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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 40 Documents
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Search results for , issue "Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Data Balancing Approach Using Combine Sampling on Sentiment Analysis With K-Nearest Neighbor Kondy, Evlyn Pricilia; Siswanto, Siswanto; Ilyas, Nirwan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4013

Abstract

One of the topics that has been discussed on twitter is the rules regarding the removal of masks. However, there's a chance that the data from Twitter contains unequal data classes. An unequal amount of data can cause the classification process to malfunction. Combining under- and oversampling techniques is known as combine sampling, and it is a data-balancing strategy. The research's data consists of Indonesian tweets using the hashtag "The Policy of Removing Masks." In this study, the classification approach was K-Nearest Neighbor, while the oversampling and undersampling techniques were SMOTE and Tomek Links. The purpose of this research is to classify sentiment using the K-Nearest Neighbor algorithm and to use combine sampling to balance the amount of training data in the two classes that are not yet balanced. 234 training data with a positive sentiment and 652 training data with a negative sentiment were obtained after the data was divided. Due to an imbalance in the quantity of training data between the two classes, the positive class's data is minor and the negative class's data is major. The quantity of training data are 613 in the positive class and 613 in the negative class obtained following the combine sampling. Following the balancing of data between the two classes, sentiment classification was performed, yielding accuracy of 60.4%, precision of 78.5%, and recall of 65%. The reason for the accuracy number of 60.4% is because machine learning misinterpreted a tweet regarding Indonesia's mask removal policy, leading to incorrect classification.
Revealing the Relationship of Batik Motifs Using Convolutional Neural Network Najar, Abdul Mahatir; Abu, Maulidyani; Ratianingsih, Rina; Jaya, Agus Indra
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4480

Abstract

This study explores the use of Convolutional Neural Network to identify and classify regional batik motifs, a significant aspect of Indonesian cultural heritage. The CNN model was optimized with Adam optimizer and used to extract distinctive features from the batik patterns. Subsequently, a hierarchical clustering method was employed to construct a relationship tree depicting the link between batik motifs based on their region. The research findings demonstrate that the CNN model effectively classifies batik motifs with an accuracy of up to 88%. The study provides insights into the intricate connections between regional batik designs and contributes to the preservation and understanding of Indonesia's cultural heritage.
Spearman Rank Correlation Analysis to Assess Satisfaction with Study Locations at Tadika CERIA Octavia, Annisa Suci; Utomo, Fandy Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4375

Abstract

One of the activities that is routinely held during the learning process is filling out questionnaires by parents to determine the quality of Tadika CERIA education and services. The results of this questionnaire were then analyzed using the Spearman Rank Correlation method. This study aims to apply correlation analysis using Python to evaluate student satisfaction with the learning location at Tadika CERIA. Utilizing a dataset of survey responses that includes variables such as cleanliness, quality of teaching, and communication, analysis was conducted to identify key factors influencing student satisfaction. This approach not only shows the practical application of Python in data processing and statistical analysis, but also provides valuable insights for school administrators in improving the quality of the learning environment. The results show a positive correlation between certain aspects and student satisfaction, underscoring the importance of a deep understanding of student preferences and needs in the development of a conducive learning environment. This research makes an important contribution in the context of early childhood education, offering a foundation for further research in an effort to improve the holistic educational experience.
Feature Extraction Analysis for Diabetic Retinopathy Detection Using Machine Learning Techniques Costaner, Loneli; Lisnawita, Lisnawita; Guntoro, Guntoro; Abdullah, Abdullah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4600

