International Journal on Information and Communication Technology (IJoICT)

Performance Analysis of TCP Fractional Window Increment and Adaptive Fractional Window on IEEE 802.11 Multihop Ad Hoc Networks

Arnas Sofyan — 2023-07-25

TCP, a layer 4 transport protocol, plays a crucial role in both wireless and wired networks. However, its performance in wireless networks is often unsatisfactory due to issues such as bandwidth limitations and utility problems with lower network layers. The mobility effect further exacerbates TCP's performance, as it fails to distinguish between connection failure and congestion-induced connection loss. In response to this challenge, researchers have explored potential solutions and found that TCP FeW outperforms the existing TCP NewReno. Building upon this background, this paper aims to simulate and analyze the performance of TCP AFW and TCP FeW in an IEEE 802.11 network. The simulations conducted using ns2 in a limited environment with random mobile scenarios reveal that TCP AFW achieves a 1.12% higher throughput compared to FeW, even with minimal modifications.

Implementation of the Learner Centered Design method and the personality approach (Case Study: Redesigning The Interface mobile LMS Tel-U)

Kurniawan Malik Ibrahim — 2023-07-17

The Mobile LMS Tel-U is an e-learning platform developed by Telkom University to support the learning process. However, it still requires more demand from Telkom University students. Usability evaluation was conducted on twelve students using the System Usability Scale (SUS), resulting in a score of 46.5. Interviews and observations revealed interface problems on the dashboard, material, grades, and quizzes. This study aims to redesign the mobile LMS Tel-U interface using the Learner-Centered Design method and incorporating a personality approach by categorizing students into introverts and extroverts. Designing based on personality groups acknowledges the differences in interface design preferences and the relationship between personality and e-learning interface design. This approach yields two different interface designs, one for introverted students and one for extroverted students. The LCD method determines student needs in supporting the learning process. The redesigned interface's usability was evaluated using SUS to assess its appropriateness for students' learning needs. The study shows an average increase in usability scores of 80.4. The introverted student group achieved a usability score of 81, while the extroverted student group obtained a score of 80. Thus, the LCD method and personality approach effectively enhance the usability of distance learning applications (e-learning).

Static Code Analysis on The Effect of Virtual Secure Mode on Memory Acquisition with IDA

Nadja Adryana — 2023-06-09

Memory acquisition process is one of digital forensics act. There are several tools that support memory acquisition process. At this time, there is a feature named secure mode that can caused crash or error in memory acquisition tools system and caused the tools to be unusable, also the loss of the computer memory. This research is focusing on analyzing the acquisition tools that has error or crash when the device that is being used for memory acquisition is in secure mode. The analysis is being carried out using static code analysis method, which is one of the techniques of reverse engineering, using IDA. This study aims to find the cause of the crash or error in memory acquisition tools. The purpose of this study is to be useful for digital forensic tester in understanding the potential risk of the secure mode impact in acquisition process. The results of this study indicate that different operating system and different kernel which runs in the device are the reasons that memory acquisition tools cannot run properly on VSM environment being turned on.

Portfolio Optimization Based on Return Prediction and Semi Absolute Deviation (SAD)

Gharyni Nurkhair Mulyono — 2023-06-18

A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.

A STOCK PREDICTION SYSTEM USING TEKNIKAL INDICATORS WITH THE LSTM METHOD

Revelin Angger Saputra — 2023-06-30

The capital market industry in Indonesia is developing in a better direction so that the growth of new investors is also increasing. Until the end of February 2021, operational data from the Indonesian Stock Exchange (IDX) and data from the Indonesian Central Securities Depository (KSEI) recorded that the number of new capital market investors had increased by 16.35% or 634,350 investors, from the previous 3,880,753 investors. to 4,515,103 investors. The development of the capital market industry in Indonesia, which has increased investor interest in investing, is expected to mobilize public funds to support national economic development. Some companies that are familiar to the community are BCA, BNI, BRI and MANDIRI. This study attempts to forecast banking stock prices on the LQ45 index, using the Long Short-Term Memory (LSTM) method. LSTM is one of the Recurrent Neural Networks (RNN) which has good accuracy in predictions. The identified fields are Close, Open, RSI, MACD and MA. The evaluation method used in this prediction system is MAPE in the form of percent output.

Multi Aspect Sentiment Analysis of Mutual Funds Investment App Bibit Using BERT Method

Serly Setyani — 2023-07-05

With the rapid development of technology, an investor no longer needs to visit investment companies to make investments. Investors can conduct all investment transactions through their smartphone screens. Bibit is one investment application that can help investors invest in mutual funds. There are many reviews given by users every day, therefore, aspect-based sentiment analysis is needed to identify the aspects and sentiments of users from each review. BERT is one popular text classification method that currently has good performance. Therefore, aspect-based sentiment analysis will be carried out in this study using the BERT method with pre-trained IndoBERT on Bibit application reviews. From the multi-aspect sentiment analysis classification results, the service aspect had the highest average accuracy score of 0.92, the user satisfaction aspect had an average accuracy score of 0.87, and the system aspect had the lowest average accuracy score of 0.75. From the sentiment analysis results, the company can improve the system and service aspects of the Bibit application to provide better service & functionality.

Sentiment Analysis of Tourist Attraction Review from TripAdvisor Using CNN and LSTM

Kevin Adrian Manurung — 2023-07-24

The tourism sector has an important role in driving the economy. To find out the positive or negative responses of tourists, one of them is grouping through sentiment analysis using deep learning. The data used the tourist attraction dataset from TripAdvisor from several categories such as water and amusement park, nature, and museum. The methods used in this research are convolutional neural network (CNN) and long short-term memory (LSTM). In addition, Word2vec for feature extraction and Synthetic Minority Over-sampling (SMOTE) for handling imbalanced datasets will be used for this research. There are several scenarios used to perform sentiment analysis, with early stopping and with hyperparameter tuning using random search. The highest performance obtained on water and amusement park, nature, and museum category data is 83%, 97%, and 88% respectively for accuracy and 91%, 92%, and 93% respectively for F1-score. For the use of sentiment analysis methods, CNN can perform with the highest F1-score and LSTM can perform with the highest accuracy.