International Journal on Information and Communication Technology (IJoICT) <p align="justify">International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.</p> <p align="justify">IJoICT includes novel ideas on ICT, state of the art technique implementations, and study cases on developing countries. Each article is featured with details of the proposed method including dataset and external link for program or codes. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Information and Communication Technology (ICT).&nbsp;&nbsp;The Journal is published by the <a href="" target="_blank" rel="noopener">School of Computing</a>, Telkom University, Bandung, Indonesia. Accepted paper will be immediately available online (open access) without any publication fee.</p> School of Computing, Telkom University en-US International Journal on Information and Communication Technology (IJoICT) 2356-5462 Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT. Non-Line of Sight LoRa –Based Localization using RSSI-Kalman-Filter and Trilateration <p>The method that analyzes in this research is the combination of the Received Signal Strength Indicator (RSSI) with the Trilateration Method. This research also filtered the RSSI value using the Kalman filter method for smoothing data. The localization system traditionally based on Global Positioning System (GPS) device. However, GPS technology not working well in Non-line-of-sight (NLOS) like an indoor location or mountain area. The other way to implement the localization system is by using LoRa technology. This technology used radio frequency to communicate with each other node. The radiofrequency has a measurement value in the form of signal strength. These parameters, when combined with the trilateration method, can be used as a localization system. After implementation and testing, the system can work well compared with the GPS system for localization. RMSE is used to calculate error distance on these methods, the result from three methods used, the value from RSSI with Kalman filter have a close result to actual position, then value GPS follows with close result from Kalman filter, and the last one is RSSI without Kalman filter.</p> Thirafi Wian Anugrah Andrian Rakhmatsyah Aulia Arif Wardana Copyright (c) 2020 Thirafi Wian Anugrah, Andrian Rakhmatsyah, Aulia Arif Wardana 2020-07-02 2020-07-02 6 2 52 63 10.21108/IJOICT.2020.00.495 Analysis of Voice Changes in Anti Forensic Activities Case Study: Voice Changer with Telephone Effect <p>Voice recordings can be changed in various ways, either intentionally or unintentionally, one of which is by using a voice changer. Reference voice recordings and suspect voice recordings will be more difficult to analyze if suspect voice recordings are changed using a voice changer application under certain effects such as telephone effect. Voice Changer can be one form of activity that can be carried out by anti-forensics, making it difficult for investigators to investigate if the voice recording is changed with telephone effect. This study has two types of recordings, namely the reference voice recording (unknown sample) and suspect voice recording (known sample) that has been changed using a voice changer application with telephone effect. Investigations were carried out based on data results extraction and analysis using pitch, formant, and spectrogram using the Analysis of variance (ANOVA) method and the likelihood ratio method. The results of this study indicate that the application of voice changer can be one form of activity that can be carried out by anti-forensics so that it can be difficult for investigators to conduct investigations on sound recording evidence. This research may help forensic communities, especially investigators to conduct investigations on sound recording.</p> Abiyan Bagus Baskoro Niken Cahyani Aji Gautama Putrada Copyright (c) 2020 Abiyan Bagus Baskoro, Niken Cahyani, Aji Gautama Putrada 2020-10-21 2020-10-21 6 2 64 77 10.25124/ijoict.v6i2.508 A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps <p>In this study, we demonstrate the role of visualization to facilitate forensic analysis goal in interpreting metadata of evidence of interest to answer who, what, why, when, where, and how an incident occurred. Two mobile Instant Messaging (IM) applications (i.e. WhatsApp and Line) were deployed as a case study.&nbsp; Subsequently, a tool – W*W Visualizer – was designed and developed with the aims to analyze and visualize the connection of evidence metadata, text frequency and word count, and display report of analysis activities. The tool is developed by adopting Object-Oriented Software Development Model with Visual Studio platform and C# language were used to develop the system. Our findings show that W*W Visualizer could transform the data of the chat database into a visual form, for example graph, chart and word cloud. The tool also allows the user to perform search feature such as searching based on keyword and timestamp from the IM chat history. It is expected that outcomes from this study would significantly influence digital forensics practitioners in analyzing and interpreting evidence data, and judicial authorities in understanding the presentation of evidence.&nbsp;</p> Wee Sern Ong Nurul Hidayah Ab Rahman Copyright (c) 2020 Wee Sern Ong, Nurul Hidayah Ab Rahman 2020-11-21 2020-11-21 6 2 78 87 10.21108/IJOICT.2020.62.530 Sales Demand Forecasting Using One of Multivariate Markov Chain Model Parameter <p>The imbalance between demand and supply is frequently occurred in a market. This is due to the availability of goods that cannot match with the demand or the growth rate of customer. This is not preferable since the profit is not on the track. In contrast, the goods are probably over supplied so that company has to expense additional cost for extra storage. Both situations can be anticipated if the demand is precisely estimated. Therefore, in this study we will estimate demand in market situation by implementing multivariate Markov chain model. Multivariate Markov chain model is popular model for forecasting by observing current state in various applications. This model is compatible with 5 data sequences (product types) defined as product A, product B, product C, product D and product E, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the highest transition probability value for the sales demand in a company is found at the transition probability matrix from product C to product C, from very fast moving to very fast-moving condition, which had the highest probability value 0.625 with the highest frequency 105 times.</p> Annisa Martina Copyright (c) 2020 Annisa Martina 2020-12-31 2020-12-31 6 2 88 93 10.21108/IJOICT.2020.62.533 The Foreign Exchange Rate Prediction Using Long-Short Term Memory <p>The foreign exchange market is a global financial market that is influenced by economic, political, and psychological factors that are interconnected in complex ways. This complexity makes the foreign exchange market a difficult time-series prediction. At the end of 2019, the world was faced with the COVID-19 pandemic that has not only affected public health but also the foreign exchange market, which makes the trading behaviour affected. Long Short-Term Memory network (LSTM) is a type of recurrent neural network (RNN) that can solve long-term dependencies and is suitable to be a financial time-series model. This study implemented the LSTM model to predict the foreign exchange rate at a timeframe of 1 hour and daily in 2020 to get the best hyperparameter based on the RMSE evaluation results. Furthermore, with the obtained hyperparameters, the prediction result of 2020 was then compared with the 2018 and 2019 prediction results. The best RMSE result was obtained in 1-hour timeframe and when 2020’s RMSE result was compared to 2018’s and 2019’s RMSE result, the prediction of 2019 gave the best RMSE result. The LSTM model is able to achieve good results in the 2020 prediction, proven by the RMSE result which is 0.00135.</p> Hasna Haifa Zahrah Siti Sa’adah Rita Rismala Copyright (c) 2020 Hasna Haifa Zahrah 2021-01-05 2021-01-05 6 2 94 105 10.21108/IJOICT.2020.62.538