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All Journal Systemic: Information System and Informatics Journal Jurnal Riset Ilmu Teknik
Victor Amrizal
Universitas Islam Negeri Syarif Hidayatullah
Computer Science & IT Engineering

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Rancang Bangun Service Application Program Interface Sistem Machine Learning Klasifikasi Teks Menggunakan Algoritma Support Vector Machine Ottoh Hidayatullah; Victor Amrizal; Arini
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i1.920

Abstract

Data shows very large numbers for Internet use in Indonesia. In the field of education, online libraries are an effort to facilitate researchers to search for references to research documents. Based on observations, UIN Jakarta already has a good repository of research documents, but the online research document repository does not fulfill the Knowledge Acquisition feature. This capability allows users to obtain knowledge information that is not easily accessible to users. This research build a machine learning system using the Support Vector Machine algorithm so that the system built can categorize documents based on the informatics research fields. This research also builds a system services API (Application Program Interface) so that data output from machine learning systems can be used by a variety of platforms and different operating system environments. The accuracy of the machine learning system in this study resulted in a percentage of classification accuracy of 73.2% with a parameter value of 0.9. At the preprocessing stage the selection of unigram-bigram is the best in this study. Preprocessing affects the level of classification of machine learning systems. Preprocessing using stemming improves the results of ability accuracy. The amount of data affects the accuracy of the machine learning classification ability, it can be seen when the data is increased to 488 accuracy increases to 74.49. When the experiment was done again so that the data increased to 492 data, the accuracy increased again to 77.78%.
Implementation of Adaptive Neuro-Fuzzy Inference System and Image Processing for Design Applications Paper Age Prediction Valeria Cynthia Dewi; Victor Amrizal; Fenty Eka Muzayyana Agustin
Jurnal Riset Ilmu Teknik Vol. 1 No. 1 (2023): Jurnal Riset Ilmu Teknik
Publisher : Lembaga Penelitian dan Ilmu Pengetahuan JEPIP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59976/jurit.v1i1.6

Abstract

The development of technology today is widely misused by some people who intend to forge paper on documents and books. One way to find out the authenticity of a paper is by knowing its age. The age of paper can be known in several ways: carbon dating, uranium dating, and potassium-argon dating. But these methods still have weaknesses, requiring sophisticated equipment at a high cost, long processes to get results and limited access. To solve this problem, researchers made an application that can identify the age range of a sheet of paper with a faster process, low cost and does not have to be used by laboratory employees alone. The application is a Paper Age Prediction Application made desktop-based, using the MATLAB programming language with the Anfis Sugeno (TSK) Gaussian membership function method. Image processing by taking the average values of C, M, Y, and K from 70 images used as a database and will be trained with ANFIS. The research method uses interviews, observations, and literature studies—the prototype application development method. The test results showed an application success rate in identifying 60 data that had been trained by 100% against 40 that had not been trained by 42.5%.  
Co-Authors Arini Fenty Eka Muzayyana Agustin Ottoh Hidayatullah Valeria Cynthia Dewi