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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 443 Documents
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Penerapan Market Basket Analysis Data Mining Pada Penjualan Batik dengan Menerapkan Algoritma Apriori Soepriyono, Gatot
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5198

Abstract

Batik is a cloth that is depicted by applying wax to the cloth and processing it in a certain way. In certain batik companies, there are products that are characteristic of that company. As time goes by, problems arise with the sale of batik due to lack of proper stock availability in shops, making it difficult for customers to order batik. This problem that occurs is certainly a problem that must be resolved. If the available stock of goods does not match the customer's wishes, the goods will be piled up in the shop's stock. Apart from that, if the customer does not find a batik model that suits his wishes, it will cause the customer to switch to another shop. The sales results are reported in the form of a ledger or entered into a computer. The report produced is a sales transaction data report. Data mining itself is a process of processing quite large amounts of data. In the future, the data recorded in the ledger can be used as information in determining business strategies for batik sales. Market Basket Analysis aims to manage customer data or sales data. The a priori algorithm is an association part of mining data. A priori algorithms can help in forming candidate item combinations. From the results of the research carried out, there is 1 combination of items that meets the support value of 30%, namely items T09 and T12, where the support value obtained is 30.76% and with a confidence value of 100%.
Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means Dinata, Ferdian Arya; Nazir, Alwis; Fikry, Muhammad; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5200

Abstract

Ceramics has become one goods that consumers show interest in every year, so many companies are interested in selling ceramics. However, ceramic sales must meet and balance changing customer needs as well as problems found regarding ceramic products and customers, such as a lack of stock of ceramic products which results in customers not placing orders and product sales not meeting targets. So it is necessary to group ceramics to anticipate the risks that the company will accept by utilizing the data mining process using past data. This research uses the K-Means method found in data mining. The objective of this research is to group determine sales of brands that have potential for additional stock in the future and to test the data using the DBI (Davies Bouldin Index) which is carried out by testing the distance values between clusters through a series of experiments. This research uses data for the last 1 year from January 2022 to December 2022 with a total of 156 data using 9 attributes, namely brand, item code (FT, WT) and size (40x40, 25x25, 50x50, 25x40, 60x60, 20x40). The results of the research using the K-Means method, the best-selling brand is cluster 2, the best-selling brand is cluster 1 and the best-selling brand is cluster 0. The best-selling brand is HRM, the best-selling brand is VALENSIA and the best-selling brand is MCC. Test results using the DBI method with a validity of 01.013 show that the best cluster is obtained at k=3 using the elbow method. It is hoped that this research will contribute to related companies as support for decision making.
Prediksi Prediksi Perpindahan Pelanggan Pada Toko Online Menggunakan Metode Tree-Based Gradient Boosted Models Sholeha, Selfia Hafidatus; Faid, Mochammad; Yaqin, Moh. Ainol
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5215

Abstract

Customers are a critical asset to a company's success and ensuring their satisfaction is paramount. However, continuous churn can lead to reduced value flowing from customers, potentially jeopardizing a company's competitive advantage. Customer churn, where consumers choose products from other brands, is influenced by various factors such as promotion, price, product availability, and customer satisfaction levels. While much of the research on churn prediction is concentrated in the telecommunications, retail, and banking industries and only a few have conducted churn prediction research on online stores. This research aims to utilize data mining with a focus on machine learning algorithms, especially the tree-based gradient boosted models method that applies XGBoost, LightGBM, and CatBoost models, to predict customer churn in online stores. The research methodology involves data collection, data pre-processing, model selection and training, model evaluation, analysis and results. This research uses several libraries such as pandas library, numpy, matplotlib, and so on. The results of this study show that the XGBoost model achieved the highest accuracy in predicting customer churn, with an ROC curve of 0.66 and an accuracy value of 0.80032. The feature importance analysis highlights the gender variable as an important factor in model performance. This research contributes to improving customer service, minimizing churn, and ultimately increasing company profitability in the online store sector. Suggestions for future research include expanding data sources, testing with more evaluation metrics, exploring additional churn factors and comparing with other prediction methods for validation.
Analisis Sentimen Hasil Pemilu (Quick Count) Calon Presiden dan Wakil Presiden 2024 di Media Sosial Media X Menggunakan Metode Bidirectional Long Short-Term Memory (BiLSTM) Aini, Qurrotu; Hidayat, M. Noer Fadli; Tholib, Abu
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5223

