Pub Date : 2024-05-20DOI: 10.30595/juita.v12i1.20210
Joko Siswanto, Irwan Sembiring, Adi Setiawan, Iwan Setyawan
The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.
{"title":"Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model","authors":"Joko Siswanto, Irwan Sembiring, Adi Setiawan, Iwan Setyawan","doi":"10.30595/juita.v12i1.20210","DOIUrl":"https://doi.org/10.30595/juita.v12i1.20210","url":null,"abstract":"The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"30 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The application of biclustering analysis to mixed data is still relatively new. Initially, biclustering analysis was primarily used on gene expression data that has an interval scale. In this research, we will transform ordinal categorical variables into interval scales using the Method of Successive Interval (MSI). The BCBimax algorithm will be applied in this study with several binarization experiments that produce the smallest Mean Square Residual (MSR) at the predetermined column and row thresholds. Next, a row and column threshold test will be carried out to find the optimal bicluster threshold. The existence of different interests in the variables for international market potential and the number of Indonesian export destination countries is the reason for the need for identification regarding the mapping of destination countries based on international trade potential. The study's results with the median threshold of all data found that the optimal MSR is at the threshold of row 7 and column 2. The number of biclusters formed is 9 which covers 74.7% of countries. Most countries in the bicluster come from the European Continent and a few countries from the African Continent are included in the bicluster.
{"title":"BCBimax Biclustering Algorithm with Mixed-Type Data","authors":"Hanifa Izzati, Indahwati Indahwati, Anik Djuraidah","doi":"10.30595/juita.v12i1.21519","DOIUrl":"https://doi.org/10.30595/juita.v12i1.21519","url":null,"abstract":"The application of biclustering analysis to mixed data is still relatively new. Initially, biclustering analysis was primarily used on gene expression data that has an interval scale. In this research, we will transform ordinal categorical variables into interval scales using the Method of Successive Interval (MSI). The BCBimax algorithm will be applied in this study with several binarization experiments that produce the smallest Mean Square Residual (MSR) at the predetermined column and row thresholds. Next, a row and column threshold test will be carried out to find the optimal bicluster threshold. The existence of different interests in the variables for international market potential and the number of Indonesian export destination countries is the reason for the need for identification regarding the mapping of destination countries based on international trade potential. The study's results with the median threshold of all data found that the optimal MSR is at the threshold of row 7 and column 2. The number of biclusters formed is 9 which covers 74.7% of countries. Most countries in the bicluster come from the European Continent and a few countries from the African Continent are included in the bicluster.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"98 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.30595/juita.v12i1.20868
Dede Rizki Darmawan, R. Arifudin
DurroTalk, a chatbot for new student admissions at Pondok Pesantren Durrotu Ahlissunnah Waljamaah, Semarang, integrates a hybrid model with Recurrent Neural Network (RNN) and Decision Tree. RNN, the base model, employs Natural Language Processing (NLP) to understand sentence structure and context, overcoming vanishing gradient through LSTM layers. The Decision Tree normalizes words, addressing slang and synonyms. The hybrid model boosts chatbot accuracy by 9%, reaching 77% from the initial 68%. This research signifies progress in integrating artificial intelligence into traditional education, showcasing a chatbot adept at handling non-standard language. Decision Tree integration enhances overall performance, making the chatbot proficient in understanding user inputs and generating contextually relevant responses. This study exemplifies the potential of AI, particularly chatbot technology, in modernizing educational processes at traditional institutions.
