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Journal of Advanced Computer Knowledge and Algorithms
Published by Universitas Malikussaleh
ISSN : -     EISSN : 30318955     DOI : http://doi.org/10.29103/jacka.v1i1.14530
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Teori Komputasi dan Matematika
Articles 51 Documents
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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.
Systematic Literature Review of Sentiment Analysis on Various Review Platforms in the Tourism Sector Panjaitan, Cherlina Helena Purnamasari
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.20287

Abstract

Sentiment analysis has become an essential tool for understanding public opinion, especially in the digital era. This study aims to systematically review the methods and algorithms used in sentiment analysis of reviews in the tourism sector using datasets from social media and digital platforms from 2019 to 2024. The study adopts the Systematic Literature Review (SLR) methodology based on Kitchenham's guidelines, comprising three phases: planning, execution, and reporting. Data were collected from academic databases such as Scopus, IEEE Xplore, and ScienceDirect, with inclusion criteria covering relevant articles published between 2019 and 2024 and using datasets from social media platforms like Twitter or tourism platforms like TripAdvisor. A total of 22 models and algorithms, including deep learning, machine learning, hybrid, transformer, and lexicon-based methods, were identified in this analysis. The findings indicate that the methods with the highest accuracy are lexicon-based algorithms such as VADER (accuracy of 98%) and machine learning algorithms such as the Naïve Bayes Classifier (F1-score of 96%). This study also highlights the importance of data pre-processing to improve model performance. This research provides insights into trends, strengths, and weaknesses of the algorithms used in sentiment analysis within the tourism sector, as well as recommendations for researchers and practitioners to select the most suitable methods for their needs. The results are expected to contribute to the development of more optimal sentiment analysis methods for the tourism sector.
Implementation of the Secure Hashing Algorithm-512 (SHA-512) for Sign-Up Page Security in the KelasSeru Tutoring System Agusniar, Cut; Fazira, Ira; Wahyunita, Laili
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.20320

Abstract

Security for user authentication Security for user authentication in the sign-up process is an important aspect in protecting data from unauthorized access. This study aims to implement the Secure Hashing Algorithm-512 (SHA-512) algorithm on the sign-up page of the website-based KelasSeru tutoring system using Flask, to improve the security of user data, especially passwords. SHA-512 was chosen because of its ability to produce a 512-bit hash that cannot be returned to its original form, making it more resistant to cyber attacks such as bruteforce collision attacks. The research methodology includes developing a Flask-based application, validating input, and encrypting passwords before saving them to the database. This encryption process ensures that passwords are not stored in plaintext, but in hash form that is difficult to crack. The results show that SHA-512 is effective in maintaining password confidentiality and improving overall system security. In addition, the website also displays additional features and a different page display if the person logging in is an admin or users. This study proves that implementing SHA-512 on the sign up page can provide significant protection against cyber threats and ensure user data remains secure, providing a sense of comfort and trust for its users.
The Application of Decision Tree C4.5 to Determine College Majors for Students After Madrasah Aliyah (Case Study: Madrasah Aliyah Misbahul Ulum) Mulyani, Tasya; Bustami, Bustami
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.19051

Abstract

This research aims (1) to implement the C4.5 Decision Tree in determining college majors for students after Madrasah Aliyah, (2) to assess the outcomes of applying the C4.5 Decision Tree method in selecting college majors for post-Madrasah Aliyah students, and (3) to design a decision support system that simplifies and speeds up the process of analyzing student data to determine suitable college majors. The system was developed using the PHP programming language, applying the C4.5 Decision Tree algorithm. Decision Tree is a machine learning algorithm that makes decisions using a set of rules structured like a tree. It employs conditional statements, with branches representing different decision-making steps aimed at producing optimal results. The process involves calculating entropy, split information, and gain ratio. This study, which focused on 12th-grade students during the odd semester, successfully identified appropriate engineering majors for each student.
Geographic Information System for Mapping Drug Abuse Areas in Lhokseumawe City Using the Average Linkage Method Syintia, Icut; Fuadi, Wahyu; Yunizar, Zara
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.17804

Abstract

Aceh is one of the provinces in Indonesia where the development of drug abuse has increased. The system that runs at BNN Lhokseumawe City in recording data and information about drug abuse cases has not been integrated with the mapping of drug abuse areas. Therefore, BNN and Lhokseumawe City Police need a drug abuse area mapping system in the Lhokseumawe City area. This research aims to build a webgis-based geographic information system using the Google Maps API for map visualization. The data mining method used is Average Linkage, clustering is done based on the number of cases, number of suspects and population in each sub-district in Lhokseumawe City. Cluster 1 consists of 1 sub-district, namely Banda Sakti, which in cluster 1 has a relatively high average value compared to clusters 2 and 3 so that it is included in a very vulnerable level. In cluster 2 consists of 2 sub-districts, namely Muara Satu and Muara Dua, because this cluster has a medium average value compared to clusters 1 and 3 so that it is included in the vulnerable level. Whereas the cluster in cluster 3 consists of 1 sub-district, namely Blang Mangat, which in cluster 3 has a relatively lower average value than clusters 1 and 2 so that it is included in the moderately vulnerable level.
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA, JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.20383

Abstract

Decision Support System for Selecting the Best Facial Wash Brand for Acne-Prone Skin Using the Fuzzy Analytical Hierarchy Process (F-AHP) Method Armaya, Devira Yuda; Rosnita, Lidya; Asrianda, Asrianda; Rachman, Aulia; Azhari, Muhammad
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i1.19542

Abstract

Acne is a problem that is often experienced by women. The factors that trigger acne skin problems are due to the pores on the facial skin that are clogged with oil, and the presence of bacteria. This research was conducted to provide a decision support system in recommending brands facial washthe best for acne prone skin types through the value of the intensity of importance of criteria such as price, packaging form, packaging size, active ingredient content, and packaging design. The final result of the calculation process using the method fuzzy AHP produces the lowest to the highest weight value for each brandfacial wash. And the final ranking data shows that there are 5 brand recommendations facial wash with the highest value of the other alternatives. That is there is an alternative code A04 which has the highest value asfacial wash the best for acne prone skin types, namely the brand is The Body Shop Tea Tree Skin Clearing Facial Wash with a total value of 7.663, and followed by alternative code A13 namely is Some By Mi AHA BHA PHA with a total value of 7.663, alternative code A07 is Miracle Cleansing with a total value of 7.337, the alternative code A15 is Ponds Anti Bacterial Facal Foam with a total value of 7.326, and the last alternative code A14 is Emina MS Pimple Acne Solutonwith a total score of 6.663.

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