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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Jurnal ULTIMATICS Jurnal Teknik Informatika (JUTIF)
Diana Purwitasari
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING Khadijah Fahmi Hayati Holle; Agus Zainal Arifin; Diana Purwitasari
Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.084 KB) | DOI: 10.21609/jiki.v8i1.283

Abstract

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.
COVERAGE, DIVERSITY, AND COHERENCE OPTIMIZATION FOR MULTI-DOCUMENT SUMMARIZATION Khoirul Umam; Fidi Wincoko Putro; Gulpi Qorik Oktagalu Pratamasunu; Agus Zainal Arifin; Diana Purwitasari
Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.144 KB) | DOI: 10.21609/jiki.v8i1.278

Abstract

A great summarization on multi-document with similar topics can help users to get useful information. A good summary must have an extensive coverage, minimum redundancy (high diversity), and smooth connection among sentences (high coherence). Therefore, multi-document summarization that considers the coverage, diversity, and coherence of summary is needed. In this paper we propose a novel method on multi-document summarization that optimizes the coverage, diversity, and coherence among the summary's sentences simultaneously. It integrates self-adaptive differential evolution (SaDE) algorithm to solve the optimization problem. Sentences ordering algorithm based on topical closeness approach is performed in SaDE iterations to improve coherences among the summary's sentences. Experiments have been performed on Text Analysis Conference (TAC) 2008 data sets. The experimental results showed that the proposed method generates summaries with average coherence and ROUGE scores 29-41.2 times and 46.97-64.71% better than any other method that only consider coverage and diversity, re-spectively.
Optimasi Pembobotan pada Query Expansion dengan Term Relatedness to Query-Entropy based (TRQE) Resti Ludviani; Khadijah F. Hayati; Agus Zainal Arifin; Diana Purwitasari
Jurnal Buana Informatika Vol. 6 No. 3 (2015): Jurnal Buana Informatika Volume 6 Nomor 3 Juli 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v6i3.433

Abstract

Abstract. An appropriate selection term for expanding a query is very important in query expansion. Therefore, term selection optimization is added to improve query expansion performance on document retrieval system. This study proposes a new approach named Term Relatedness to Query-Entropy based (TRQE) to optimize weight in query expansion by considering semantic and statistic aspects from relevance evaluation of pseudo feedback to improve document retrieval performance. The proposed method has 3 main modules, they are relevace feedback, pseudo feedback, and document retrieval. TRQE is implemented in pseudo feedback module to optimize weighting term in query expansion. The evaluation result shows that TRQE can retrieve document with the highest result at precission of 100% and recall of 22,22%. TRQE for weighting optimization of query expansion is proven to improve retrieval document.     Keywords: TRQE, query expansion, term weighting, term relatedness to query, relevance feedback Abstrak..Pemilihan term yang tepat untuk memperluas queri merupakan hal yang penting pada query expansion. Oleh karena itu, perlu dilakukan optimasi penentuan term yang sesuai sehingga mampu meningkatkan performa query expansion pada system temu kembali dokumen. Penelitian ini mengajukan metode Term Relatedness to Query-Entropy based (TRQE), sebuah metode untuk mengoptimasi pembobotan pada query expansion dengan memperhatikan aspek semantic dan statistic dari penilaian relevansi suatu pseudo feedback sehingga mampu meningkatkan performa temukembali dokumen. Metode yang diusulkan memiliki 3 modul utama yaitu relevan feedback, pseudo feedback, dan document retrieval. TRQE diimplementasikan pada modul pseudo feedback untuk optimasi pembobotan term pada ekspansi query. Evaluasi hasil uji coba menunjukkan bahwa metode TRQE dapat melakukan temukembali dokumen dengan hasil terbaik pada precision  100% dan recall sebesar 22,22%.Metode TRQE untuk optimasi pembobotan pada query expansion terbukti memberikan pengaruh untuk meningkatkan relevansi pencarian dokumen.Kata Kunci: TRQE, ekspansi query, pembobotan term, term relatedness to query, relevance feedback
Deteksi Bot Spammer pada Twitter Berbasis Sentiment Analysis dan Time Interval Entropy Christian Sri Kusuma Aditya; Mamluatul Hani’ah; Alif Akbar Fitrawan; Agus Zainal Arifin; Diana Purwitasari
Jurnal Buana Informatika Vol. 7 No. 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i3.656