{"title":"Enhancing Durrotalk Chatbot Accuracy Utilizing a Hybrid Model Based on Recurrent Neural Network (RNN) Algorithm and Decision Tree","authors":"Dede Rizki Darmawan, R. Arifudin","doi":"10.30595/juita.v12i1.20868","DOIUrl":"https://doi.org/10.30595/juita.v12i1.20868","url":null,"abstract":"DurroTalk, a chatbot for new student admissions at Pondok Pesantren Durrotu Ahlissunnah Waljamaah, Semarang, integrates a hybrid model with Recurrent Neural Network (RNN) and Decision Tree. RNN, the base model, employs Natural Language Processing (NLP) to understand sentence structure and context, overcoming vanishing gradient through LSTM layers. The Decision Tree normalizes words, addressing slang and synonyms. The hybrid model boosts chatbot accuracy by 9%, reaching 77% from the initial 68%. This research signifies progress in integrating artificial intelligence into traditional education, showcasing a chatbot adept at handling non-standard language. Decision Tree integration enhances overall performance, making the chatbot proficient in understanding user inputs and generating contextually relevant responses. This study exemplifies the potential of AI, particularly chatbot technology, in modernizing educational processes at traditional institutions.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-20DOI: 10.30595/juita.v12i1.21137
Tio Dharmawan, Danu Adi Nugroho, Muhammad Arief Hidayat
The age factor had a significant impact on human faces, potentially influencing the performance of existing gender classification systems. This research proposed a new method that combined local descriptors such as Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) with Self-Principal Component Analysis (Self-PCA) as a feature extraction technique. The use of Self-PCA was chosen for its ability to address the age factor in human facial images, while also leveraging local descriptors to capture features from these images. The primary focus was to compare the performance of Self-PCA with LPQ+Self-PCA, along with the additional comparison of LBP+Self-PCA, in the task of gender classification using facial images. Euclidean distance served as the classifier, and the evaluation was conducted using the FG-Net and ORL datasets. The combination of LPQ+Self-PCA showed an improvement in accuracy by 57.85% compared to the combination of LBP+Self-PCA, which provided an accuracy of 56.47%. Meanwhile, using Self-PCA alone gave an accuracy of 55.37% on the FG-Net. In contrast, on the ORL dataset, both combinations gave the same accuracy result as Self-PCA, which was 90.14%, for images without blurring.
{"title":"Face Gender Classification using Combination of LPQ-Self PCA","authors":"Tio Dharmawan, Danu Adi Nugroho, Muhammad Arief Hidayat","doi":"10.30595/juita.v12i1.21137","DOIUrl":"https://doi.org/10.30595/juita.v12i1.21137","url":null,"abstract":"The age factor had a significant impact on human faces, potentially influencing the performance of existing gender classification systems. This research proposed a new method that combined local descriptors such as Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) with Self-Principal Component Analysis (Self-PCA) as a feature extraction technique. The use of Self-PCA was chosen for its ability to address the age factor in human facial images, while also leveraging local descriptors to capture features from these images. The primary focus was to compare the performance of Self-PCA with LPQ+Self-PCA, along with the additional comparison of LBP+Self-PCA, in the task of gender classification using facial images. Euclidean distance served as the classifier, and the evaluation was conducted using the FG-Net and ORL datasets. The combination of LPQ+Self-PCA showed an improvement in accuracy by 57.85% compared to the combination of LBP+Self-PCA, which provided an accuracy of 56.47%. Meanwhile, using Self-PCA alone gave an accuracy of 55.37% on the FG-Net. In contrast, on the ORL dataset, both combinations gave the same accuracy result as Self-PCA, which was 90.14%, for images without blurring.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"70 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.18704
Oktafian Sultan Hakim, M. A. Zainuddin, S. Sukaridhoto, Agus Prayudi
Cyber-physical systems is integrated computation with the physical world. CPS increasing in a wide range of applications, from smart homes to smart buildings. Digital twins are promising way to solve challenges with combination of CPS, 3D technology, and IoT. The system provides users with immersive interfaces to control and interact with devices within the smart building environment. Blockchain was chosen to secure user data using cryptographic algorithms and ensure data protection against manipulation, spying, and theft. Average load testing data for digital twin platform implemented in smart buildings range from 1 to 11 floors. The results reveal a gradual increase in average test times as the buildings' size and complexity grow, with the following values: 5.663s for 1 floor until 11 floors 7.294s. The data obtained from of the blockchain test using Hyperledger Besu provide essential insights into the system's performance with several bandwidth that used in the system. Average time for each test trial ranged from 1.066 seconds to 2.006 seconds, showing slight variations based on the bandwidth used. However, transactions per second (TPS) values were relatively fast, ranging from 1.066 tps to 0.499 tps with positive aspect of the retention rate for all trials was 100% success.