Abstract

It is important to understand public opinions, attitudes and sentiments in relation to presidential and vice presidential candidates in the context of Indonesia's general elections. The fact that quick count results have become a major topic of conversation on social media, especially on platforms such as Twitter, shows how important it is to monitor people's views on election results. However, tweets that are free-form and use digital language are often difficult for the unfamiliar to understand, which can lead to the spread of misinformation or inaccurate views. Sentiment analysis is therefore key in understanding people's views on election results. This research proposes the use of the Bidirectional Long Short Term-Memory (BiLSTM) method to analyse sentiment related to the quick count results of the 2024 presidential and vice presidential elections on X social media. This sentiment analysis aims to classify texts into positive, negative, or neutral categories. The purpose of this study is to measure the sentiment value and accuracy of the BiLSTM method in sentiment analysis of election results. Data was collected by scraping X social media using the keywords "quick count results of 2024 presidential election" and "results of 2024 presidential election", resulting in 1348 tweets. Preprocessing included cleaning, case folding, normalisation, tokenisation, stopword removal, and stemming. Sentiments were labelled using the Vader lexicon dictionary. BiLSTM modelling was performed by dividing the data into 70% for training and 30% for testing. The results showed that neutral sentiment had the highest percentage at 92.86%, followed by positive sentiment at 3.83% and negative at 3.31%. The BiLSTM model achieved an accuracy of 86.89% with an overall accuracy of 97%. The highest precision, recall, and F1-score values were found in the neutral class, at 98%, 99%, and 99% respectively. This research proves that BiLSTM is an effective method for sentiment analysis of complex texts such as election results.
Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels Simanjuntak, Nurchaya; Saragih, Raymond Erz; Pernando, Yonky
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5123

Abstract

In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the potential of employing YOLOv8, a cutting-edge deep learning model, to automate Ambarella fruit ripeness classification. This work focuses on the YOLOv8n, YOLOv8s, and YOLOv8m, lightweight models within the YOLOv8 family. Our results are promising: all three models achieved 100% accuracy on the training set, with YOLOv8s demonstrating the lowest loss at 0.00286. The web application was utilised to deploy the trained models, allowing users to upload images of Ambarella fruit and run the model for inference.
Comparative Analysis of Machine Learning Models for Classifying Human DNA Sequences: Performance Metrics and Strategic Recommendations Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5168

Abstract

This study presents a comprehensive evaluation of seven machine learning models applied to the classification of human DNA sequences, highlighting their performance and potential applications in genomics. We explored Logistic Regression, Support Vector Machines (SVM), Random Forest, Decision Trees, Gradient Boosting, Naive Bayes, and XGBoost, using a 5-fold StratifiedKFold cross-validation method to ensure robustness and reliability in our findings. Naive Bayes demonstrated exceptional performance with near-perfect accuracy, precision, recall, and F1 scores, suggesting its suitability for rapid and efficient genomic classification. Logistic Regression also showed high efficacy, proving effective even in multi-class classifications of complex genetic data. Conversely, Decision Trees and SVM struggled with overfitting and computational efficiency, respectively, indicating the need for careful parameter tuning and optimization in practical applications. The study addresses these challenges and proposes strategies for enhancing model robustness and computational efficiency, such as advanced regularization techniques and hybrid modeling approaches. These insights not only aid in selecting appropriate models for specific genomic tasks but also pave the way for future research into integrating machine learning with genomic science to advance personalized medicine and genetic research. The findings encourage ongoing refinement of these models to unlock further potential in genomic applications.
Comparative Analysis of Deep Learning Architectures for DNA Sequence Classification: Performance Evaluation and Model Insights Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5170

Abstract

The classification of DNA sequences using deep learning models offers promising avenues for advancements in genomics and personalized medicine. This study provides a comprehensive evaluation of several deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Bidirectional LSTMs (BiLSTMs), and hybrid models combining CNNs with various recurrent networks, to classify human DNA sequences into functional categories. We employed a dataset of approximately 100,000 labeled sequences, ensuring a balanced representation across seven distinct classes to facilitate a fair comparison of model performance. Each model was assessed based on accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The CNN model demonstrated superior accuracy (74.86%) and the highest AUC (94.64%), indicating its effectiveness in capturing spatial patterns within sequences. LSTM and GRU models showed commendable performance, particularly in balancing precision and recall, suggesting their capability in managing sequential dependencies. However, hybrid models did not perform as expected, showing lower overall metrics, which highlighted challenges in model integration and complexity management. The findings suggest that while CNNs excel in feature extraction, sequence-based models like LSTMs and GRUs provide valuable capabilities in capturing long-range dependencies, essential for comprehensive genomic analysis. The study underscores the need for optimized hybrid models and further research into model robustness and generalizability.
Analisis Sentimen Media Sosial Twitter Terhadap Calon Presiden RI Tahun 2024 Menggunakan Klasifikasi Algoritma Naïve Bayes Effendi, Muhammad Makmun; Zy, Ahmad Turmudi; Arwan, Asep
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5210

