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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 154 Documents
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Video shot boundary detection based on frames objects comparison and scale-invariant feature transform technique Ibrahim, Noor Khalid; Abduljabbar, Zinah Sadeq
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p130-139

Abstract

The most popular source of data on the Internet is video which has a lot of information. Automating the administration, indexing, and retrieval of movies is the goal of video structure analysis, which uses content-based video indexing and retrieval. Video analysis requires the ability to recognize shot changes since video shot boundary recognition is a preliminary stage in the indexing, browsing, and retrieval of video material. A method for shot boundary detection (SBD) is suggested in this situation. This work proposes a shot boundary detection system with three stages. In the first stage, multiple images are read in temporal sequence and transformed into grayscale images. Based on correlation value comparison, the number of redundant frames in the same shots is decreased, from this point on, the amount of time and computational complexity is reduced. Then, in the second stage, a candidate transition is identified by comparing the objects of successive frames and analyzing the differences between the objects using the standard deviation metric. In the last stage, the cut transition is decided upon by matching key points using a scale-invariant feature transform (SIFT). The proposed system achieved an accuracy of 0.97 according to the F-score while minimizing time consumption.
Implementation of automation configuration of enterprise networks as software defined network Prasetyo, Lindo; Prihandi, Ifan; Rifqi, Muhammad; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p99-111

Abstract

Software defined network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. In Cisco system, the SDN concept is implemented in Cisco Application Centric Infrastructure (Cisco ACI), which by default can be configured through the main controller, namely the Application Policy Infrastructure Controller (APIC). Conventional configuration on Cisco ACI creates problems, i.e.: the large number of required configurations causes the increase of time required for configuration and the risk of misconfiguration due to repetitive works. This problem reduces the productivity of network engineers in managing Cisco system. In overcoming these problems, this research work proposes an automation tool for Cisco ACI configuration using Ansible and Python as an SDN implementation for optimizing enterprise network configuration. The SDN is implemented and experimented at PT. NTT Indonesia Technology network, as a case study. The experimental result shows the proposed SDN successfully performs multiple routers configurations accurately and automatically. Observations on manual configuration takes 50 minutes and automatic configuration takes 6 minutes, thus, the proposed SDN achieves 833.33% improvement.
Acoustic echo cancellation system based on Laguerre method and neural network Nguyen, Duy-Thao; Nguyen, Thanh-Nghia
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p186-194

Abstract

Acoustic echo cancellation (AEC) is a fundamental requirement of signal processing to increase the quality of teleconferences. In this paper, a system that combines the Laguerre method with neural networks is proposed for AEC. In particular, the signal is processed using the Laguerre method to effectively handle nonlinear transmission line system. The results after applying the Laguerre method are then fed into a neural network for training and acoustic echo cancellation. The proposed system is tested on both linear and nonlinear transmission lines. Simulation results show that combining the Laguerre method with neural networks is highly effective for AEC in both linear and nonlinear transmission lines system. The AEC results obtained by the proposed method achieves a significant improvement in nonlinear transmission lines and it is the basis for building a practical echo cancellation system.
Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting Wahyuningsih, Tri; Iriani, Ade; Dwi Purnomo, Hindriyanto; Sembiring, Irwan
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p29-37

Abstract

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students. 
Clustering of uninhabitable houses using the optimized apriori algorithm Al-Khowarizmi, Al-Khowarizmi; Nasution, Marah Doly; Sary, Yoshida; Bela, Bela
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p150-159

Abstract

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.
Fingerprint based smart door lock system using Arduino and smartphone application Sutikno, Tole; Faqih Ubaidillah, Moh Ainur; Arsadiando, Watra; Purnama, Hendril Satrian
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p91-98

Abstract

In 2023, crime cases in Indonesia reached 105,133. Cases of theft with aggravation dominate the majority of cases. Everyone is concerned about safety, but doors are typically opened and closed using physical keys. This is vulnerable to being tampered with with fake keys, which can lead to house break-ins and theft. In this research, we propose a fingerprint-based wireless door lock design using Arduino and a smartphone. We offer this solution as a preventive measure to reduce the high rate of theft in homes or other buildings. The devices used are Arduino UNO R3, fingerprint sensor, HC-05 Bluetooth module, buzzer, and door lock solenoid. The results of the fingerprint-based wireless door lock using Arduino and a smartphone can function well, with an average response time of 1.20 seconds. Furthermore, testing the HC-05 Bluetooth when sending signals to a smartphone shows that it can read data accurately with an average response time of 1.54 seconds.
Implementing lee's model to apply fuzzy time series in forecasting bitcoin price Farida, Yuniar; Ainiyah, Lailatul
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p72-83

Abstract

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.
Development of learning videos for natural science subjects in junior high schools Siswosuharjo, Partono; Al-Bahra, Al-Bahra; Sunarya, Po Abas
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p160-167

Abstract

The purpose of this study was to determine the development procedure and the feasibility of learning media for whiteboard animation in Natural Sciences subjects at SMP Padindi, Tangerang Regency. This study uses a research and development (RD) approach. The development model in this study is the analysis design development implementation evaluation (ADDIE) model. The feasibility test is carried out by means of individual testing (one to one) on 3 experts, namely material experts, learning experts, and media experts, as well as 3 students. In addition, a small group test was also carried out on 9 students. The results showed that: i) the material expert test was 87.5%, the learning expert was 85%, the media expert was 84.44%, 3 students were 88.84%, and the small group was 90%; and ii) this whiteboard animation learning media is suitable for use based on the results of media trials by experts and students.
Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters Munsarif, Muhammad; Saman, Muhammad
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p38-45

Abstract

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%. 
Machine learning-based anomaly detection for smart home networks under adversarial attack Rejito, Juli; Stiawan, Deris; Alshaflut, Ahmed; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p122-129

Abstract

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

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