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All Journal JURNAL SISTEM INFORMASI BISNIS Journal of Telematics and Informatics Jurnal Transformatika JUITA : Jurnal Informatika Al-Ihkam: Jurnal Hukum dan Pranata Sosial Proceeding of the Electrical Engineering Computer Science and Informatics TRANSISTOR Elektro dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian kepada Masyarakat Nusantara JAST Nusantara of Engineering (NOE) Jurnal Rekayasa Sistem Informasi dan Teknologi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)

Badieah Assegaf

Universitas Sultan Agung

Author-ID : 593189

Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Machine Learning Approaches on External Plagiarism Detection Imam Much Ibnu Subroto; Ali Selamat; Badieah Assegaf
Journal of Telematics and Informatics Vol 4, No 2: September 2016
Publisher : Universitas Islam Sultan Agung

External plagiarism detection is a technique that refers to the comparison between suspicious document and different sources. External plagiarism models are generally preceded by candidate document retrieval and further analysis and then performed to determine the plagiarism occurring. Currently most of the external plagiarism detection is using similarity measurement approaches that are expressed by a pair of sentences or phrase considered similar. Similarity techniques approach is more easily understood using a formula which compares term or token between the two documents. In contrast to the approach of machine learning techniques which refer to the pattern matching and cannot directly comparing token or term between two documents. This paper proposes some machine learning techniques such as k-nearest neighbors (KNN), support vector machine (SVM) and artificial neural network (ANN) for external plagiarism detection and comparing the result with Cosine similarity measurement approach. This paper presented density based that normalized by frequency as the pattern. The result showed that all machine learning approach used in this experiment has better performance in term of accuracy, precision and recall.

Student Academic Performance Prediction on Problem Based Learning Using Support Vector Machine and K-Nearest Neighbor Badieah Assegaf
Journal of Telematics and Informatics Vol 5, No 1: March 2017
Publisher : Universitas Islam Sultan Agung

Academic evaluation is an important process to know how well the learning process was conducted and also one of the decisive factors that can determine the quality of the higher education institution. Though it usually curative, the preventive effort is needed by predicting the performance of the student before the semester begin. This effort aimed to reduce the failure rate of the students in certain subjects and make it easier for the PBL tutor to create appropiate learning strategies before the tutorial class begin. The purpose of this work is to find the best data mining technique to predict student academic performance on PBL system between two data mining classification algorithms. This work applied and compared the performance of the classifier models built from Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). After preprocessed the dataset, the classifier models were developed and validated. The result shows that both algorithms were giving good accuracy by 97% and 95,52% respectively though SVM showing the best performance compared to KNN in F-Measure with 80%. The further deployment is needed to integrate the model with academic information system, so that academic evaluation can be easily done.

Sentiment Analysis of Indonesian Figure using Support Vector Machine Suharyo Herwasto; Imam Much Ibnu Subroto; Badieah Assegaf
Journal of Telematics and Informatics Vol 6, No 3: September 2018
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v6i3.230-237

On the political year 2018 will be mutually popping reverberated figures for Indonesian presidential candidate 2019. The figures recognition process generally are now using social media, so it would appear the opinions of social media users. Opinions that appeared not only contain positive and negative polarity, but also contain a sentence of subjective and objective. By using a machine learning algorithm, namely Support Vector Machine, made sentiment analysis. The results of the analysis of this sentiment more optimally use the kernel Linear with the F-Measure of Polarity 68%, 68%, 63%, and the F-Measure Subjectivity 73%, 77%, 75% for each figure Anies Baswedan, Joko Widodo, and Prabowo Subianto.

Co-Authors Ahmad Jalaluddin Ahmad Mujib Akmalia Fatimah Alamsyah Alamsyah Alfiah Nurul Fatimah Intan Pertiwi Ali Selamat Andi Riansyah Bahtiar, Thoriq Bayu Surarso Dewi Ayu Fatmawati Eko Saputra, Wardianto Ghozi Fidaul Haq Ghufron Imam Much Ibnu Subroto Jenny Putri Hapsari Lanang Bagus Prakarsa Muhammad Khosyi’in Muna Madrah Muna Yastuti Madrah Munaf Ismail Nadia Wulandari Nur Muhammad Syaifuddin Pratama, Nirmala Putri Rachmat Gernowo Riansyah, Andi Sam Farisa Chaerul Haviana Sri Arttini Dwi Prasetyawati Sri Mulyono Sri Mulyono Suharyo Herwasto Wardianto Eko Saputra

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