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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 161 Documents
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Enhancing Performance Using New Hybrid Intrusion Detection System Candra Supriadi; Charli Sitinjak; Fujiama Diapoldo Silalahi; Nia Dharma Pertiwi; Sigit Umar Anggono
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.134

Abstract

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.
A DIGITAL PRINTING APPLICATION AS AN EXPRESSION IDENTIFICATION SYSTEM. Arman Arman; Prasetya Prasetya; Feny Nurvita Arifany; Fertilia Budi Pradnyaparamita; Joni Laksito
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.135

Abstract

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.
IoT-Based System of Monitoring Realtime Air Quality with MQ135 and Automatic Chicken Feeding Dani Sasmoko; Reni Veliyanti; Rozi Azwar Pradana
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.136

Abstract

IoT technology is useful for chicken farming to control the condition of the farm concerning some problems such as odour, temperature fluctuations and feeding time system. This study used MQ135 to detect ammonia odour level, DHT11 to check room temperature, and RTC3231 to regulate feeding time. The data obtained from the sensor was sent by wemos to the fire base and MySQL to be read on android so that it can be monitored directly by the breeder. When the ammonia level is above normal it will turn on the odour fan. If DHT11 reaches the value above 300C, it will turn on the cooling fan, and turn on the heating light when the temperature is below 250C. As RTC3231 sets the time at 07.00 and 14.00, the chicken feeder will automatically open to feed the chickens according to the time set. Table 1 and 2 show that the experiment obtained a value of 100% working well. This study succeeded in monitoring environmental conditions through Android and executing automatic feeding at the predetermined time. Thus, it can be concluded that the use of IoT technology for monitoring and feeding system automation in chicken farms is highly recommended.
The Efficient Approach in Peer-to-Peer Systems to Achieve High Efficiency Lukman Santoso; Marcus Gunadi Wibawa; Muhamad Syarifudin; Priyadi Priyadi; Titi Christiana
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.137

Abstract

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.
ANALYSIS RENDEZVOUS-BASED TECHNIQUES RELATE TO POWER CONSERVATION Agus Waryanto; Antonius Juniadhi Soekendar; Ely Andra Widharta; Greget Widhiati; Irdha Yunianto
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.138

Abstract

Power conservation is a vibrant area of research on wireless sensor networks. Several research papers have shown that using mobile sync in the sensor field, which collects data from sensor nodes via single or multi-hop communication, can save significant energy. However, sinks move slowly, which increases sensor network delays, especially in delay-sensitive applications. To address this issue, various rendezvous-based techniques are described in which a subset of sensor nodes are selected from the field as rendezvous points (RPs). The remaining nodes transfer the captured data to the next RP, where the data is buffered. The RP is then used to build a path for the mobile sink to tour and collect the buffered data. This article describes various rendezvous-based techniques and analyzes their strengths and weaknesses in terms of energy savings.
Analysis of Ag2+ and Cu2+ electroplating on the aluminum layer thickness level: A reanalysis Aming Sungkowo; Yanuar Zulardiansyah Arief; Rosyid Ridlo Al-Hakim
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.139

Abstract

We reanalyze the effect of silver (Ag2+) and copper (Cu2+) coating solutions for the thickness of the layer on aluminum (Al) materials with increased electrical currents 0.4A, 0.8A, 1A, 1.2A, and 1.4A and increased thickness layer (10µm, 20µm, 30µm, 40µm, and 50µm), as well as the previous study was conducted. We used the electroplating method and thickness test, as well as the Brinell hardness test for both coating solutions. The results show statistically significant (p-value < 0.05, one-tailed) between high electric current and aluminum (Al) coating process with silver (Ag2+) and copper (Cu2+), as well as silver (Ag2+), get the faster coating process time. The Brinell hardness test shows a statistically significant difference (p-value < 0.05, one-tailed) between the high thickness layer and HB value (Ag-coated and Cu-coated).
High electric current and hours can increase layer thickness and decrease white rust corrosion using Zn2+ electroplating Slamet Riyadi; Yanuar Zulardiansyah Arief; Antonius Darma Setiawan; Agung Pangestu; Rosyid Ridlo Al-Hakim
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.140