Abstract

The progress of social media is currently being felt by many Indonesian people, one of the social media that is often used is Twitter, which is a media for posting information. Currently the viral post is the election of Presidential Candidates (capres) of the Republic of Indonesia which will be held in 2024, in line with this, the General Election Commission (KPU) is holding a presidential candidate debate which will be held on various television media in Indonesia and from the results of this debate the Indonesian people usually give opinions or comments on the debate from the positive and negative sides of the presidential candidates who appeared at that time, namely Anis, Prabowo and Ganjar Pranowo. To find out the results of sentiment towards the presidential candidates, the researchers carried out an analysis using a classification of tweets containing public sentiment towards the 2024 presidential candidacy, namely Anis, Prabowo and Ganjar with the classification method used in this research is Naive Bayes Classification (NBC). Anies Baswedan dataset 61.35% of Twitter users have negative comments and 39.65% of Twitter users have positive comments, Ganjar Pranowo dataset 59.12% of Twitter users have negative comments and 41.88% of Twitter users have positive comments, Ganjar Prabowo Subianto dataset 49.25% Twitter users commented negatively and 51.75% of Twitter users commented positively. Comparing the results of the three presidential candidates, Anies Baswedan's accuracy value is smaller than the other two candidates because Anies Baswedan has more negative comments than the other two candidates. Anies Baswedan got an accuracy value of 67.23%, Prabowo Subianto 83.42% and Ganjar Pranowo 88.15%. The amount of data affects the results of sentiment analysis, the more data the better the accuracy value obtained.
Sistem Pakar Diagnosa Gangguan Tidur pada Anak Menggunakan Naïve Bayes Kurniawan, Bagus Dwi; Akbar, Mutaqin
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5267

Abstract

Humans have a need for sleep that can be said to be very important, especially as children begin to develop. Sleep helps children become smarter, producing hormones that boost energy storage, increase muscle stamina, agility, intelligence, cognitive function, and long-term memory storage are all positively affected by sleep. To identify sleep disorders in children, parents usually need to consult a pediatrician, which can be expensive and time consuming. With an expert system, the system can relieve and help parents in detecting sleep disorders in their children by selecting symptom options in the system, then the system will give the final result of the child's sleep disorder with the highest probability based on the symptoms presented, as well as providing the appropriate solution. This expert system uses the Naïve Bayes method, which is a simple probabilistic classification. This method uses machine learning that relies on probability calculations. The system covers 31 symptoms of child sleep disturbances as well as the types of sleep disorders studied include Sleep Apnea, Insomnia, Narcolepsy, Enuresis, Night Terror, Nightmare, and Sleepwalking. Based on testing with 20 case data from experts, the system achieved a 95% accuracy level. Although there are some expert system results that show two disturbances with one of which corresponds to the result of an expert showing one disturbence, the result is still considered "Suitable".
Deteksi Outlier Hasil Clustering Algoritma K-Medoids Menggunakan Metode Boxplot Pada Data KIP Kuliah Simorangkir, Elsya Sabrina Asmita; Siahaan, Andysah Putera Utama; Marlina, Leni; Nasution, Darmeli; Sitorus, Zulham
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5479

Abstract

In the process of forming clusters with the K-Medoids algorithm, cluster result anomalies often occur, such as outliers. This value appears as a revelation in existing data patterns. Outliers occur due to measurement errors, rare events, or due to other unexpected factors. In this research, the dataset used is data on prospective KIP recipient students at Budi Darma University, where there is a high level of interest in KIP Kuliah while the quota is limited, which means that KIP Kuliah administrators sometimes have difficulty determining which students are eligible to receive KIP Kuliah. For this reason, the K-Medoids clustering technique was used to cluster data on 54 prospective students who were eligible to receive KIP Kuliah Merdeka and those who were not eligible. From the cluster results, outlier detection was carried out using the box plot method with the aim of finding out whether each cluster member was actually in the appropriate cluster or not. The result is that the data cluster is divided into 2 (K-2). In the max min centroid selection, cluster I consists of 52 members and cluster II consists of 2 members, where the outlier data consists of 3 data, while in random centroid selection (python), cluster I consists of 36 members and cluster II 18 members with data The outlier consists of 4 members. The accuracy of the clustering results between max min and random centroid selection has an accuracy of 64.81%, and the outlier accuracy is 75%.

Page 35 of 45 | Total Record : 443

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