cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hafiz Irsyad
Contact Email
hafizirsyad@mdp.ac.id
Phone
+6281373740969
Journal Mail Official
hafizirsyad@mdp.ac.id
Editorial Address
Universitas Multi Data Palembang, Kampus Rajawali. Jl. Rajawali no 14 Palembang
Location
Kota palembang,
Sumatera selatan
INDONESIA
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 104 Documents
 4   5   6   7   8 
Identifikasi Kadar Ikan Pada Pempek Menggunakan Fitur GLCM dan SVM Naufal, Muhammad Afif; Gasim, Gasim
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 2 (2023): April 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i2.4791

Abstract

Pempek merupakan makanan khas kota Palembang, Sumatera Selatan. Pempek dibuat dari olahan daging ikan giling yang sebelumnya telah dikuliti dan dipisahkan dari duri halus. Perbandingan pada pempek tersebut selain dapat diketahui oleh orang awam melalui rasa dapat juga diketahui melalui media elektronik yakni melalui kecerdasan buatan. Penelitian ini dilakukan untuk mengetahui perbandingan kadar ikan pada pempek dengan empat jenis kadar perbandingan yakni kadar 1 terdiri dari 1 ikan gabus 1 tepung (1:1), kadar 2 terdiri dari 1.5 ikan gabus 1 tepung (1.5:1), kadar 3 terdiri dari 2 ikan gabus 1 tepung (2:1), dan kadar 4 terdiri dari 1 ikan gabus 2 tepung (1:2). Metode pengenalan yang digunakan Support Vector Machine dengan ekstraksi fitur GLCM dengan dua jenis parameter yang berbeda yakni menggunakan GLCM dengan 4 parameter yang terdiri dari nilai Contras, Homogeneity, Correlation, dan Energy. Dan GLCM dengan 2 parameter yang terdiri dari nilai Homogeneity dan Correlation. Klasifikasi menggunakan metode SVM dengan ekstraksi GLCM dua parameter berbeda pada penelitian ini mendapatkan nilai akurasi sebesar 25.83% pada ekstraksi GLCM empat parameter, sedangkan hasil dari SVM dengan ekstraksi GLCM dua parameter hanya 25%.
Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Cuaca Dandy, Dandy; Udjulawa, Daniel; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
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 Mahendra, Izha; Rachmat, Nur
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
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 Lestari, Yehezekiel Gian; Irsyad, Hafiz
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
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 Rijaya, Rheza; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
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 SATRIAWAN, MUHAMMAD AKBAR; WIDHIARSO, WIJANG
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika
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.
Analisis Metode Klasifikasi Pada Data Sewa Sepeda Di Seoul Charles, Nichola
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i2.6636

Abstract

Penelitian ini menyoroti peran penting klasifikasi berbasis empat musim dalam konteks manajemen mobilitas perkotaan, dengan fokus untuk mengatasi tantangan kemacetan lalu lintas dan mendukung pilihan transportasi yang berkelanjutan. Penelitian ini menekankan pentingnya akurasi dan stabilitas dalam klasifikasi berdasarkan musim, dengan menggunakan metode analisis data seperti Principal Component Analysis (PCA), K-Nearest Neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Regresi Logistik, dan Gradient Boosting. Dataset yang didapat berasal dari website UCI Machine Learning Repository, dengan mencakup berbagai variabel yaitu musim(musim semi, musim panas, musim gugur, dan musim dingin), suhu, kelembapan, kecepatan angin, jarak pandang, titik embun, radiasi matahari, curah salju, dan curah hujan dalam analisi. Hasil penelitian dalam analisis klasifikasi berdasarkan musimam per jam dan tanggal, mendapatkan keakurasian data yang paling tinggi sebesar 0.709 oleh metode Random Forest dengan menggunakan data training 80% dan data testing 20%.
Identifikasi Tingkat Kesegaran Daging Ayam Kampung Menggunakan Metode KNN Berdasarkan Warna Daging Kasanova, Sinyo; Udjulawa, Daniel
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i2.7872

Abstract

Free-range chicken is a type of poultry that is still natural in the sense that it has not received genetic improvement treatment. The economically advantageous characteristics of free-range chickens are relatively few compared to purebred chickens, even in terms of egg production and the ability to produce meat. Determining the level of freshness of free-range chicken meat is an important factor in determining the quality of the meat to be consumed, so that people as consumers can avoid the worst risks if they consume free-range chicken meat that is not fresh. For this reason, research was carried out to determine the level of freshness of free-range chicken meat using the KNN and HSV methods. The level of freshness is divided into 3 levels, namely rotten, fresh and not fresh. Based on the results of tests carried out using the KNN method with models k=1, k=3, k=5, k=7, k=9, and k=11, it was found that in the testing process the value of the highest accuracy was obtained by the value k=5 namely 80% and k=7, namely 80%, meanwhile the lowest result was obtained by the value k=1, namely 80%.
Penerapan Teknik SMOTE Pada Analisis Sentimen Bea Cukai Menggunakan Algoritma Naïve Bayes Moniung, Yosefa Camilia; Marcellino, Alwin; Rusbandi, Rusbandi
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v4i2.8155

Abstract

Social media platforms like YouTube are frequently used by the public and can quickly make issues go viral. Recently, customs duties have come under scrutiny for being considered too high. For example, a man who bought shoes worth 10 million rupiahs was charged 30 million rupiahs in import duties, and a female migrant worker from Madura was charged hundreds of millions of rupiahs for bringing 3kg of gold from Saudi Arabia. These cases have sparked public debate, leading to a sentiment analysis using the Naïve Bayes algorithm and SMOTE method. The research dataset was imbalanced, prompting a comparison between using SMOTE and not using it. The evaluation results without SMOTE showed an accuracy of 95.175%, with precision at 95%, recall at 100%, and an F1-score of 98% for the negative class, but all metrics for the positive class were 0%. After applying SMOTE, the overall accuracy was 85.526%. The negative class achieved a precision of 98%, recall of 87%, and an F1-score of 92%, while the positive class achieved a precision of 19%, recall of 64%, and an F1-score of 30%. Without SMOTE, the accuracy was higher, but overfitting occurred.
Identifikasi Kualitas Beras Berdasarkan Fitur Citra Menggunakan Metode K-Nearest Neighbors(KNN) Putra, Boy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.7790

Abstract

So far, rice companies determine the quality of rice through 2 stages, namely visual tests and laboratory tests. Laboratory tests are said to take quite a long time, while visual tests are carried out manually, by estimation or by human eye vision, so errors often occur in determining the quality of rice due to fatigue. and doubts in determining the quality of rice. Based on this problem, this research developed an application for identifying rice quality using Hue, Saturation, Value (HSV) color extraction with an identification method using K-Nearest Neighbor (KNN) and applying an evaluation results method using Euclidean Distance, in order to determine the level of accuracy. higher with digital processing. Therefore, this research carried out the process of identifying the quality of rice into 3 classes, namely medium 2, medium 1 and premium. With the KNN identification method, and the dataset used is 240 training data and 60 test data. The highest value is k=3 with an accuracy of 93.33%, precision of 93.33% and recall of 93.33%. So identifying rice quality based on HSV color image features using the K-Nearest Neighbors (KNN) method is suitable for use as intended.

Page 6 of 11 | Total Record : 104

 4   5   6   7   8 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 2 (2024): April 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 2 (2023): April 2023 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 1 (2021): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika More Issue