Faried Zamachsari
STMIK Nusa Mandiri Jakarta

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Implementasi Finite State Automata Dalam Siklus Pembelajaran Magister Ilmu Komputer STMIK Nusa Mandiri Angelina Puput Giovani; Faried Zamachsari; Efid Dwi Agustono; Muhammad Ilham Prasetya; Windu Gata
CESS (Journal of Computer Engineering, System and Science) Vol 5, No 2 (2020): JULI 2020
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.462 KB) | DOI: 10.24114/cess.v5i2.16696

Abstract

Menyelesaikan Pendidikan Magister Ilmu Komputer pada STMIK Nusa Mandiri dalam waktu 4 semester merupakan harapan setiap mahasiswa. Untuk dapat lulus tepat waktu setiap mahasiswa wajib memenuhi semua persyaratan yang telah ditentukan oleh pihak kampus. Dalam tiap semester terdapat berbagai kegiatan diluar kegiatan belajar mengajar yang wajib diikuti oleh mahasiswa. Kegiatan tersebut meliputi Seminar, Workshop, dan Tes TOEFL. Hal-hal tersebut seringkali tidak diketahui mahasiswa sehingga tidak lulus mata kuliah tertentu. Apabila seorang mahasiswa dinyatakan tidak lulus mata kuliah tertentu maka diwajibkan untuk mengulang di semester berikutnya. Mengulang mata kuliah akan menambah pengeluaran dan tentunya menambah waktu belajar sehingga tidak dapat lulus tepat waktu sesuai yang diharapkan. Pada paper ini akan membahas tentang bagaimana Finite State Automata (FSA) jenis Nondeterministic Finite Automata (NFA) dapat diimplementasikan dalam siklus pembelajaran Magister Ilmu Komputer pada STMIK Nusa Mandiri. Dengan diterapkan metode iniĀ  diharapkan dapat membantu mahasiswa dalam pemenuhan persyaratan untuk mencapai kelulusan
Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection Algoritma Naive Bayes dan Support Vector Machine Faried Zamachsari; Gabriel Vangeran Saragih; Susafa'ati; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.834 KB) | DOI: 10.29207/resti.v4i3.1942

Abstract

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.