SVM Dengan Fitur HSV HOG Dalam Mengklasifikasi Jenis Ikan Guppy Yehezekiel Gian Lestari; Hafiz Irsyad
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.5698

Abstract

Ornamental fish are fish that are often traded to be kept as decoration to beautify and not for consumption, ornamental fish are the same as consumption fish, both of which live in fresh water or in sea water. Ornamental fish in general have a characteristic, namely a unique body shape with a body pattern with various attractive colors. One of the ornamental fish in Indonesia is Guppy fish. Guppy fish is a type of freshwater fish that lives freely in waters and is widespread in the tropics. This fish is widely cultivated by ornamental fish lovers because of the beauty of its color. There are many types of Guppy fish, a classification is needed to make it easier to distinguish the types, this research was conducted to determine the types of Guppy fish. Guppy fish used in this study were Leopard, Koi, and Albino Full Red (AFR), with the use of the SVM classification feature with HSV and HOG features. obtained scores for Guppy Leopard fish Accuracy 77%, Precision 70%, Recall 53%, values for Guppy Koi fish Accuracy 82%, Precision 78%, Recall 69%, and values for Guppy Albino Full Red (AFR) Accuracy 85%, Precision 83%, Recall 85%. Of the three types of fish studied, the Albino Full Red Guppy fish gave the highest recognition accuracy value of 85%
Perbandingan Penempatan Pivot Pada Quick Sort Berdasarkan Ukuran Pemusatan Data Rheza Rijaya; Muhammad Ezar Al Rivan
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.5735

Abstract

Sorting is one of the basic algorithm that executed frequently in a program. The most popular sorting algorithm is Quick Sort because it is faster in most scenarios than other algorithms. However, pivot selection on Quick Sort algorithm is very important to avoid the worst case scenario. This study aims to test commonly used pivot selection methods (first, middle, last) and pivot selection based on central tendency of data (mean, median, mode). The data that is tested are random data (repeated), random data (permutation), sorted, reverse-sorted, and almost sorted. The size of data that is tested are 1.000, 10.000, 100.000, dan 1.000.000. The best result is achieved by selecting middle element as pivot based on the execution time of each scenario.
Klasifikasi Pengenalan Wajah Untuk Mengetahui Jenis Kelamin Menggunakan Metode Convolutional Neural Network MUHAMMAD AKBAR SATRIAWAN; WIJANG WIDHIARSO
Jurnal Algoritme Vol 4 No 1 (2023): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i1.6095

Abstract

The face is the component that is most easily recognized and is often the center of attention of other people in the human body. There are often difficulties in distinguishing and analyzing large numbers of facial images manually due to the large number of similarities between males and females, which slows down the process of gender identification. This research was made to fix this problem by using the CNN method. The dataset used is 2280 images consisting of train, valid and test. The research process includes data pre-processing, model initialization, model training, hyperparameter validation and adjustment, and model performance evaluation. The test results show an increase in accuracy and a decrease in loss as training iterations increase. In this study, results were obtained with an accuracy rate of 92%, which shows the effectiveness of using a Convolutional Neural Network (CNN) with the ResNet-50 architecture in processing and classifying male and female facial images.

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