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
 3   4   5   6   7 
Identifikasi Kadar Ikan Pada Pempek Menggunakan Teknik Blok Citra Dengan Fitur GLCM Dan Metode JST Saputri, Nurdiana Dewi; Gasim, Gasim
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

As we know today, Indonesia has many unique foods, each in each region. For example, pempek is a typical food from Palembang, South Sumatra. The ingredients for making pempek do not only use fish, but there are many different dough formulas that create different flavor compositions. Differences that occur in the dough formula when making pempek will affect the texture and taste, because there is a mixture of fish and the amount of flour. The research uses image block techniques with GLCM (Gray Level Co-Occurrence Matrix) features and artificial neural network methods. The GLCM (Gray Level Co-Occurrence Matrix) feature extraction used consists of Entropy, Standard Deviation, Contrast, Angular Second Moment (ASM)/ Homogeneity, Correlation, and Inverse Different Moment (IDM)/ Energy. The dataset used in this study is to use the best results at a portrait distance of 13 cm from previous studies. There are 4 types of comparisons used, namely 1 fish 1 flour, 1.5 fish 1 flour, 2 fish 1 flour, and 1 fish 2 flour. The recognition results obtained in this study were 360 recognized training data and 89 recognized test data and obtained an accuracy rate of 37.08%.
Identifikasi Kadar Ikan Pada Pempek Dengan Fitur LBP Dan Metode Pengenalan SVM Suhanto, Kevita Titany; Gasim, Gasim
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || 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.v3i1.3363

Abstract

The main ingredients commonly used by the community in making pempek are ground fish and sago flour. The people of Palembang usually make pempek into several variants such as egg pempek, pempek pistel, pempek curly, pempek submarine, pempek roasted, pempek lenggang, etc. In the previous study, the dataset was 4 types of pempek lenjer with different levels of snakehead fish and flour, where each comparison was equal to 200 grams. The comparisons are: 1 snakehead fish to 1 sago flour, 1.5 snakehead fish to 1 sago flour, 2 snakehead fish to 1 sago flour, 1 snakehead fish to 2 sago flour. In this study, the dataset used is a photo image using a 2MP camera resolution which is the best dataset from previous research (Amatullah, 2021) which obtained an accuracy rate of 23.33% and the number of test data recognition was 56 out of 240 test data. Then this research was conducted using LBP feature extraction and the introduction of the Support Vector Machine method which resulted in an accuracy rate of 22.92%.
Implementasi Algoritma DBSCAN Dalam Mengelompokan Data Pasien Terdiagnosa Penyakit Ginjal Kronis(PGK) Anggara, Richardo; Rahman, Abdul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || 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.v3i1.3593

Abstract

Chronic disease (CKD) is a kidney disease characterized by structural or functional kidney damage that lasts more than three months. CKD is characterized by one or more signs of kidney damage, namely albuminuria, abnormal urine sediment, electrolytes, histology, renal structure, or a history of kidney transplantation, with decreased glomerular filtration rate. This study used the DBSCAN implementation method to classify data on diagnosed CKD patients. The essential data used is Chronic Kidney Disease which collects 400 medical data with 24 attributes or features. The study's results note many clusters and noise obtained using Euclidean metrics. The best results are in the second scenario with an epsilon value of 3.5 and Min sample = 5, which produces a total of 2 clusters with a Silhouette value of 0.158.
PENGGUNAAN ALGORITMA RANDOM FOREST DALAM KLASIFIKASI BUAH SEGAR DAN BUSUK Santoso, Felix; Hartati, Ery
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || 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.v3i1.3404

Abstract

Buah-buahan merupakan salah satu makanan yang sering dikonsumsi oleh berbagai kalangan umur karena sumber berbagai mineral, vitamin dan serat pangan. Untuk memperoleh manfaat yang terdapat pada buah, masyarakat harus mengonsumsi buah yang segar dan belum busuk. Secara fisik, kesegaran buah dapat dilihat karena tanda-tanda yang ada pada buah segar atau buah busuk mudah diamati.LBP (Local Binary Pattern) adalah metode ekstraksi fitur tekstur yang sederhana,namun efisien dalam mempresentasikan ciri tekstur, sedangkan HSV (Hue, Value dan Saturation) merupakan ruang warna yang cocok untuk mengidentifikasi warna-warna dasar yang akan digunakan dalam penelitian sebagai warna identifikasi cahaya dan bisa menoleransi perubahan intensitas cahaya. Penelitian ini menggunakan public dataset buah segar dan buah busuk. Proses di mulai dari resize menjadi ukuran 300 x 300 pixel dan selanjutnya dilakukan ekstraksi fitur LBP dan dilanjutkan dengan ekstraksi fitur HSV. Hasil ekstraksi fitur LBP dan HSV di gunakan sebagai input klasifikasi menggunakan algoritma random forest dengan nilai n_estimator 500,1000,1500,dan 2000. Hasil pengujian menggunakan algoritma random forest menghasilkan nilai Accuracy tertinggi sebesar 95,92% dengan nilai n_estimator 2000.
Implementasi Model Perhitungan Untuk Menentukan Performa Basis Data MySQL Dan PostgreSQL Dias Wendri, Yoga Pramuda; Kusmiran, Ari; Arman, Molavi
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || 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.v3i1.4219