{"title":"Digital Twin and Blockchain Extension in Smart Buildings Platform as Cyber-Physical Systems","authors":"Oktafian Sultan Hakim, M. A. Zainuddin, S. Sukaridhoto, Agus Prayudi","doi":"10.30595/juita.v11i2.18704","DOIUrl":"https://doi.org/10.30595/juita.v11i2.18704","url":null,"abstract":"Cyber-physical systems is integrated computation with the physical world. CPS increasing in a wide range of applications, from smart homes to smart buildings. Digital twins are promising way to solve challenges with combination of CPS, 3D technology, and IoT. The system provides users with immersive interfaces to control and interact with devices within the smart building environment. Blockchain was chosen to secure user data using cryptographic algorithms and ensure data protection against manipulation, spying, and theft. Average load testing data for digital twin platform implemented in smart buildings range from 1 to 11 floors. The results reveal a gradual increase in average test times as the buildings' size and complexity grow, with the following values: 5.663s for 1 floor until 11 floors 7.294s. The data obtained from of the blockchain test using Hyperledger Besu provide essential insights into the system's performance with several bandwidth that used in the system. Average time for each test trial ranged from 1.066 seconds to 2.006 seconds, showing slight variations based on the bandwidth used. However, transactions per second (TPS) values were relatively fast, ranging from 1.066 tps to 0.499 tps with positive aspect of the retention rate for all trials was 100% success.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"63 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.17348
Ronald Sebastian, Christina Juliane
Stroke is a circulation disorder in the brain that can cause symptoms and signs related to the affected part of the brain and is the leading cause of death and disability in Indonesia. Everyone is at risk of experiencing a stroke, and it is important to recognize and manage risk factors. Data Mining techniques can help in the extraction and prediction of information, as well as finding hidden patterns in stroke medical data. The dataset used in this research comes from Kaggle and is imbalanced, so the SMOTE Upsampling technique is used to address this imbalance issue. The results of the study conclude that the use of SMOTE technique in the C4.5, NB, and KNN algorithms can increase precision, recall, and AUC. The C4.5 algorithm and SMOTE technique as the best performing algorithm were selected for testing new data, and the results show that the model created can predict stroke risk more accurately than the C4.5 model without SMOTE. However, it should be noted that based on the author's interview with one of the medical practitioners, the model cannot be directly used in medical practice because the observations in the medical field to determine factors related to stroke are highly complex. Thus, a new understanding revealed that predicting stroke in a practical setting is highly complex. While data mining can be used as a predictive tool in the initial stage for predictions in the general population, it is strongly recommended to undergo direct examination by doctors in a hospital to obtain more accurate and comprehensive medical evaluations.
{"title":"Comparison of Data Mining Classification Algorithms for Stroke Disease Prediction Using the SMOTE Upsampling Method","authors":"Ronald Sebastian, Christina Juliane","doi":"10.30595/juita.v11i2.17348","DOIUrl":"https://doi.org/10.30595/juita.v11i2.17348","url":null,"abstract":"Stroke is a circulation disorder in the brain that can cause symptoms and signs related to the affected part of the brain and is the leading cause of death and disability in Indonesia. Everyone is at risk of experiencing a stroke, and it is important to recognize and manage risk factors. Data Mining techniques can help in the extraction and prediction of information, as well as finding hidden patterns in stroke medical data. The dataset used in this research comes from Kaggle and is imbalanced, so the SMOTE Upsampling technique is used to address this imbalance issue. The results of the study conclude that the use of SMOTE technique in the C4.5, NB, and KNN algorithms can increase precision, recall, and AUC. The C4.5 algorithm and SMOTE technique as the best performing algorithm were selected for testing new data, and the results show that the model created can predict stroke risk more accurately than the C4.5 model without SMOTE. However, it should be noted that based on the author's interview with one of the medical practitioners, the model cannot be directly used in medical practice because the observations in the medical field to determine factors related to stroke are highly complex. Thus, a new understanding revealed that predicting stroke in a practical setting is highly complex. While data mining can be used as a predictive tool in the initial stage for predictions in the general population, it is strongly recommended to undergo direct examination by doctors in a hospital to obtain more accurate and comprehensive medical evaluations.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.18582
Angelina Rumuy, R. Delima, Kuncoro Probo Saputra, J. Purwadi
A Stroke is a cerebrovascular disease characterized by impaired brain function due to damage or death of brain tissue caused by reduced or blocked blood and oxygen flow to the brain. Expert systems can be used as learning aids for medical students to diagnose stroke. Medical records of stroke cases can be reused as a reference for diagnosing stroke when there are new cases, known as the case-based reasoning (CBR) method. This study implements the Minkowski distance similarity method in CBR to calculate the similarity value between cases, where each similar case has the same solution. This study uses the Minkowski distance similarity method in CBR to obtain the most optimal value of r and the most appropriate threshold value in the expert system for stroke diagnosis. The diagnosis process is carried out by inputting the patient's condition, symptoms, and risk factors. Then the system will calculate the similarity value and take the case with the highest similarity value as the solution, providing that the similarity value must be greater than or equal to the threshold value. Based on system testing, the best accuracy value was achieved by applying a threshold value of 75 with an r value of 3 or 4, with an accuracy rate of 88,89%, a recall value of 88%, and a precision of 100%.
