cover
Contact Name
Sujacka Retno
Contact Email
sujacka@unimal.ac.id
Phone
+6282295574747
Journal Mail Official
jacka@unimal.ac.id
Editorial Address
Jl. Batam. Kampus Bukit Indah. Gedung Prodi Teknik Informatika. Blang Pulo, Lhokseumawe, Aceh
Location
Kota lhokseumawe,
Aceh
INDONESIA
Journal of Advanced Computer Knowledge and Algorithms
ISSN : -     EISSN : 30318955     DOI : http://doi.org/10.29103/jacka.v1i1.14530
Core Subject : Science,
JACKA journal published by the Informatics Engineering Program, Faculty of Engineering, Universitas Malikussaleh to accommodate the scientific writings of the ideas or studies related to informatics science. JACKA journal published many related subjects on informatics science such as (but not limited to): Adversarial Machine Learning: Addressing security concerns and developing algorithms robust to adversarial attacks. Anomaly Detection Algorithms: Identifying unusual patterns or outliers in data. Automated Machine Learning (AutoML): Developing algorithms that automate the machine learning model selection and hyperparameter tuning. Automated Planning and Scheduling: Developing algorithms for autonomous decision-making and task scheduling. Bayesian Networks: Utilizing probability theory to model and analyze uncertain systems. Computer Vision: Developing algorithms for image and video analysis, enabling machines to interpret visual information. Constraint Satisfaction Problems (CSP): Designing algorithms to solve problems subject to constraints. Deep Learning: Developing algorithms for neural networks with multiple layers to model complex patterns. Distributed AI Algorithms: Implementing AI algorithms that can work across multiple interconnected devices or nodes. Ensemble Learning: Combining multiple models to improve overall system performance. Evolutionary Algorithms: Utilizing principles of natural selection for optimization and problem-solving. Experiential Learning Algorithms: Designing algorithms that improve performance through experience and learning. Expert Systems: Creating rule-based systems that emulate human expertise in specific domains. Explainable AI (XAI): Developing algorithms that provide transparency and explanations for AI decisions. Fuzzy Logic: Implementing logic that deals with uncertainty and imprecision in decision-making. Genetic Algorithms: Implementing algorithms inspired by genetic evolution for optimization tasks. Knowledge Representation and Reasoning: Creating structures and algorithms to represent and manipulate knowledge. Machine Learning Algorithms: Designing algorithms that enable systems to learn from data and make predictions. Multi-agent Systems: Designing algorithms for systems with multiple interacting agents. Natural Language Processing (NLP): Creating algorithms that understand and process human language. Neuroevolution: Combining evolutionary algorithms with neural networks for optimization. Optimization Algorithms: Developing algorithms focused on improving the performance, efficiency, or decision-making of systems by finding optimal solutions to problems. Pattern Recognition: Developing algorithms to identify patterns within data. Reinforcement Learning: Designing algorithms that learn through trial and error, often applied in decision-making systems. Robotics Algorithms: Designing algorithms for autonomous navigation, manipulation, and decision-making in robots. Semantic Web Technologies: Implementing algorithms for structuring and retrieving information on the web. Sentiment Analysis Algorithms: Analyzing text data to determine sentiment or emotion. Speech Recognition: Developing algorithms to convert spoken language into text. Swarm Intelligence: Developing algorithms based on collective behavior, as seen in swarms or colonies. Time Series Forecasting Algorithms: Predicting future values based on historical data patterns.
Articles 6 Documents
Search results for , issue "Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024" : 6 Documents clear
Web-Based Expert System Application for Early Diagnosis of HIV/AIDS Using the Naive Bayes Method Aisah, Sri Purwani; Adek, Rizal Tjut; Yunizar, Zara
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17809