Abstract

Diabetic retinopathy is a serious complication of diabetes that can lead to blindness if not detected and treated early. Automated detection of diabetic retinopathy requires effective feature extraction techniques to enhance diagnostic accuracy. This study aims to develop a method for detecting diabetic retinopathy by utilizing Local Binary Pattern (LBP) combined with wavelet transform, and then classifying the extracted features using Support Vector Machine (SVM). The approach includes feature extraction from retinal images using LBP and wavelet transform. The extracted features are subsequently classified with SVM to evaluate performance in detecting diabetic retinopathy. Analysis results show that the dominant feature is found in the fifth row with a value of 0.57006, indicating the effectiveness of the LBP method in feature extraction. The developed model demonstrates high performance with an accuracy of 95.59%, precision of 96%, recall of 97.96%, and F1-score of 96.97%. The combination of feature extraction methods with SVM proves to be effective and reliable in detecting diabetic retinopathy, offering low error rates and high accuracy, thus potentially serving as a valuable tool in clinical diagnosis
Fuzzy tahani Implementation for Food Nutritional Status in Achieving Balanced Nutritional Dietary Dewi, Ika Novita; Priyo Utomo, Rino Agung; Sani, Ramadhan Rakhmat
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3644

Abstract

Fulfilling nutritional needs with the Recommended Dietary Allowances (RDA) shows the average value of the number of vitamins, protein and other nutrients the body needs to function properly. Each person's nutritional needs are different, so the RDA can be calculated based on different age groups and gender. In general, there are still many people who do not know the nutritional value of food and consume food without considering whether it meets the body's needs. This usually happens because calculating the RDA value is not yet familiar to do. Efforts are needed to increase awareness about the importance of a balanced diet through RDA calculations. Calculation and determination of nutritional status using the RDA number serves as a measuring tool to monitor whether a person's nutritional intake is in accordance with daily needs. This research developed a web-based application to calculate RDA numbers and group RDA numbers into nutritional status of less, enough, or more. Determination of nutritional status is carried out using the fuzzy tahani method, by displaying the results in percentage form, so that users can easily see the proportion or percentage of nutritional status obtained. This application not only calculates nutritional values, but provides a food record feature to help users manage healthy eating patterns.
Optimization of K-Means Clustering Method for Drug Grouping at Mertoyudan I Health Center, Magelang Astuti, Dwi; Muqorobin, Muqorobin
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4541

Abstract

Health centers as health service providers have an important role in ensuring adequate drug availability for the community. However, drug management in health centers often faces obstacles, including the use of manual methods in stock monitoring, which can lead to unavailability or accumulation of unused drugs. This study aims to improve the efficiency of drug management in health centers through the application of the K-Means Clustering method. This method is used to group drug stocks into three main clusters: heavy drugs, moderate drugs, and light drugs. With this grouping, it is expected that pharmacists can more easily determine the type of drug, monitor drug availability in a timely manner, and manage drug procurement according to patient needs. Research methods include literature studies, data collection and analysis, system modeling design, and system testing and evaluation. The results of this study are expected to improve the efficiency of drug management in health centers, so that health services to the community can be more optimal. The results of testing using the Silhouette Coefficient (SC) method produced an average SC of 0.97 which means strong structure, so it can be concluded that it has a strong structure.
Risk Management Analysis of PT XYZ Using COBIT 2019 with Domain EDM03, APO12, APO13, and DSS05 Yulita, Riskila; Tambotoh, Johan Jimmy Carter
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4430

Abstract

Technology that continues to develop indirectly forces people to adapt to these developments. The vital role of technology is becoming increasingly felt during the COVID-19 pandemic when all world activities are paralyzed and only allowed to communicate online. However, the enormous benefits of technology are also directly proportional to the risks that may occur. Therefore, IT Risk Management is needed to mitigate potential sources of threat. This research aims to analyze IT risk management by measuring the level of capability, gap analysis, and providing recommendations for improvement using the COBIT 2019 framework to support PT XYZ's work performance and IT security. Researchers used qualitative methods with data collection techniques through observation, interviews, and questionnaires. The results showed that the risk management domain that was the research focus EDM03, APO12, APO13, and DSS05 had a gap between the expected capabilities and what was happening in the company. Therefore, improvement recommendations are needed, such as determining the level of IT risk and socializing it with stakeholders, recording IT risk events, building an Information Security Management System (ISMS), implementing a network filtering mechanism, and regularly evaluating information about potential new threats by reviewing product security and vendor or third-party services.
Prediction of Tsunamis in Indonesia Using an Optimized Neural Network with SMOTE Siregar, Aisyah Anjani Putri; Sudianto, Fauzan Bayu Hera; Nooraeni, Rani
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4263

