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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 104 Documents
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Analisis Sentimen Publik Terhadap Keberadaan Juru Parkir Liar Menggunakan Naïve Bayes Dengan Teknik SMOTE Daniel, Daniel; Saputra, Andreas; Al Rivan, Muhammad Ezar
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.v5i1.8154

Abstract

The continuous growth of YouTube is increasingly leveraged by users to convey information, including critiques and suggestions about illegal parking attendants. The method used in this research is data classification using the Naïve Bayes Classifier (NBC). The system is developed using internal data collected from the internet/YouTube to determine whether sentences are positive or negative opinions. This determination is classified as a classification process. The data is processed using SMOTE to balance the dataset, followed by classifying comments into two classes: positive and negative. This classification employs the Naïve Bayes algorithm. This classification provides convenience for users to view both positive and negative opinions. The accuracy test results for the Naïve Bayes method without SMOTE for classification yielded an average of 86.93%, while the accuracy test results for the Naïve Bayes method with SMOTE technique yielded an average of 91.99%.
Implementasi K-Nearest Neighbor Untuk Klasifikasi Citra Wayang Beber Khas Pacitan Purnamasari, Pricylia Oka Diah Ayu; Yama Hendra, R. Oktav; Wijaya, Indra
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.v5i1.8785

Abstract

The fading admirartion for the culture of one’s own country means that today’s generation is unaware of the art of wayang beber, wayang beber is the oldest wayang in Indonesia. Wayang beber flourished during the existence of the majapahit kingdom. Preserving wayang beber, which is now never played again, is the aim of the research, targeting the younger generation so that they get to know the charactters and storylines in wayang beber. The samples used in classifying wayang images to recognize wayang characters are three: Prabu Brawijaya, Dewi Sekartaji, and Joko Kembang Kuning. The method used in this research is Hue Saturation Value (HSV) for image extraction and the matlab application for processing digital image programs. From the research that has been carried out, accuracy results are up to 87%.
Implementasi Metode Viola-Jones dan Eigenfaces Pada Sistem Pengenalan Wajah Secara Real-Time La Ode, Abdul Azis Syah; Ningrum, Ika Purwanti; Saputra, Rizal Adi
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.v5i1.8927

Abstract

The application of face recognition technology in CCTV systems has become an important topic in improving security and efficiency in various sectors. This research examines the Viola-Jones and Eigenface methods for real-time face detection and recognition using CCTV. The Viola-Jones method is used for initial face detection with Haar features, while Eigenface is used for face recognition based on principal component analysis (PCA). This research involves capturing images from two CCTV camera positions with variations in lighting and distance. The test results show variations in recognition results between individuals at various light conditions and camera distances. Despite challenges such as lighting and viewing angle variations, this method provides a success percentage of up to 73.68% in face recognition under optimal conditions. The integration of this technology is expected to make a significant contribution in improving security and efficiency in various sectors, with a note of the need to pay attention to important privacy and data security aspects in its application.
Pemantauan Kelembaban tanah Berbasis IoT Menggunakan Sensor Soil Moisture Laksono, Ivan Luthfi; Kynta, Diva Putri; Fadli, Muhammad; Wijaya, Vannes; Hermanto, Dedy
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.v5i1.8961

Abstract

Soil is commonly utilized as a substrate for the cultivation of plants. The level of soil moisture has a significant impact on the growth and survival of nearby plants. Flourishing vegetation assimilates water from the soil, thereby impacting soil moisture. In addition, solar radiation induces water evaporation in the soil, leading to its desiccation. Excessively arid soil leads to plant wilting, whereas excessively saturated soil hinders the optimal growth of neighboring plants. This study focuses on the real-time detection of soil moisture using a Soil Moisture sensor. The approach employed in this research is Research and Development. The research process consists of three stages: planning, design, and assessment. The Soil Moisture sensor will collect data on the moisture content of the soil. This data will then be retrieved and documented by the ESP32 device, which will transmit and store it in a Firebase database. Once the data is recorded, it will be showcased on a website developed with the PHP programming language and the Laravel framework. This will allow users to monitor the exhibited information directly. The investigation yielded variations within each category for dry soil 28.5%, moist 58.4%, and wet 68%.
Klasifikasi Kanker Kulit Pada Citra Dermatoskopi Menggunakan CNN Martin, Nicolas; Udjulawa, Daniel
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.v5i1.9034

Abstract

Skin health is an important aspect of human well-being that is often overlooked because it is considered trivial. There are various types of skin diseases, ranging from allergies, fungal infections, to skin cancer which causes high mortality rates according to WHO. Early diagnosis is essential to improve skin cancer recovery, but often requires sophisticated medical devices and biopsies, where doctors remove a patient's skin lesion through minor surgery to detect cancer cells. This study uses the Convolutional Neural Network (CNN) method with the AlexNet architecture to classify skin cancer types. Convolutional Neural Network was chosen because of its ability to extract complex features from images for accurate classification. The dataset used came from Kaggle, consisting of 24,839 images, with testing using all data and 3,000 data, 500 images each for 6 types of skin cancer. The data is divided into 80% for training and 20% for testing. The best results were achieved using 24.839 data, a learning rate of 0.0001, Adamax Optimizer, batch size 16, and epoch 40, resulting in an accuracy value of 72%, a recall value of 72%, a precision value of 70%, and an F1 score of 69%.
Klasifikasi Penyakit Cacar Menggunakan Arsitektur AlexNet Susanto, Rafael Ivan; Tinaliah, Tinaliah
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.v5i1.9045