Abstract

AIDS is a progressive decrease in the immune system so that opportunistic infections can appear and end in death, therefore the author created an early diagnosis system for HIV/AIDS using the website-based Naïve Bayes algorithm. Naïve Bayes is a simple probability classification that can calculate all possibilities by combining a number of combinations and frequencies of a value from the database obtained.the results of the research obtainedThe naïve Bayes algorithm can be implemented for early diagnosis of HIV/AIDS by means that the existing HIV/AIDS symptom data is adjusted to the patient's symptom data processed using the naïve Bayes algorithm and then it is concluded what the symptoms are and What is the solution.
Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method Jasmin, Nadya; Dinata, Rozzi Kesuma; Sahputra, Ilham
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17807

Abstract

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.
Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory Tawakal, Rayendra; Nazar, Muhammad; Asri, Rahmadi
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.18889

Abstract

A real-time facial detection system for identifying young and old faces has been developed using a combination of Template Matching and Fuzzy Associative Memory (FAM) methods. This study aims to improve accuracy in detecting facial age, particularly from images captured via a webcam. The system was tested across four categories: Old Men, Young Men, Old Women, and Young Women, with 10 image samples per category. The results indicate that the system achieved an accuracy rate of 83%. The Young Men category exhibited the best performance with 100% accuracy, while detection errors occurred in the Old Men and Old Women categories, with a false positive rate of 30%. The system proved to be more effective at detecting young faces than old faces. The primary challenge of this study was managing the complex variation in the patterns of older faces. Thus, further research is required to enhance the system’s performance in detecting older faces and reduce the false positive rate.
Implement the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) Algorithms for Sales Classification Husna, Asmaul; Retno, Sujacka; Rijal, Himmatur
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17819

Abstract

The Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) algorithms are two algorithms that have proven efficient in various classification and prediction applications. This research examines the application of these two algorithms in the context of selling goods in PIM supermarkets. In this research, AHP and KNN are used to classify goods sold based on various criteria such as price, number of stock items sold, total sales amount. The research results show that KNN outperforms AHP in predicting the best-selling, best-selling and least-selling items based on sales in 2022 at PIM supermarkets. Based on this research, it can be concluded that the KNN algorithm is suitable for predicting the classification of goods sales in PIM Supermarkets. This research classifies sales of goods using the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) methods. This research uses 3 criteria. By using the value K=1, the experimental results show that the highest KNN has an accuracy of 38%, while AHP has an accuracy of 32%. Differences in accuracy results can be influenced by parameter settings and characteristics of the dataset used. Therefore, further analysis of these factors is needed to understand the performance differences between the two methods.
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA, JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.18895

Abstract

The Use of Brown's Double Exponential Smoothing Method to Predict Harvest Yields in Horticultural Crops Mutiara, Mutiara; Fuadi, Wahyu; Maryana, Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17806

Abstract

Agriculture stands as a pivotal sub-sector within the economy of North Aceh. Among its primary commodities are horticultural crops, encompassing the cultivation of vegetables, fruits, medicinal plants, and ornamental flora. In endeavors to boost agricultural productivity and efficiency, the utilization of harvest prediction methodologies has grown increasingly indispensable. This study relies on historical harvest data spanning from 2017 to 2022 to forecast crops such as leafy greens, fruits, and medicinal plants. The selected plants for prediction include spinach, water spinach, cucumber, banana, durian, rambutan, ginger, lesser galangal, and turmeric. Data analysis employs Brown's double exponential smoothing method, selecting the α (alpha) parameter that minimizes the Mean Absolute Percentage Error (MAPE) for accurate forecasting. Spinach is anticipated to yield 1239.9508 quintals, with an α (alpha) parameter of 0.9 and a MAPE of 38.46%. Water spinach is forecasted to yield 2069.75 quintals, with an α (alpha) parameter of 0.5 and a MAPE of 18.14%. Cucumber is projected to yield 1023.22432 quintals, with an α (alpha) parameter of 0.4 and a MAPE of 31.51%. Consequently, the highest projected yield is for water spinach at 2069,75 quintals.

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