{"title":"Application of the Minkowski Distance Similarity Method in Case-Based Reasoning for Stroke Diagnosis","authors":"Angelina Rumuy, R. Delima, Kuncoro Probo Saputra, J. Purwadi","doi":"10.30595/juita.v11i2.18582","DOIUrl":"https://doi.org/10.30595/juita.v11i2.18582","url":null,"abstract":"A Stroke is a cerebrovascular disease characterized by impaired brain function due to damage or death of brain tissue caused by reduced or blocked blood and oxygen flow to the brain. Expert systems can be used as learning aids for medical students to diagnose stroke. Medical records of stroke cases can be reused as a reference for diagnosing stroke when there are new cases, known as the case-based reasoning (CBR) method. This study implements the Minkowski distance similarity method in CBR to calculate the similarity value between cases, where each similar case has the same solution. This study uses the Minkowski distance similarity method in CBR to obtain the most optimal value of r and the most appropriate threshold value in the expert system for stroke diagnosis. The diagnosis process is carried out by inputting the patient's condition, symptoms, and risk factors. Then the system will calculate the similarity value and take the case with the highest similarity value as the solution, providing that the similarity value must be greater than or equal to the threshold value. Based on system testing, the best accuracy value was achieved by applying a threshold value of 75 with an r value of 3 or 4, with an accuracy rate of 88,89%, a recall value of 88%, and a precision of 100%.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.17336
Wiga Maaulana Baihaqi, Arif Munandar
The development of the Internet has played a significant role in various aspects of life and has generated vast amounts of data, including student comments about universities. The challenge in analyzing comment data is the large number of students providing feedback, which makes manual analysis impractical. The purpose of this study is to analyze the performance evaluation of universities by students in terms of positive and negative sentiments, with the aim of assessing the level of student satisfaction with all elements and areas of university operations. This research utilized the Naïve Bayes algorithm and the IndoBERT model to build a classification model based on questionnaire data, starting from the data collection process, data preprocessing, feature extraction, modeling, and evaluation. The results of the IndoBERT model demonstrated the best performance, with an accuracy of 85%. The IndoBERT model effectively recognizes sentiments in text, distinguishing between positive and negative comments regarding university performance.
