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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Linguistik Komputasional Jurnal Informatika dan Rekayasa Elektronik Journal of Computer System and Informatics (JoSYC) Automata
Ahmad Fathan Hidayatullah, Ahmad Fathan
Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Kualitas Data dan Klasifikasi Data Pasien Kanker Hidayatullah, Ahmad Fathan; Prasetyo, Alan Dwi; Sari, Dantik Puspita; Pratiwi, Intan
Seminar Nasional Informatika Medis (SNIMed) 2014: Prosiding SNIMED 2014
Publisher : Magister Teknik Informatika, Universitas Islam Indonesia

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Abstract

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.
Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM Chairullah Naury; Naury, Chairullah; Fudholi, Dhomas Hatta; Hidayatullah, Ahmad Fathan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2556

Abstract

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.
Attention-based CNN-BiLSTM for Dialect Identification on Javanese Text Hidayatullah, Ahmad Fathan; Cahyaningtyas, Siwi; Pamungkas, Rheza Daffa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i4.1121

Abstract

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.
Analysis of Stemming Influence on Indonesian Tweet Classification Ahmad Fathan Hidayatullah; Chanifah Indah Ratnasari; Satrio Wisnugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.3113

Abstract

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.
TWITTER SEBAGAI MEDIA DAKWAH Ahmad Fathan Hidayatullah
Teknoin Vol. 22 No. 1 (2016)
Publisher : Faculty of Industrial Technology Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/teknoin.vol22.iss1.art5

Abstract

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.
Penerapan Text Mining dalam Klasifikasi Judul Skripsi Ahmad Fathan Hidayatullah; Muhammad Rifqi Ma'arif
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2016
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

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.
KOMPARASI ALGORITMA MACHINE LEARNING DAN DEEP LEARNING UNTUK NAMED ENTITY RECOGNITION : STUDI KASUS DATA KEBENCANAAN Nuli Giarsyani; Ahmad Fathan Hidayatullah; Ridho Rahmadi
Jurnal Informatika dan Rekayasa Elektronik Vol. 3 No. 1 (2020): JIRE April 2020
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v3i1.222

Abstract

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.
The Influence of Stemming on Indonesian Tweet Sentiment Analysis Ahmad Fathan Hidayatullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (918.77 KB) | DOI: 10.11591/eecsi.v2.791

Abstract

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
Identifikasi Dual Sentimen Terhadap Ulasan Objek Wisata di Daerah Istimewa Yogyakarta Dimas Bintang Prasetyo; Ahmad Fathan Hidayatullah
AUTOMATA Vol. 1 No. 1 (2020)
Publisher : AUTOMATA

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Abstract

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.
Deteksi Cyberbullying pada Cuitan Media Sosial Twitter Nassharih Abdulloh; Ahmad Fathan Hidayatullah
AUTOMATA Vol. 1 No. 1 (2020)
Publisher : AUTOMATA

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Abstract

Tujuan dari penelitian ini adalah mengidentifikasi konten yang mengandung makna perundungan secara daring (Cyberbullying) pada media sosial. Dalam kasus ini, penulis memilih media sosial Twitter sebagai obyek penelitian. Setidaknya, ada 1971 baris data yang telah dikumpulkan. Data - data tersebut berisi dua jenis cuitan baik cuitan yang memiliki kecenderungan Cyberbullying dan yang tidak. Untuk mencapai tujuan penelitian, peneliti menggunakan lima langkah penelitian, yaitu pengumpulan data, preprocessing, ekstraksi fitur, klasifikasi, dan evaluasi. Empat algoritma Machine Learning diimplementasikan dalam penelitian ini, yaitu K-Nearest Neighbor (KNN), Multinomial Naïve Bayes, Logistic Regression, dan Support Vector Machine with linear kernel (SVM). Dapat disimpulkan bahwa keempat algoritma tersebut memiliki performa yang relatif sama. Akurasi dari masing masing algoritma dituliskan sebagai berikut Multinomial Naïve Bayes 0.9616, Logistic Regression 0.9949, Support Vector Machine with linear kernel 0.9975, and K-Nearest Neighbor (KNN) 0.9188.
Co-Authors Adityo Permana Wibowo Ahmad Raf'ie Pratama Ahmad Rafie Pratama Amiin Majiid Nugroho Anas Satria Lombu Annisa Zahra Azmiardhy Zulkifli Farmadiansyah Cahyaningtyas, Siwi Chairullah Naury Chanifah Indah Ratnasari Dantik Puspita Sari, Dantik Puspita Dhomas Hatta Fudholi Dimas Bintang Prasetyo Fayruz Rahma Febrianto Eko Saputro Fudholi, Dhomas Hatta Habib Faizal Fadli Kartika Rizqi Nastiti Muhammad Fakhri Despawida Aulia Putra Muhammad Rifqi Ma'arif Nassharih Abdulloh Nastiti, Kartika Rizqi Naury, Chairullah Nuli Giarsyani Pamungkas, Rheza Daffa Prasetyo, Alan Dwi PRATIWI, INTAN Purwoko, Agus Putra, Muhammad Fakhri Despawida Aulia Rheza Daffa Pamungkas Ridho Rahmadi Ridho Rahmadi Satrio Wisnugroho Septia Rani Siwi Cahyaningtyas Yurio Windiatmoko, Yurio