Solikhun Solikhun
YOU?
Author Swipe
Optimizing convolutional neural network hyperparameters to enhance liver segmentation accuracy in medical imaging Open
Liver segmentation in medical imaging is a crucial step in various clinical applications, such as disease diagnosis, surgical planning, and evaluation of response to therapy, which require a high degree of precision for accurate results. T…
View article: Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance
Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance Open
Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucialrole in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clusteringmethod by integrating a quantum compu…
View article: Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering
Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering Open
Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns…
View article: Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques
Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques Open
Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identificatio…
View article: DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY
DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY Open
The development of electric vehicles is an important innovation in reducing greenhouse gas emissions while reducing dependence on fossil fuels. The main problem in developing electric vehicles is the lack of adequate infrastructure. Inaccu…
Quantum Perceptron in Predicting the Number of Visitors to E-Commerce Websites in Indonesian Open
In the current digital era, e-commerce has become the backbone of Indonesia's digital economy, which is experiencing rapid growth. However, competition in this industry is becoming increasingly fierce, indicating the importance of predicti…
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk Open
Hypertension is a major cause of cardiovascular disease, making early risk prediction essential. According to WHO, hypertension cases are estimated to reach 1.28 billion by 2023. This study aims to optimize the K-Nearest Neighbor (KNN) alg…
View article: Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance
Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance Open
Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and…
Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna Open
Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning a…
OPTIMIZING THE KNN ALGORITHM FOR CLASSIFYING CHRONIC KIDNEY DISEASE USING GRIDSEARCHCV Open
Chronic Kidney Disease (CKD) is a progressive condition that impairs kidney function and cannot be cured. Early detection is crucial for effective management and therapy. However, diagnosing CKD is challenging as patients often have comorb…
View article: IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE
IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE Open
Heart disease is a severe health condition characterized by dysfunctions in the heart and blood vessels, which can be fatal if not properly managed. Early detection and prediction of heart disease are crucial for understanding the prevalen…
New Approach to The Perceptron Algorithm with Quantum Computing for Prediction Analysis of Rice Imports in Indonesia Open
Rice imports are crucial to ensure a country's food availability, especially when domestic production is insufficient. Because rice is the staple food of Indonesians, a spike in rice prices could cause social unrest. Rice imports have a st…
Analyzing Perceptron Algorithm for Global Gold Price Prediction using Quantum Computing Approach Open
The price of gold has garnered significant attention in the world of finance and investment due to its role as a safe haven asset and an indicator of global economic stability. An inherent risk of investing in gold is the daily fluctuation…
Refining CNN architecture for forest fire detection: improving accuracy through efficient hyperparameter tuning Open
Forest fire detection is one of the critical challenges in disaster mitigation and environmental management. This research aims to increase the accuracy of forest fire detection through improving the convolutional neural network (CNN) arch…
A revolutionary convolutional neural network architecture for more accurate lung cancer classification Open
This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architec…
A COMPARATIVE EVALUATING NUMERICAL MEASURE VARIATIONS IN K-MEDOIDS CLUSTERING FOR EFFECTIVE DATA GROUPING Open
The K-Medoids Clustering algorithm is a frequently employed technique among researchers for data categorization. The primary difficulty addressed in this investigation pertains to the extent of optimality achieved when varying distance com…
Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Open
This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of tr…
COMPARISON OF ADALINE AND HEBBIAN ALGORITHMS ON PATTERN RECOGNITION WITH QUANTUM COMPUTING APPROACH Open
In this research, a quantum computational approach was employed to enhance the Adaline and Hebbian algorithms. A comparative analysis of these algorithms was conducted, focusing on their performance, specifically the accuracy of test outco…
New Innovation: Predicting Anemia with the K-Medoids Method and Quantum Computing Using Manhattan Distance Open
The low accuracy of anemia diagnosis with the classical K-Medoids method shows the need for alternative, more effective techniques in processing medical record data. This research aims to analyze the effectiveness of the quantum computing …
Rancang Bangun Sistem Presensi Biometrik Sidik Jari Berbasis IoT dengan Arduino Node MCU Open
Presence is a procedure or process that needs to be carried out by all employees or members of a company or agency. The attendance register is a reference and benchmark for assessing, measuring, and determining the quality and quantity of …
PERANCANGAN ABSENSI QR CODE MAHASISWA BERBASIS WEBSITE PADA STIKOM TUNAS BANGSA PEMATANG SIANTAR MENGGUNAKAN METODE AGILE Open
This research discusses the implementation of a web-based attendance system that utilizes QR scanner technology to record individual attendance, especially in an educational environment. The report also highlights the primary benefits of t…
POLAK-RIBIERE CONJUGATE GRADIENT ALGORITHM IN PREDICTING THE PERCENTAGE OF OPEN UNEMPLOYMENT IN NORTH SUMATRA PROVINCE Open
The economic problem that has a direct impact on human life and welfare is unemployment. One of the cities in Indonesia with the highest unemployment rate is North Sumatra Province. For example, Tebing Tinggi City had the highest unemploym…
The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data Open
The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clusteri…
Penerapan Algoritma K-Means Dalam Mengelompokkan Jumlah Penerimaan Sinyal Telepon Seluler Di Sumatera Utara Open
The purpose of this study was to cluster the number of cell phone signal reception in North Sumatra. The source of the data used is obtained from BPS. The variable used is the number of cell phone reception signals in North Sumatra. This r…
Q-Madaline: Madaline Based On Qubit Open
This research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALI…
Pengujian Jaringan Saraf Tiruan Dalam Mendiagnosa Gangguan Jiwa Menggunakan Algoritma Backpropogation Levenberg-Marquardt Open
Mental disorders are mental health issues that make it hard to meet one's own or other people's needs. A person's life may be affected by changes in behavior brought on by this condition. To conquer this issue, a backpropagation calculatio…
The Performance Machine Learning Powel-Beale for Predicting Rubber Plant Production in Sumatera Open
This study aims to predict rubber plants in Sumatra; rubber plants have a relatively high economic value; rubber sap must be cultivated because it is a product of the rubber plant, which is the raw material for the rubber industry, so in l…
The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province Open
Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the…
View article: Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data
Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data Open
The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in thi…
The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra Open
Determination of spinach plant predictions is one of the most critical decision-making processes. In predicting spinach plants in each period, it depends on each period, both the previous and subsequent periods. The production of spinach p…