Articles
<![CDATA[Analisis Kualitas Data dan Klasifikasi Data Pasien Kanker ]]>
<![CDATA[Hidayatullah, Ahmad Fathan]]>;
<![CDATA[Prasetyo, Alan Dwi]]>;
<![CDATA[Sari, Dantik Puspita]]>;
<![CDATA[Pratiwi, Intan]]>
<![CDATA[ Seminar Nasional Informatika Medis (SNIMed) 2014: Prosiding SNIMED 2014 ]]>
Publisher : <![CDATA[Magister Teknik Informatika, Universitas Islam Indonesia ]]>
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<![CDATA[Masalah yang ditemui dalam dataset yang besar adalah adanya duplikasi data dan missing value. Duplikasi terjadi karena ada perbedaan identifikasi antara entitas yang sama dalam dunia nyata misalnya duplikasi data pasien rumah sakit. Solusi dari permasalahan duplikasi adalah dengan melakukan deduplikasi. Deduplikasi dilakukan dengan mengeliminasi data yang memiliki kemiripan. Pendeteksian duplikasi data dilakukan dengan Algoritma Levenshtein distance. Missing value terjadi jika ada nilai dari suatu atribut yang tidak ditemukan. Atribut yang mengandung missing value diganti dengan nilai rata-rata seluruh data dalam setiap atribut. Setelah duplikasi data dan missing value dapat diatasi, kemudian dilakukan klasifikasi untuk mengidentifikasi adanya kesamaan data. Klasifikasi dilakukan dengan tools WEKA menggunakan algoritma Decision Tree dan Naive Bayes. Metode Decision tree menghasilkan akurasi sebesar 99.9988 % sedangkan metode Naive Bayes menghasilkan akurasi 99.9799 %. Akurasi yang diperoleh algoritma Decision Tree memiliki hasil sedikit lebih baik daripada Naive Bayes. Namun demikian, secara umum metode Decision Tree dan Naive Bayes sama-sama memiliki akurasi yang baik dalam melakukan klasifikasi kemiripan data pasien.]]>
<![CDATA[Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM ]]>
<![CDATA[Chairullah Naury]]>;
<![CDATA[Naury, Chairullah]]>;
<![CDATA[Fudholi, Dhomas Hatta]]>;
<![CDATA[Hidayatullah, Ahmad Fathan]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i1.2556
<![CDATA[The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.]]>
<![CDATA[Attention-based CNN-BiLSTM for Dialect Identification on Javanese Text ]]>
<![CDATA[Hidayatullah, Ahmad Fathan]]>;
<![CDATA[Cahyaningtyas, Siwi]]>;
<![CDATA[Pamungkas, Rheza Daffa]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v5i4.1121
<![CDATA[This study proposes a hybrid deep learning models called attention-based CNN-BiLSTM (ACBiL) for dialect identification on Javanese text. Our ACBiL model comprises of input layer, convolution layer, max pooling layer, batch normalization layer, bidirectional LSTM layer, attention layer, fully connected layer and softmax layer. In the attention layer, we applied a hierarchical attention networks using word and sentence level attention to observe the level of importance from the content. As comparison, we also experimented with other several classical machine learning and deep learning approaches. Among the classical machine learning, the Linear Regression with unigram achieved the best performance with average accuracy of 0.9647. In addition, our observation with the deep learning models outperformed the traditional machine learning models significantly. Our experiments showed that the ACBiL architecture achieved the best performance among the other deep learning methods with the accuracy of 0.9944.]]>
<![CDATA[Analysis of Stemming Influence on Indonesian Tweet Classification ]]>
<![CDATA[Ahmad Fathan Hidayatullah]]>;
<![CDATA[Chanifah Indah Ratnasari]]>;
<![CDATA[Satrio Wisnugroho]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v14i2.3113
<![CDATA[Stemming has been commonly used by some researchers in natural language processing area such as text mining, text classification, and information retrieval. In information retrieval, stemming may help to raise retrieval performance. However, there is an indication that stemming does not hand over significant influence toward the accuracy in text classification. Therefore, this paper analyzes further research about the influence of stemming on tweet classification in Bahasa Indonesia. This work examines about the accuracy result between two conditions by involving stemming and without involving stemming in pre-processing task for tweet classification. The contribution of this research is to find out a better pre-processing task in order to obtain good accuracy in text classification. According to the experiments, it is observed that all accuracy results in tweet classification tend to decrease. Stemming task does not raise the accuracy either using SVM or Naive Bayes algorithm. Therefore, this work summarized that stemming process does not affect significantly towards the accuracy performance.]]>
<![CDATA[Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot ]]>
<![CDATA[Yurio Windiatmoko]]>;
<![CDATA[Ahmad Fathan Hidayatullah]]>;
<![CDATA[Dhomas Hatta Fudholi]]>;
<![CDATA[Ridho Rahmadi]]>
<![CDATA[ International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022 ]]>
Publisher : <![CDATA[STMIK Dharma Wacana ]]>
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DOI: 10.29099/ijair.v6i1.247
<![CDATA[Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.]]>
<![CDATA[TWITTER SEBAGAI MEDIA DAKWAH ]]>
<![CDATA[Ahmad Fathan Hidayatullah]]>
<![CDATA[ Teknoin Vol. 22 No. 1 (2016) ]]>
Publisher : <![CDATA[Faculty of Industrial Technology Universitas Islam Indonesia ]]>