Abstract

Electroplating was the process of coating metal surfaces using the electrochemical method. We used alkaline zinc (Zn2+) plating that was anti-corrosion coating, cheapest, evenly adhesion, as well as better-looking crushing. This study aims to test and measure the thickness of the layer on spark plugs with variations in different electrical currents 300, 400, and 500A and increased hours during the coating process, investigate the corrosion resistance of white rust on the surface and analyze the changes in alkaline zinc concentration and temperature that affect the thickness of the layer, respectively. The results, such as 1st sample 13 pcs, 300A, and thickness of 7.26-micron with white rust 9 pcs. 2nd sample 13 pcs, 400A, and thickness of 9.15-micron white rust 5 pcs. 3rd sample 13 pcs, 500A, and thickness of 12.75-micron white rust 3 pcs. The high electric current (500A) and 45 hours of the experiment would influence the lowest white rust corrosion level. The high alkaline zinc solution with an optimum 36°C solution temperature and 500A electric current would undoubtedly deposit the white rust until 3 pcs.
BLACK BOX APPROACH TO MONITORING CONTAINER MICROSERVICES IN FOG COMPUTING Danang Danang; Nuris Dwi Setiawan; Indra Ava Adianta
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.141

Abstract

In recent years IoT has developed very rapidly. IoT devices are used to monitor and control physical objects to transform the physical world into intelligent spaces with computing and communication capabilities. Compared to cloud computing, fog computing is used to support latency-sensitive applications at the edge of the network which allows client requests to be processed faster. This study aims to propose a monitoring framework for containerized black box microservices in a fog computing environment to evaluate CPU overhead, as well as to determine the operating status, service characteristics, and dependencies of each container. This study proposes a monitoring framework to integrate computing resource usage and run-time information from service interactions using a black box approach that seeks to integrate service-level information and computing resource information into the same framework. The proposed framework is limited to observing information monitoring after the server receives a request. This study uses JMeter to simulate user actions, which send requests to the server, and this research assumes the user knows the IP address of the server. For container monitoring methods in fog computing, all are indirect monitoring methods. The results of this study indicate that the proposed framework can provide operational data for visualization that can help system administrators evaluate the status of running containers using a black box approach. System administrators do not need to understand and modify target microservices to gather service characteristics from containerized microservices. Regarding future research, it is suggested to expand the exploration of modified system information, and that part of the container management tool code can be pre-tried so that the framework proposed in this study can provide real-time quantitative indexes for the load balancing algorithm to help optimize the load balancing algorithm.
HYBRID MODEL MACHINE LEARNING FOR DETECTING HOAXES Budi Hartono; Munifah; Sindhu Rakasiwi
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.142

Abstract

Unlimited availability of content provided by users on social media and websites facilitates aggregation around a broad range of people's interests, worldviews, and common narratives. However, over time, the internet, which is a source of information, has become a source of hoaxes. Since the public is commonly flooded with information, they occasionally find it difficult to distinguish misinformation disseminated on net platforms from true information. They may also rely massively on information providers or platform social media to collect information, but these providers usually do not verify their sources. The purpose of this research is to propose the use of machine learning techniques to establish hybrid models for detecting hoaxes. The research methodology used here is a feature extraction experiment, in which a series of features will be analyzed and grouped in an experiment to detect hoax news and hoax, especially in the political sphere by considering five modalities. The outcome of this research indicates that the relation between publisher Prejudice and the attitude of hyper-biased news sources makes them more possible than other sources to spread illusive articles, besides that the correlation between political Prejudice and news credibility is also very strong. This shows that the experiment using a hybrid model to detect hoaxes works. well. To achieve even better results in future research, it is highly recommended to analyze user-based features in terms of attitudes, topics, or credibility.
MACHINE LEARNING TECHNIQUE FOR CREDIT CARD SCAM DETECTION Fujiama Diapoldo Silalahi; Toni Wijanarko Adi Putra; Edy Siswanto
Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i1.143

Abstract

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC. The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly. Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.

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