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All Journal JURNAL SISTEM INFORMASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics

Rizal Broer Bahaweres

Syarif Hidayatullah State Islamic University

Author-ID : 3174465

Computer Science & IT Electrical & Electronics Engineering Other

Published : 7 Documents Claim Missing Document

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WatsaQ: Repository of Al Hadith in Bahasa (Case Study: Hadith Bukhari) Atqia Aulia; Dewi Khairani; Rizal Broer Bahaweres; Nashrul Hakiem
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

The Hadith is one of the two sources of Islamic law after the Qur'an. It is a fact that there are a number of false hadith, recognised by Muslim scholars since the end of the first century of Hijra, and even earlier. In addition to the breadth of false hadith circulating among the public at this time, it is difficult to determine the source of authenticity and distinguish false from genuine. This is due to the configuration of the genuine documents which are revealed in Arabic. To that end, the authors have built a repository collection of hadith al- Bukhari in the Indonesian language. The hadith chosen have secured originality and standardisation has been applied that can assist users in learning the content of the hadith. The authors implemented a repository of translation in Bahasa of Bukhari Hadith using XML schema. To study the repository performance, we use a web presentation using PHP employing brute-force string match algorithms to display the search results based on keywords entered by the user. We analyse the results of our proposed repository implementation average searching time is faster by 0.85 milliseconds compared with the repository based on the unstructured one.

Analysis of Statement Branch and Loop Coverage in Software Testing with Genetic Algorithm Rizal Broer Bahaweres; Khoirunnisya Zawawi; Dewi Khairani; Nashrul Hakiem
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Software testing is one important aspect of the software development process. About 50% of the time and cost in the software development process used for software testing process. There are two methods of software testing, black-box testing and white-box testing. This research using white-box testing. Software testing can be done manually or automatically. Based on research conducted, genetic algorithm has been widely implemented in software testing, such as test data generator. The purpose of this study is to apply a genetic algorithm in software testing and comparing the results with manual testing, automated, and automated with genetic algorithm. The test parameters are coverage measurements (statement, branch and loop coverage) and the time of testing. The conclusion of this study is automated testing with genetic algorithm requires fewer time and test cases to achieve coverage of 100%

Software Defect Prediction Using Neural Network Based SMOTE Rizal Broer Bahaweres; Fajar Agustian; Irman Hermadi; Arif Imam Suroso; Yandra Arkeman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2090

Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The defect prediction software dataset naturally has a class imbalance problem with very few defective modules compared to non-defective modules. Class imbalance can reduce performance from classification. In this study, we applied the Neural Networks Based Synthetic Minority Over-sampling Technique (SMOTE) to overcome class imbalances in the six NASA datasets. Neural Network based on SMOTE is a combination of Neural Network and SMOTE with each hyperparameters that are optimized using random search. The results use a nested 5-cross validation show increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network based SMOTE with SMOTE + Traditional Machine Learning Algorithm. The Neural Network based SMOTE takes first place in the average rank.

Co-Authors Abdi Wahab Agustian, Fajar Arif Imam Suroso Arkeman, Yandra Atqia Aulia Aulia, Atqia Dewi Khairani Dewi Khairani Fajar Agustian Irman Hermadi Khairani, Dewi Khoirunnisya Zawawi Mudrik Alaydrus Nashrul Hakiem Nashrul Hakiem Nashrul Hakiem Oki Teguh Karya Tulus Hadianto Viva A Wahyudianto Wahyudianto Widi Prasetyo Yandra Arkeman Zawawi, Khoirunnisya

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Journal : Proceeding of the Electrical Engineering Computer Science and Informatics