Abstract

Abstract. Spam is an abuse of messaging undesired by recipients. Those who send spam are called spammers.  Popularity of Twitter has attracted spammers to use it as a means to disseminate spam messages. The spams are characterized by a neutral emotional sentiment or no particular users’ preference perspective. In addition, the regularity of tweeting behavior periodically shows automation performed by bot. This study proposes a new method to differentiate between bot spammer and legitimate user accounts by integrating the sentiment analysis (SA) based on emotions and time interval entropy (TIE). The combination of knowledge-based and machine learning-based were used to classify tweets with positive, negative and neutral sentiments. Furthermore, the collection of timestamp is used to calculate the time interval entropy of each account. The results show that the precision and recall of the proposed method reach up to 83% and 91%. This proves that the merging SA and TIE can optimize overall system performance in detecting Bot Spammer.Keywords: bot spammer, twitter, sentiment analysis, polarity, entropy Abstrak. Spam merupakan penyalahgunaan pengiriman pesan tanpa dikehendaki oleh penerimanya, orang yang mengirimkan spam disebut spammer. Ketenaran Twitter mengundang spammer untuk menggunakannya sebagai sarana menyebarluaskan pesan spam. Karakteristik dari tweet yang dikategorikan spam memiliki sentimen emosi netral atau tidak ada preferensi tertentu terhadap suatu perspektif dari user yang memposting tweet. Selain itu keteraturan waktu perilaku saat memposting tweet secara periodik menunjukkan otomatisasi yang dilakukan bot. Pada penelitian ini diusulkan metode baru untuk mendeteksi antara bot spammer dan legitimate user dengan mengintegrasikan sentimen analysis berdasarkan emosi dan time interval entropy. Pendekatan gabungan knowledge-based dan machine learning-based digunakan untuk mengklasifikasi tweet yang memiliki sentimen positif, negatif dan tweet netral. Selanjutnya kumpulan timestamp digunakan untuk menghitung time interval entropy dari tiap akun. Hasil percobaan menunjukan bahwa precision dan recall dari metode yang diusulkan mencapai 83% dan 91%. Hal ini membuktikan penggabungan Sentiment Analysis (SA) dan Time Interval Entropy (TIE) dapat mengoptimalkan performa sistem secara keseluruhan dalam mendeteksi Bot Spammer.Kata Kunci:  bot spammer, twitter, sentiment analysis,  polarity, entropy
Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia Rizqa Raaiqa Bintana; Chastine Fatichah; Diana Purwitasari
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2494.968 KB) | DOI: 10.31937/ti.v10i1.842

Abstract

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval
ANALYSIS OF RAW MATERIAL INVENTORY PREDICTION FOR PLASTIC ORE USING A COMBINATION OF CAUSALITY AND TIME SERIES METHODS: A CASE STUDY IN A TEXTILE INDUSTRY COMPANY Frangky Rawung; Agus Budi Raharjo; Diana Purwitasari
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1809

Abstract

Raw material inventory is a valuable company asset in production activities. Inadequate or excessive availability can lead to production failures or cost wastage. This research aims to predict raw material inventory based on factors such as initial stock, receipts, usage, final stock, and differences in usage. A causality-based approach with Multiple Linear Regression (MLR) is used as the basis, complemented by a time series data approach that processes data trends using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The prediction results from both models are then combined using the harmonic mean. This research utilizes a dataset of raw material inventory and applies the Root Mean Squared Error (RMSE) and R-squared (R²) performance parameters for model evaluation. The research is expected to provide useful information for companies in managing their raw material inventory and improving the efficiency of their production processes. Results show that, in the BiLSTM deep learning model, Polyethylene Terephthalate (PET) raw materials yielded an RMSE of 6.53 and an R² of 0.93. These results indicate that PET raw materials have a higher predictive value than other materials.
Co-Authors Agus Budi Raharjo Agus Zainal Arifin Alif Akbar Fitrawan Chastine Fatichah Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Frangky Rawung Gulpi Qorik Oktagalu Pratamasunu Khadijah F. Hayati Khadijah Fahmi Hayati Holle Khoirul Umam Mamluatul Hani’ah Resti Ludviani Rizqa Raaiqa Bintana Wincoko Putro, Fidi