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Sujacka Retno
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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 50 Documents
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K Fold Cross Validation Analysis for Electricity Meter Classification at PLN Lhoksukon Using K-NN and SVM Methods Zuboili, Zuboili; Dinata, Rozzi Kesuma; Syahputra, Irwanda
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21340

Abstract

Electricity consumption continues to increase every year in line with the increase in national economic growth. Predicting current electricity demand is important to understand the overall electricity supplied to each region. The problem is that currently, the electricity supply in various areas of Lhoksukon has not matched the needs of the community. In addition, problems can arise if the power generated is less than the load power requirements, causing energy shortages in an area. To find out whether the electricity provided is appropriate or not, a classification using Supervised Learning method is used. After classification, we will use K-fold Cross Validation to measure how good the accuracy is between the methods. This study will use 200 electricity meter data consisting of 150 test data and 50 training data with a composition of 75%: 25%. The testing process where the data process that has been divided is then carried out in the testing process where the data process is obtained from manual calculations. So that in this study get results in the form of the K-NN method with 99.3% accuracy, 100% precision, 99.29% recall and the SVM method with 94.00% accuracy, 94.00% precision, 100% recall. And to find out how well the performance of the method is based on Supervised Learning method, it will be checked using K-Fold Cross Validation with the results of K-NN 99.53% and SVM 96.00%, with the conclusion that the K-Nearest Neighbor method has a better accuracy rate.
Classification of Asthma Diseases Using Machine Learning Models at Arun Hospital Muqarrabin, Khalis Al; Fadlisyah, Fadlisyah; Safari, T Mirzal
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21229

Abstract

Asthma is one of the chronic diseases that significantly affects the quality of life of patients. This study aims to classify asthma disease based on patient data from Arun Hospital using the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 330 patient data with attributes such as allergy, itchy throat, and shortness of breath. The data went through preprocessing, transformation, and normalization stages. The KNN model was tested with a value of k = 3, resulting in three main classifications: Mild Asthma, Moderate Asthma, and Severe Asthma. The evaluation results showed a high accuracy rate, with an average of more than 90%. In addition, the model was implemented in the form of a system that visualizes the dataset, KNN analysis, and model evaluation. These findings demonstrate the potential of the KNN algorithm to provide accurate predictions and support the diagnosis of asthma disease effectively.
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA, JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21358

Abstract

Diet Recommendation Application for Diabetes Patients Using the Preference Selection Index Method Siregar, Winda Ramadhani; Yunizar, Zara; Afrillia, Yesy
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.17810

Abstract

Diabetes mellitus is a chronic condition characterized by elevated blood glucose levels. Effective diet management is crucial for controlling this condition and preventing serious complications. This study aims to develop a meal recommendation application for diabetes patients using the Preference Selection Index (PSI) method. The data used include user identity, health conditions, food preferences, and the nutritional content of meal menus. The PSI implementation process involves several key steps: collecting user data, normalizing nutritional values based on the minimum and maximum values in the database, adjusting the criterion weights according to the user's health conditions and food preferences, and calculating the PSI for each meal menu. The study results show that this application can provide meal recommendations that match the nutritional needs and health conditions of users. From a total of 10 user data analyzed, 50% received "Red Bean Soup with Vegetables" as the best menu, 30% received "Grilled Chicken Breast with Vegetables," and 10% each received "Grilled Chicken with Green Beans" and "Quinoa Salad with Avocado." The conclusion of this study is that the PSI method is effective in helping diabetes patients select an optimal diet, which can assist in better managing their condition and improving their quality of life. Suggestions for future research include increasing the variability of nutritional data, integrating with wearable technology, and developing reminder and education features.
The Implementation of a Chatbot and Website Interface in Department of Development Economic Mulyadi, Rizki; Rosnita, Lidya; Rachman, Aulia; Azhari, Muhammad
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21315

Abstract

The issue arising from this activity is the need to optimize the existing CMS Joomla-based website to make it more responsive and interactive in line with modern technological developments. The update was carried out using SP Page Builder as the main tool for interface development, while the chatbot was implemented using JavaScript technology with the Levenshtein Distance algorithm to provide automatic information services to users. The result of this practical work includes updates to several key components of the website, including the homepage, which is now equipped with a dynamic banner, a message from the head of the department, highlights of the study program, and a news and announcement section. The faculty and staff pages have been optimized with more comprehensive information, while the gallery page has been redesigned with a responsive grid layout. The chatbot implementation successfully provides automated information services for common academic questions, such as class schedules, KRS (course registration), and scholarships. Overall, these updates have improved the accessibility of information and the user experience in accessing the Deparment of Development Economics website.
Devayan Language Translator Dictionary Application Using the Levenshtein Distance Method on Android Faturrahman, Puja; Ardian, Zalfie; Maryana, Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol 2, No 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.20124