Abstract

Smallpox is a human skin disease that causes fluid-filled lumps, especially on the face and can spread throughout the body. There are several types of smallpox, including chickenpox (caused by the Varicella-Zoster virus), monkeypox (by the monkeypox virus), and cowpox (by the cowpox virus). Although smallpox is often considered mild, it can cause serious complications, especially for people with weakened immune systems. This research aims to develop an application that uses the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture to help doctors diagnose types of smallpox. CNNs work similarly to the way the human brain recognizes objects in images. The dataset used consists of 3200 images 800 images for each type of smallpox and healthy skim, with 80% for training data and 20% for test data. The test results show the highest accuracy of 92%, using Batch size 16, Learning rate 0.0001, Optimizer Adam, and Epoch 40.
Association Rules Menggunakan Algoritma FP-Growth Untuk Tata Letak Di Koperasi IT DEL Hutapea, Oppir
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.v5i1.9393

Abstract

Koperasi IT Del is located in IT Del campus that sells a range of office and school supplies, drinks, and snacks. For almost 20 years, the Koperasi IT Del has recorded and arranged goods manually, disregarding the needs or purchasing patterns of its clientele. The owners frequently fail to see how consumer behavior affects the sales of the cooperatives they oversee. Owners may find it easier to access their consumers' associative nature if they can identify user behavior and purchasing patterns. FP-Growth is a method that solves item layout issues by utilizing transaction or historical data that is already accessible. With a minimum support value of 24% and a minimum confidence level of 60%, this investigation yielded 47 association rules. 24 attributes that would be used from transaction history data that had already been verified were acquired from the data transformation results that were performed. The end consequence is that each association rule forms a close-knit product arrangement or position based on the item set that is commonly purchased. The Cimory UHT Matcha 20 ml box is positioned next to the 42 g Roma Coconut Cream Chocolate product (48.1%), then the My-Gell Blue Pen (47.3%), and finally the Standard AE7 Red Pen (48.1%), which is positioned next to the 42 g Roma Coconut Cream Chocolate product (48.1%), and finally next to the Nescafe Original 240 ml (47.3%).
Pengenalan Motif Songket Palembang Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-50 Cahyati, Imelia Dwinora; Devella, Siska; Yohannes, Yohannes
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.v5i1.9404

Abstract

Songket fabric is a cultural heritage of Indonesia woven with gold or silver threads, creating textiles that are not only visually captivating but also rich in cultural significance. Each motif on Palembang Songket reflects the traditions and beliefs of the community, where the selection of motifs is often tailored to specific event contexts. However, the recognition of several motifs with similar patterns presents unique challenges in the identification process. This study aims to implement a Convolutional Neural Network (CNN) method for classifying Palembang Songket motifs. The dataset used consists of images of Songket motifs, including Bintang Berantai, Naga Besaung, Nampan Perak, and Pulir. The ResNet-50 architecture is utilized as the classification model. The results indicate that the implemented model achieves an accuracy of 96% in recognizing these motifs, thereby contributing to the preservation and enhancement of understanding regarding the cultural richness of Palembang Songket.
Monitoring Sistem Fluktuasi Harga Pangan Secara Realtime Berbasis Website sari, dian megah
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.v5i1.9730

Abstract

The availability and access to sufficient, safe, and nutritious food is a fundamental human right. However, food price fluctuations are a key factor that significantly affect people's ability to meet their basic needs. Food price instability can result in various negative impacts, ranging from economic hardship to serious health problems. Monitoring food price fluctuations becomes crucial for governments, businesses, non-governmental organizations, and the general public to take appropriate steps in addressing the challenges faced. The aim of this research is to develop a system that monitors food price fluctuations as a key indicator of the development of the community's basic needs. The web-based system is developed using the PHP programming language with the open-source PHP framework Laravel as its platform, ensuring that food price information is presented transparently and is easily accessible to the public. Through this system, users can monitor changes in food prices over time, which can then be used as a basis for making economic decisions and managing household budgets.
Identifikasi Penyakit Anemia menggunakan Metode Support Vector Machine (SVM) Berdasarkan Hemoglobin Darah Wulandari, A’isyah; Wahyuni, Sri; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || 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.v5i2.8767

Abstract

In the world, the number of people infected with anemia is still very high, especially in the Asian region, reaching 48.7 percent. Anemia or anemia occurs due to a lack of blood pressure below normal values. If many people experience blood shortages, there will be many people who suffer from anemia. So it can be seen that variable Then the variable Y shows that the anemia class can be grouped into two parts, namely class 1 which states that they are infected with anemia and class 0 which states that they are not infected with anemia. This research aims to identify anemia using the Support Vector Machine (SVM) method which can be used in the analysis process with approaches from various types of kernels including; Linear, Radial Basis Function (RBF), Polynomial, and Sigomid to determine the level of accuracy, sensitivity and specificity in anemia. This research can show that the best classification of anemia using a linear kernel produces an accuracy value of 99.3 percent. The results obtained from this study indicate that the SVM method with a linear kernel is highly effective in identifying and classifying cases of anemia.

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