Tesa Eranti Putri
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TERM WEIGHTING BASED ON INDEX OF GENRE FOR WEB PAGE GENRE CLASSIFICATION Sugiyanto, Sugiyanto; Rozi, Nanang Fakhrur; Putri, Tesa Eranti; Arifin, Agus Zainal
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 12, No 1, Januari 2014
Publisher : Informatics, ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.086 KB) | DOI: 10.12962/j24068535.v12i1.a43

Abstract

Automating the identification of the genre of web pages becomes an important area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. To index the terms used in classification, generally the selected type of weighting is the document-based TF-IDF. However, this method does not consider genre, whereas web page documents have a type of categorization called genre. With the existence of genre, the term appearing often in a genre should be more significant in document indexing compared to the term appearing frequently in many genres despites its high TF-IDF value. We proposed a new weighting method for web page documents indexing called inverse genre frequency (IGF). This method is based on genre, a manual categorization done semantically from previous research. Experimental results show that the term weighting based on index of genre (TF-IGF) performed better compared to term weighting based on index of document (TF-IDF), with the highest value of accuracy, precision, recall, and F-measure in case of excluding the genre-specific keywords were 78%, 80.2%, 78%, and 77.4% respectively, and in case of including the genre-specific keywords were 78.9%, 78.7%, 78.9%, and 78.1% respectively.
Prediksi Ketepatan Waktu Lulus Mahasiswa dengan k-Nearest Neighbor dan Naïve Bayes Classifier Sabilla, Wilda Imama; Putri, Tesa Eranti
Jurnal Komputer Terapan Vol 3 No 2 (2017): Jurnal Komputer Terapan November 2017
Publisher : Politeknik Caltex Riau

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Abstract

Salah satu aspek pengukuran kualitas dalam evaluasi keberhasilan penyelenggaraan pendidikan tinggi adalah ketepatan lulus mahasiswa. Jumlah prosentase mahasiswa yang lulus tepat waktu menjadi indikator keberhasilan pelaksanaan proses belajar mengajar di suatu program studi. Penelitian ini menawarkan penggunaan metode penggalian data untuk memprediksi waktu lulus mahasiswa menggunakan dua metode yaitu k-Nearest Neighbour dan Naïve Bayes Classifier. Hasil dari penelitian ini berupa sistem yang dapat memprediksi ketepatan waktu lulus. Uji coba dilakukan dengan menggunakan data lulusan mahasiswa D3 Sistem Informasi Universitas Airlangga. Hasil uji coba menunjukkan bahwa metode k-Nearest Neighbor menghasilkan akurasi lebih tinggi dibandingkan dengan Naïve Bayes Classifier. Akurasi tertinggi diperoleh dengan menggunakan metode k-Nearest Neighbor yaitu sebesar 98.7%. Oleh karena itu dapat disimpulkan bahwa sistem yang dibangun pada penelitian ini mampu memprediksi ketepatan waktu lulus dengan akurasi cukup tinggi.
Pengelompokan Dokumen Berita Berbahasa Indonesia Menggunakan Reduksi FiturInformation Gain dan Singular Value Decomposition dalam Fuzzy C-MeansClustering Sari, Yuita Arum; Putri, Tesa Eranti; Hapsani, Anggi Gustiningsih
Jurnal Informatika dan Multimedia Vol 10 No 1 (2018): Jurnal Volume 10, No.1 (2018)
Publisher : Teknik Informatika Politeknik Kediri

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Abstract

Koran dan berita online merupakan media informasi digital saat ini yang proses pembaruan informasinya sangat mudah dan fleksibel. Kemudahan ini memungkinkan penulis berita untuk mengunggah informasi baru di waktu kapanpun dan dimanapun. Hal ini menyebabkan data dokumen berita sangat banyak dan tidak teratur sehingga perlu dilakukan pengelompokan berita sesuai dengan kontennya. Pengelompokanberita sesuai content dapat membantu pembaca untuk membaca berita dengan topiktertentu sesuai dengan minatnya. Proses pengelompokan informasi berita diimplementasikan denganbeberapa tahap, yaitu preprocessing dan pengelompokan dokumen. Preprocessing dilakukan dengan mengimplementasikan metode kombinasi reduksi fitur Document Frequency (DF) dan Information Gain (IG) Thresholding dalamSingular Value Decomposition (SVD). Algoritme SVD dipilih karena memiliki kemampuan untuk melakukan dekomposisi pada matriks dokumen-term, sehingga diperoleh matriks yang masih menyimpan informasi penting dengan ukuran dimensi yang lebih kecil.Pada tahap pengelompokan dokumen berita dilakukandengan algoritme Fuzzy C-Means. Hasil uji coba akurasipengelompokan dokumen berita menunjukkan bahwa pengelompokan yang dilakukan memberikan hasil pengkategorian yang cukup akurat dengan tingkat akurasi rata-rata 74,5 % (IG threshold 0.5, k = 5). Hal tersebut menunjukkan bahwa pengelompokan dokumen menggunakan IG dan SVD dengan FUZZY C-MEANS adalah sesuai dengan kebutuhan.
Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network Putri, Tesa Eranti; Firdaus, Aji Akbar; Sabilla, Wilda Imama
Journal of Information Systems Engineering and Business Intelligence Vol 4, No 2 (2018): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1369.973 KB) | DOI: 10.20473/jisebi.4.2.96-105

Abstract

Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends’ revenue. This suggests that in Indonesia, weekends’ electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.