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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Securing E-mail Communication Using Hybrid Cryptosystem on Android-based Mobile Devices Teddy Mantoro; Andri Zakariya
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 4: August 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the most popular internet services is electronic mail (e-mail). By using mobile devices with internet connection, e-mail can be widely used by anyone to exchange information anywhere and anytime whether public or confidential. Unfortunately, there are some security issues with email communication; e-mail is sent in over open networks and e-mail is stored on potentially insecure mail servers. Moreover, e-mail has no integrity protection so the body can be undectected altered in transit or on the e-mail server. E-mail also has no data origin authentication, so people cannot be sure that the emails they receive are from the e-mail address owner. In order to solve this problem, this study proposes a secure method of e-mail communication on Android-based mobile devices using a hybrid cryptosystem which combines symmetric encryption, asymmetric encryption and hash function. The experimental results show that the proposed method succeeded in meeting those aspects of information security including confidentiality, data integrity, authentication, and non-repudiation. DOI: http://dx.doi.org/10.11591/telkomnika.v10i4.874
An automatic lexicon generation for indonesian news sentiment analysis: a case on governor elections in Indonesia Media A Ayu; Sony Surya Wijaya; Teddy Mantoro
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1555-1561

Abstract

Sentiment analysis has been popularly used in analyzing data from the internet.  One of the techniques used is lexicon based sentiment analysis.  Generating lexicon is not an easy process, and lexicon in Bahasa Indonesia is rarely available.  This paper proposes an automatic lexicon generation in Bahasa Indonesia for sentiment analysis purpose.  Experiments were performed using the generated lexicon for doing sentiment analysis on Indonesian political news about the 2018 governor election in three provinces in Indonesia. The conducted experiments show promising results where it can predict the candidate’s rank, the election winner, and the percentage of votes for each candidate with better accuracy than the previous work which used manually generated lexicon.
Weighted inverse document frequency and vector space model for hadith search engine Septya Egho Pratama; Wahyudin Darmalaksana; Dian Sa'adillah Maylawati; Hamdan Sugilar; Teddy Mantoro; Muhammad Ali Ramdhani
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp1004-1014

Abstract

Hadith is the second source of Islamic law after Qur’an which make many types and references of hadith need to be studied. However, there are not many Muslims know about it and many even have difficulties in studying hadiths. This study aims to build a hadith search engine from reliable source by utilizing Information Retrieval techniques. The structured representation of the text that used is Bag of Word (1-term) with the Weighted Inverse Document Frequency (WIDF) method to calculate the frequency of occurrence of each term before being converted in vector form with the Vector Space Model (VSM). Based on the experiment results using 380 texts of hadith, the recall value of WIDF and VSM is 96%, while precision value is just around 35.46%. This is because the structured representation for text that used is bag of words (1-gram) that can not maintain the meaning of text well).
Indonesian news classification using convolutional neural network Muhammad Ali Ramdhani; Dian Sa’adillah Maylawati; Teddy Mantoro
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp1000-1009

Abstract

Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in natural language processing (NLP) research area. In the current NLP research area, data mining (DM) or machine learning (ML) technique is popular, especially for deep learning (DL) method. This research aims to classify text data in the Indonesian language using convolutional neural network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase, stopword removal and stemming are particularly suitable for the Indonesian language. The experiment conducted using 472 Indonesian news text data from various sources with four categories: ‘hiburan’ (entertainment), ‘olahraga’ (sport), ‘tajuk utama’ (headline news), and ‘teknologi’ (technology). Based on the experiment and evaluation using 377 training data and 95 testing data, producing five models with ten epoch for each model, CNN has the best percentage of accuracy around 90,74% and loss value around 29,05% for 300 hidden layers in classifying the Indonesian News data.
Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran Ayu, Media Anugerah; Irawan, Edi; Mantoro, Teddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1469-1480

Abstract

The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran.  However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e.  most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it.   These insights could be useful for muslims in general and for people that doing research in related areas.  This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently.  As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.