Abstract

Tsunamis have the potential to have a large impact on the environment, therefore early detection and preparation for tsunami need to be carried out to reduce the impact of casualties and losses incurred. This research aims to predict tsunami events due to large earthquakes in Indonesia as a form of early detection. The optimized neural network method is used in research to classify tsunami events in Indonesia in 2000-2023 for large earthquakes with strength more than 5 magnitudes. The research results show that the neural network structure formed consists of an input layer, a hidden layer, and an output layer. The results of the evaluation of the neural network model with SMOTE obtained an accuracy value of 99.43%, precision of 96.31%, and an F1 score of 97.86%, which means the resulting model is good. Therefore, an optimized neural networks can be applied as a warning system in various regions to detect potential tsunami events in the future.
Feature Extraction Optimization to Improve Naïve Bayes Accuracy in Sentiment Analysis of Bulukumba Tourism Objects Setiawan, Darmawan; Umar, Najirah; Nur, M. Adnan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4580

Abstract

This research employs social media (Twitter) to apply sentiment analysis ascertain the degree of public satisfaction with the Bulukumba tourist attraction. Unstructured text data is a major challenge in sentiment analysis. For this reason, implementing the Naïve Bayes algorithm is an effective approach for conquering this challenge because of its ability to handle text data well. This study aims to evaluate the performance of multinomial Naïve Bayes by testing a combination of minimum document frequency (min-df) and maximum document frequency (max-df) parameter values in determining the level of accuracy. This analysis stage includes collecting data from Twitter related to the Bulukumba tourist attraction. Preprocessing carried out includes data cleaning, casefolding, text normalization, tokenization, stopword removal, and stemming. Feature extraction using Count Vectorizer and TF-IDF weighting. The process ends with 10-Fold Cross-Validation by separating the data into training data and test data for sentiment analysis classification, as well as evaluation using the Confusion Matrix. In this research, there are 10 test scenarios with various combinations of min-df and max-df. The values of employed min-df consists of 0.001, 0.002, 0.005, 0.01, 0.02 and max-df consists of 0.5 and 0.8. The results of implementing Multinomial Naïve Bayes in this test show that classification accuracy increases with effective min-df and max-df parameter settings. The greatest accuracy was 0.7910 in testing a combination of min-df parameter values of 0.001 and max-df 0.8. Meanwhile, the average accuracy for each test was obtained the highest value of 0.7272 with min-df of 0.002 and max-df of 0.5 and 0.8 respectively.
Analisis Sentimen Ulasan Kawah Ijen Menggunakan Naïve Bayes Classification dan Optimasi Oversampling Hizham, Fadhel Akhmad; Asy'ari, Hasyim; Urrochman, Maysas Yafi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4490

Abstract

Sentiment analysis is a method that applies text mining concepts to provide classifications that have polarity that is positive, negative, or neutral from each sentence or document. In this context, the purpose of this research is to analyse the sentiment of user reviews related to the Ijen crater tourist attractions found on the Google Maps platform. This research is conducted in three main stages: first, Data Collection and Preprocessing by taking data samples obtained from Ijen Crater reviews contained on Google Maps; second Optimisation and Classification by changing the minority class samples to be almost equal to the majority class by randomly duplicating the minority class samples, third, classification performance measurement using confusion matrix. The test is conducted by comparing the performance between NBC classification without optimisation and NBC classification with SMOTE and ADASYN optimisation. The performance results show that SMOTE-optimised NBC classification provides the best improvement in accuracy by 6.74% compared to the performance of ordinary NBC and NBC added with ADASYN.

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