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DOI: 10.20885/teknoin.vol22.iss1.art5
<![CDATA[Social media utilization by moslem community has increased from year to year. In the last decade, Facebook and Twitter are used as means of communication to forward the religious messages and Islamic preaching (dakwah). Among existing social media, twitter is considered having quite high popularity in society. Trend of conducting dakwah by twitter with kultwit apparently has assisted the Muslim preachers to deliver Islamic messages in more interesting way. Furthermore, they gain positive respond from society. Kultwit has become one of the alternative sources to obtain information about Islam and to understand Islam deeper.]]>
<![CDATA[Penerapan Text Mining dalam Klasifikasi Judul Skripsi ]]>
<![CDATA[Ahmad Fathan Hidayatullah]]>;
<![CDATA[Muhammad Rifqi Ma'arif]]>
<![CDATA[ Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2016 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia ]]>
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<![CDATA[Salah satu masalah yang berkaitan dengan text classification yang ditemukan di perguruan tinggi yaitu proses pengelompokkan judul skripsi secara otomatis. Penelitian ini bertujuan untuk membuat model data judul skripsi di bidang informatika menggunakan Support Vector Machine (SVM) dan Naïve Bayes. Berdasarkan hasil eksperimen, model SVM memiliki akurasi yang lebih rendah dengan perbedaan yang cukup signifikan jika dibandingkan dengan model yang dihasilkan dari algoritma Naive Bayes.Pada perhitungan precision, recall, dan f-score diketahui bahwa hasil perhitungan ketiganya memiliki pola yang sama dengan perhitungan akurasi. Secara keseluruhan, hasil perolehan f-score dengan algoritma Naive Bayesmemberikan hasil yang lebih tinggi dibandingkan dengan algoritma SVM.]]>
<![CDATA[KOMPARASI ALGORITMA MACHINE LEARNING DAN DEEP LEARNING UNTUK NAMED ENTITY RECOGNITION : STUDI KASUS DATA KEBENCANAAN ]]>
<![CDATA[Nuli Giarsyani]]>;
<![CDATA[Ahmad Fathan Hidayatullah]]>;
<![CDATA[Ridho Rahmadi]]>
<![CDATA[ Jurnal Informatika dan Rekayasa Elektronik Vol. 3 No. 1 (2020): JIRE April 2020 ]]>
Publisher : <![CDATA[LPPM STMIK Lombok ]]>
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DOI: 10.36595/jire.v3i1.222
<![CDATA[Penelitian ini bertujuan untuk melakukan Named Entity Recognition guna mengidentifikasi dan mengklasifikasi kata pada tweet yang memuat informasi bencana ke dalam entitas-entitas yang telah ditentukan. Entitas yang diidentifikasi yaitu jenis bencana, lokasi, waktu, magnitude dan others. Adapun algoritma klasifikasi yang digunakan adalah Machine Learning dan Deep Learning. Algoritma Deep Learning yang digunakan yaitu Long Short-Term Memory, Gated Recurrent Units, dan Convolutional Neural Network. Sedangkan algoritma Machine Learning yang digunakan yaitu Naïve Bayes, Decision Tree, Support Vector Machine dan Random Forest. Berdasarkan hasil eksperimen, Deep Learning memperoleh akurasi yang lebih unggul dari Machine Learning. Hal tersebut dilihat dari perolehan nilai accuracy terbaik Deep Learning dihasilkan dari algoritma Gated Recurrent Units dan Long Short-Term Memory dengan nilai 0.999. Sedangkan perolehan accuracy terbaik Machine Learning dihasilkan dari algoritma Random Forest sebesar 0.98.]]>
<![CDATA[The Influence of Stemming on Indonesian Tweet Sentiment Analysis ]]>
<![CDATA[Ahmad Fathan Hidayatullah]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v2.791
<![CDATA[Stemming has commonly used in some researchabout text mining, information retrieval, and natural languageprocessing. However, there is an indication that stemming does notdeliver significant influence toward accuracy in text classification.Hence, this research attempts to investigate the influence of thestemming process on Indonesian tweet sentiment analysis.Furthermore, this work examines about the difference effectbetween two conditions by involving stemming and withoutinvolving stemming on pre-preprocessing task. The experimentsshow that the accuracy difference for SVM using stemming in preprocessingacquired 0.67% and 1.34% higher than pre-processingwithout stemming, whereas, Naive Bayes obtained 0.23% and1.12%. Finally, this research proves that stemming does not raisethe accuracy either using SVM or Naive Bayes algorithm]]>
<![CDATA[Identifikasi Dual Sentimen Terhadap Ulasan Objek Wisata di Daerah Istimewa Yogyakarta ]]>
<![CDATA[Dimas Bintang Prasetyo]]>;
<![CDATA[Ahmad Fathan Hidayatullah]]>
<![CDATA[ AUTOMATA Vol. 1 No. 1 (2020) ]]>
Publisher : <![CDATA[AUTOMATA ]]>
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<![CDATA[Saat ini review atau ulasan jadi sangat penting karena bisa jadi sumber informasi dan penilain terhadap suatu objek. Tidak jarang sebuah ulasan dapat merubah pandangan dan keputusan seseorang terhadap objek tersebut. Di internet kita dapat dengan mudah menemukan ulasan-ulasan terkait banyak hal termasuk objek wisata. Namun dengan banyaknya ulasan yang ada di internet tidak semuanya dapat dipahami dengan mudah. Masih banyak ulasan-ulasan yang memiliki ambigiutas, sehingga sulit untuk menentukan inti sarinya. Salah satu cara dalam menangani masalah ini ialah dengan menggunakan natural language processing (NLP). Jenis NLP yang tepat dalam hal ini ialah sentiment analysis.]]>
