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All Journal Jurnal ULTIMATICS JISA (Jurnal Informatika dan Sains) SKANIKA: Sistem Komputer dan Teknik Informatika
Rarasati, Dionisia Bhisetya
Universitas Bunda Mulia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Recommendation for Classification of News Categories Using Support Vector Machine Algorithm with SVD Nofenky .; Dionisia Bhisetya Rarasati
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.1854

Abstract

Online news is a digital information media currently has a very easy and flexible updating process. The News Document grouping process is implemented in several stages, including Text Mining which includes Text Pre-processing which includes Tokenizing, Stopword removal, Stemming, Word Merging, TF-IDF and Confusion Matrix. Of the several techniques in Text Mining, the most frequently used for News Document classification is the Support Vector Machine (SVM). SVM has the advantage of being able to identify separate hyperplane that maximizes the margin between two or more different classes. The selection of features in SVM significantly affects the classification accuracy results. Therefore, in this study a combination of feature selection methods is used, namely Singular Value Decomposition in order to increase accuracy and reduce the Classifier Time Support Vector Machine. This research resulted in text classification in the form of categories Entertainment, Health, Politics and Technology. Based on the Support Vector Machines Algorithm, an accuracy rate of 81% was obtained with 360 Data Training and 120 Data Testing, after adding the Singular Value Decomposition feature with a K- Rank value of 50%, a significant increase in accuracy was obtained with an accuracy value of 94% and The time of Algorithm process is faster.
A Grouping of Song-Lyric Themes Using K-Means Clustering Dionisia Bhisetya Rarasati
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.658

Abstract

One of the automatic way of theme grouping that can be used is K-Means Clustering. In this research, the song theme is taken from the text of song lyrics. The aim of this study is developing a system that can automatically group the song lyric theme and know the accuracy level of the grouping. The process stage is started with the data processing or text processing called as text mining. In text mining, there are some processes. First, the text operation. The text operation consists of tokenizing, stopword, steeming, and word weighting then can be processed using K-Means clustering. In clustering process, it consists of initial centroid initialization uses Variance Initialization, next counts the centroid distance on the data using Euclidean distance until get the proper grouping accurately. The accuracy counting uses confusion matrix. The next step to see the suitability system that has been made, new data is added which then is processed by a system. After that, it can decide the new data is classified into one specific theme. From the research that has been conducted as case study in Masdha Radio Yogyakarta, total data available 400 and divided into four clusters. The clusters consist of love cluster, friendship cluster, religion cluster, and fighting cluster. The result of research song lyric grouping based on the theme works well with 93.25% accuracy for the unique word frequency numbers 121 maximum and unique word 0 minimum.Keywords – K-Means clustering, Text Operation, Variance Initialization, Confusion Matrix.
IMPLEMENTASI METODE FUZZY TSUKAMOTO DALAM MENENTUKAN SUPPLY BBM PADA PERTASHOP Calvin Christopher Citra; Teady Matius Surya Mulyana; Halim Agung; Dionisia Bhisetya Rarasati; Evasaria Magdalena Sipayung
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 5 No 2 (2022): Jurnal SKANIKA Juli 2022
Publisher : Fakultas Teknologi Informasi, Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1419.156 KB) | DOI: 10.36080/skanika.v5i2.2946

Abstract

Kebutuhan Bahan Bakar Mesin dari masyarakat terus meningkat, hal ini juga terjadi bagi masyarakat pinggir kota.Untuk memenuhi kebutuhan Bahan Bakar Mesin yang ada dipinggir kota, maka Pertamina memberikan sebuah program bagi masyarakat untuk mendirikan SPBU mini dengan modal yang kecil, program ini dinamakan dengan Pertashop. Pertashop akan melakukan Pasokan satu bulan satu kali ke pihak pertamina. Namun dalam proses seupply proses perhitungan masih sering kurang tepat sehingga membuat pertashop mengalami kekurangan stok. Untuk mengatasi hal tersebut yang dapat dilakukan adalah dengan menentukkan Pasokan yang dibutuhkan pada sebuah SPBU atau Pertashop pada periode selanjutnya. Logika Fuzzy yang dipilih sebagai metode untuk menentukan Pasokan pada Pertashop agar mengurangi terjadinya kehabisan stok. Metode yang dipilih dalam menentukan Pasokan pada pertashop adalah Logika Fuzzy. Logika fuzzy memiliki Sistem Interferensi Fuzzy yang memberikan sebuah aturan dalam logika Fuzzy. Sistem Interferensi Fuzzy terdapat 3 metode yaitu, Tsukamoto, Mamdani, dan Sugeno. Pada penelitian ini menggunakan metode Fuzzy Tsukamoto Hasil penelitian didapatkan bahwa dengan tingkat akurasi metode fuzzy sebesar 87% menggunakan metode MAPE, dapat dinyatakan bahwa metode fuzzy Tsukamoto berhasil dalam menghitung Pasokan yang harus dilakukan pihak pertashop setiap bulannya agar tidak terjadi kekurangan stok.
Co-Authors Calvin Christopher Citra Evasaria Magdalena Sipayung Halim Agung Nofenky . Teady Matius Surya Mulyana, Teady Matius