Abstract

Indonesia is a country with a rich diversity of regional languages. Language is the primary tool of human communication, serving as a means to establish social relationships in daily life and as a medium for conveying information. One of the regional languages in Indonesia is Devayan, the native language of the people in Simeulue Regency. This language is used as a daily communication medium by the local residents. As one of Indonesia's archipelagic regions, Simeulue Island has various tourism potentials that attract tourists, workers, and students from outside the region. However, communication barriers often occur when visitors to Simeulue Island face difficulties interacting with the local community. Additionally, the language is gradually fading with the passage of time, as many young generations on Simeulue Island now have limited understanding of their regional language. Therefore, a dictionary application is needed to translate vocabulary from Devayan to Indonesian and vice versa. The Levenshtein Distance method is applied to the application's search feature, which has proven capable of correcting errors in input words and suggesting the closest words to users with an accuracy rate of 80.95%.
Security Analysis of Data Storage in Cloud-Based Digital Archive Management Systems Meri Nova Marito Br Sipahutar; Ade Linhar P; Sardo Pardingotan Sipayung
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22436

Abstract

In today's global era, archive management plays a crucial role in supporting operational efficiency and informed decision-making. The adoption of cloud computing offers an innovative solution for managing digital archives more effectively and efficiently. This journal discusses the implementation of cloud computing in digital archive management using MySQL as a relational database management system. Through this approach, archive data can be stored, accessed, and managed more securely while ensuring data integrity. The study also explores the advantages of MySQL in terms of performance, scalability, and ease of access. Implementation of this system in several organizations has shown significant improvements in archive management efficiency and a reduction in operational costs. This system helps organizations manage their digitized data effectively by utilizing cloud computing as a more affordable and reliable storage solution
Decision Support System for Determining Recipients of Direct Cash Assistance (BLT) Using Simple Additive Weighting in Meunasah Alue Village Mutia, Rosita; Hasdyna, Novia; Rahmat, Rahmat
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22526

Abstract

A Decision Support System (DSS) is a computer-based system that generates various decision alternatives to assist management in addressing both structured and unstructured problems using data and models. DSS can also be applied to determine recipients of Direct Cash Assistance (BLT), such as in Meunasah Alue Village. This system is expected to assist the village authorities in selecting BLT recipients more efficiently and effectively each year. The method used in this system is Simple Additive Weighting (SAW), which considers predefined criteria and applies weighted scoring. The aim of this research is to determine suitable BLT recipients in Meunasah Alue Village by using DSS to support accurate and fair decision-making. The results of this study show the top three candidates: Armiati with a score of 227.75, Muliadi with a score of 225.5, and Maimunah with a score of 181.5.
Analysis of Clustering Results for Crime Incident Data in Indonesia Using Fuzzy C-Means Retno, Sujacka; Hakimi, Musawer
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22565

Abstract

This study examines the clustering of crime incident data across Indonesia from 2000 to 2024 using the Fuzzy C-Means (FCM) algorithm, with a focus on the impact of data normalization. Comprehensive annual provincial crime statistics from Badan Pusat Statistik (BPS) were preprocessed to handle missing values and then standardized via the Standard Scaler. FCM clustering was performed separately on both the original and normalized datasets, with the number of clusters set to three. Cluster quality was evaluated over ten independent runs using five metrics: Davies-Bouldin Index (DBI), Silhouette Score (SS), Calinski-Harabasz Index (CH), Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Results indicate that normalization consistently yields lower DBI values (average 0.824 vs. 0.830) and higher SS (average 0.367 vs. 0.363) and CH scores (average 55.35 vs. 54.09), while ARI and NMI remain stable across treatments. These findings demonstrate that normalization enhances cluster compactness and separation without altering underlying data structures, leading to more interpretable and reliable groupings. By uncovering regional crime patterns and highlighting the methodological importance of preprocessing, this research provides actionable insights for policymakers and law enforcement agencies to allocate resources more effectively and develop targeted crime prevention strategies.
Utilizing K-Means Clustering for Grouping Student Achievement Data to Evaluate Learning Activeness Agusniar, Cut; Mulya Ulfa, Septia; Rhomadhona, Herfia
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22591

Abstract

Learning basically aims to foster student activity and creativity through various learning experiences and interactions. Teachers are an important part of the process of improving the quality of education. In addition, the success of the learning process depends on student activity. The world of education needs to improve the quality of students and their performance by using existing facilities, infrastructure and human resources. One way information systems can be used to improve student achievement and quality is by analyzing grades based on students' academic abilities, discipline and way of behaving.The aim of this research is to group students based on academic scores, disciplinary scores and attitude scores using the K-Means Clustering algorithm, so that the cluster results can be used as a reference in improving student scores in the next learning process. In this research, the elbow method was used to determine the optimal number of clusters. Students will be grouped into clusters. Visualization and correlation analysis between value variables is carried out to provide further insight into the distribution of data and the relationship between its values.

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