{"title":"Sentiment Analysis of Student Comment on the College Performance Evaluation Questionnaire Using Naïve Bayes and IndoBERT","authors":"Wiga Maaulana Baihaqi, Arif Munandar","doi":"10.30595/juita.v11i2.17336","DOIUrl":"https://doi.org/10.30595/juita.v11i2.17336","url":null,"abstract":"The development of the Internet has played a significant role in various aspects of life and has generated vast amounts of data, including student comments about universities. The challenge in analyzing comment data is the large number of students providing feedback, which makes manual analysis impractical. The purpose of this study is to analyze the performance evaluation of universities by students in terms of positive and negative sentiments, with the aim of assessing the level of student satisfaction with all elements and areas of university operations. This research utilized the Naïve Bayes algorithm and the IndoBERT model to build a classification model based on questionnaire data, starting from the data collection process, data preprocessing, feature extraction, modeling, and evaluation. The results of the IndoBERT model demonstrated the best performance, with an accuracy of 85%. The IndoBERT model effectively recognizes sentiments in text, distinguishing between positive and negative comments regarding university performance.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"54 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.17094
Hariadi Yutanto, Gaguk Suprianto, Yusuf Effendi
The technology of utilizing hotspot networks has developed quite rapidly. In its development, internet technology uses a more flexible Mikrotik hotspot because it provides convenience for administrators and users. The object of this study is the hotspot network of Hayam Wuruk University (UHW) Perbanas. The goal is to develop a leaderboard design as a medium for monitoring internet use through the UHW Perbanas hotspot. Its application is through the integration of mikrotik with the web service API as a ranking of internet users against three categories of activities, namely downloads, uploads and internet usage times on each day and month. Each of these categories has 20 users. The test method uses a black box. Hasil testing states that the system is successfully operating, so that it can be implemented in the context of decision making by the management of UHW Perbanas.
{"title":"Leaderboard Application as A Ranking Media for Internet Users","authors":"Hariadi Yutanto, Gaguk Suprianto, Yusuf Effendi","doi":"10.30595/juita.v11i2.17094","DOIUrl":"https://doi.org/10.30595/juita.v11i2.17094","url":null,"abstract":"The technology of utilizing hotspot networks has developed quite rapidly. In its development, internet technology uses a more flexible Mikrotik hotspot because it provides convenience for administrators and users. The object of this study is the hotspot network of Hayam Wuruk University (UHW) Perbanas. The goal is to develop a leaderboard design as a medium for monitoring internet use through the UHW Perbanas hotspot. Its application is through the integration of mikrotik with the web service API as a ranking of internet users against three categories of activities, namely downloads, uploads and internet usage times on each day and month. Each of these categories has 20 users. The test method uses a black box. Hasil testing states that the system is successfully operating, so that it can be implemented in the context of decision making by the management of UHW Perbanas.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"28 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.30595/juita.v11i2.16828
Leni Fitriani, Dewi Tresnawati, M. Iqbal, Ismail Safei Pamungkas
Game development is currently quite rapid. Now games can be played by various groups, because many games now contain not just games, but there are also games with educational content. The educational game that will be made in this study is a website-based Sundanese proverb game, this type of game will be multiplayer so that players can compete with other players. The purpose of this research is to make a Sundanese proverb educational multiplayer game that can be played simultaneously with many players, so that it can introduce the regional language, namely Sundanese, to the wider community. The technology used in making this game is Socket.IO and Node.JS, using these technologies can make end users interact in real time. In making this game using the Game Development Life Cycle (GDLC) methodology with the stages of initialization, pre-production, production, testing, beta and release. The results obtained in this research are website-based Sundanese proverb educational games that can be used without taking up much space on the device.
{"title":"Multiplayer Game Guessing Sunda’s Proverb Using Socket.Io And Node.Js","authors":"Leni Fitriani, Dewi Tresnawati, M. Iqbal, Ismail Safei Pamungkas","doi":"10.30595/juita.v11i2.16828","DOIUrl":"https://doi.org/10.30595/juita.v11i2.16828","url":null,"abstract":"Game development is currently quite rapid. Now games can be played by various groups, because many games now contain not just games, but there are also games with educational content. The educational game that will be made in this study is a website-based Sundanese proverb game, this type of game will be multiplayer so that players can compete with other players. The purpose of this research is to make a Sundanese proverb educational multiplayer game that can be played simultaneously with many players, so that it can introduce the regional language, namely Sundanese, to the wider community. The technology used in making this game is Socket.IO and Node.JS, using these technologies can make end users interact in real time. In making this game using the Game Development Life Cycle (GDLC) methodology with the stages of initialization, pre-production, production, testing, beta and release. The results obtained in this research are website-based Sundanese proverb educational games that can be used without taking up much space on the device.","PeriodicalId":151254,"journal":{"name":"JUITA : Jurnal Informatika","volume":"65 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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