Abstract

The Application must be can accessible for twenty-four hours a day by its users, the proper implementation and difference of databases is very important to support the connections from hundreds of thousands of users. More and more complaints from users that appear to performance, and as well as increase of the number of visits to the database applications that used MySQL and PostgreSQL to components Queries Performed: Read, Write and0 Transaction, Per-request Statistics: Average and Threads Fairness: Execution Time. In that three components is doing the test for determine which one is the best between the using of MySQL and PostgreSQL databases as well as giving the best solution in election databases which is different test components is results. This research is uses the indenpendent sample t-test calculation formula using two computers, the database used has the same size, to find out whether the differences from the result of the two databases. The result of the research is explained that MySQL is the best results compare with PostgreSQL by using indenpendent sample t-test method as the best solution.
Identifikasi Penyakit Pada Tanaman Kopi Berdasarkan Citra Daun Menggunakan Metode Convolution Neural Network Fatchurrachman, Ahmad; Udjulawa, Daniel
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.v3i2.3384

Abstract

Coffee plants are usually made for drinks made from coffee beans that have been ground into powder. One of the causes of decreased coffee quality is caused by pests that can attack from the leaves, stems and roots. This study aims to identify coffee plant diseases based on leaves using the Convolution Neural Network (CNN) method with the ResNet-50 architecture with the Adam optimizer. The total data from the dataset is 1664 images, in the dataset there are 1264 train data images and 400 test images. The highest result in training in this study using 60 epochs and Adam's optimizer with a probability value of learning_rate of 0.0001 getting a probability value of 0.9969 and the lowest value getting a probability value of 0.4918. The results of testing the test data in this study obtained an accuracy rate of 99%.
Klasifikasi Ras Anjing Berdasarkan Citra Menggunakan Convolutional Neural Network Leovincent, Axel; Yoannita, Yoannita
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.v3i2.3389

Abstract

Dogs are mammals that are much loved and kept. Dogs have 355 breeds worldwide. Each race has its own differences, but in certain races have little or almost similar differences. This study classifies 120 dog breeds using the Convolutional Neural Network (CNN) with the ResNet-50 architectural model and the Adam optimizer. The dataset used consists of 20580 images. The dataset is divided into training data, validation data, and test data with a ratio of 60:20:20. The resolution image is 224x224 pixels in size. In this study, it yielded an accuracy of 99,35%.
Deteksi Masker Melalui Video CCTV Menggunakan You Only Look Once Darmawan, Dean; Udjulawa, Daniel; Wijaya, Novan
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.v3i2.3598

Abstract

The coronavirus pandemic or known as the COVID-19 pandemic is a global pandemic of corona virus that are caused by severe acute respiratory syndrome coronavirus 2 that are started in Wuhan, China in 2019. In 30th January 2020 World Health Organization (WHO) declared an emergency situation towards COVID-19 and in 11th March 2020, WHO officially declared an ongoing global pandemic of COVID-19. COVID-19 cases in the world itself is already reaching 181 million of cases with around 3.92 million deaths. Indonesia itself is one of the country that are affected by COVID-19 spread with 2.09 million cases and 56,729 deaths. In order to decrease the amount of COVID-19 cases, WHO require each individuals to do social distancing, stay hygiene, and always wearing face mask to prevent even more spread of the virus. A method to do face mask detection is proposed using a object detection method, You Only Look Once (YOLO). The test results obtained by calculating f-measure with the highest accuracy of 0.59 and the lowest of 0.19 using CCTV video that are taken with 70 cm distance. In the second test using video that are recorded with more than 90 cm the program obtained it’s result of 0.
Prediksi Pertumbuhan Penduduk Kecamatan Cimaragas Kabupaten Ciamis Dengan Metode Artificial Neural ARMANDA, RAFLI KURNIA
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.v3i2.3709

Abstract

This research was conducted to study an Artificial Neural Network, which is one of the command languages used in the Matlab program which is used to predict input data and can be used to predict population growth. Artificial Neural Network is a powerful data model that is able to capture and represent complex Input-Output relationships, because of its ability to solve some problems relatively easy to use, resilience to input speed data for execution, and initialize complex systems. By using Artificial Neural Networks (Artificial Neural Network) is expected to provide another alternative in estimating and predicting population growth rates in Cimaragas District, Ciamis Regency.
Penentuan Tingkat Kerontokan Rambut Kepala Pria dengan Metode Fuzzy Inference System Sugeno Karuna, Eric; Petrus, Johannes
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.v3i2.4204

Abstract

Abstrak Rambut kepala merupakan organ tubuh dari manusia yang memiliki bentuk seperti helaian benang yang tumbuh di kulit dengan mengandung banyak keratin serta dapat muncul dari lapisan epidermis. Terdapat berbagai faktor yang dapat mengakibatkan perubahan kondisi kulit kepala dan rambut seperti faktor usia lanjut, depresi, berkurangnya aktifitas kelenjar minyak dikulit kepala, gangguan pembuluh darah, gangguan hormon, pengaruh kosmetika, paparan sinar matahari secara terus menerus dan kurangnya makanan yang bergizi untuk kepentingan pertumbuhan rambut. Penelitian ini melakukan perancangan model Fuzzy Sugeno untuk menentukan tingkat kerontokan rambut kepala pada pria berdasarkan faktor-faktor penyebabnya. Salah satu tujuan dalam penelitian ini adalah untuk mengetahui tingkat kerontokan rambut kepala pada pria menggunakan metode Sugeno. Pada model Fuzzy Sugeno mendapatkan hasil yang rendah dalam menentukan tingkat kerontokan rambut kepala pada pria, yaitu memperoleh nilai error sebesar 114,870 untuk nilai MSE dan 5,73% untuk nilai MAPE.

Page 5 of 11 | Total Record : 104

 3   4   5   6   7 

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