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All Journal ComEngApp : Computer Engineering and Applications Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) ICON-CSE Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Dinamisia: Jurnal Pengabdian Kepada Masyarakat International Journal of New Media Technology Jurnal Linguistik Komputasional jurnal teknik informatika dan sistem informasi Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Abdiansah, Abdiansah
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences Other

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Named-Entity Recognition pada Teks Bebahasa Indonesia menggunakan Hidden Markov Model dan POS-Tagging Novi Yusliani; Ridho Putra Sufa; Ari Firdaus; Abdiansah Abdiansah; Yoppy Sazaki
Jurnal Linguistik Komputasional Vol 4 No 1 (2021): Vol. 4, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v4i1.39

Abstract

Named entity recognition adalah salah satu tool yang berfungsi untuk mengenali entitas bernama suatu kata dan banyak digunakan dalam aplikasi di bidang pemrosesan bahasa alami. Hidden markov model (HMM) adalah salah satu metode yang dapat digunakan untuk mengenali entitas bernama suatu kata. Metode ini terdiri dari tahap pelatihan dan tahap pengujian. Pada tahap pelatihan metode ini membutuhkan sekumpulan data berlabel untuk mendapatkan model pengetahuan berupa nilai probabilitas setiap kata yang ada di dalam data latih. Nilai probabilitas ini berfungsi untuk mengenali kata-kata yang belum diketahui labelnya. Apabila kata yang akan dikenali tidak ada di dalam data latih, maka kata tersebut akan memiliki nilai probabilitas nol (zero probability). Nilai probabilitas nol pada suatu kata menyebabkan kata tersebut tidak bisa diketahui label entitas bernamanya. Karena itu, penelitian ini menggunakan part-of-speech tagging agar tidak ada kata yang memiliki nilai probabilitas nol. Pengujian dilakukan pada teks berbahasa Indonesia dengan jumlah kalimat sebanyak 511 kalimat. Hasil pengujian menunjukkan nilai rata-rata recall sebesar 83.82%, nilai rata-rata precision sebesar 89.31%, dan nilai rata-rata f-measure sebesar 86.14%.
Question Classification Menggunakan Support Vector Machines dan Stemming Abdiansah Abdiansah Abdiansah; Edi Winarko
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstract—Question Classification (QC) merupakan salah satukomponen penting dalam Question Answering System (QAS)karena akan berpengaruh langsung terhadap kinerjakeseluruhan QAS. Sejauh ini metode yang disarankan olehkomunitas QAS untuk QC adalah menggunakan SupportVector Machines (SVM). Untuk melakukan klasifikasi teksdibutuhkan fitur berdimensi tinggi, banyaknya fitur dapatmengurangi performa SVM. Stemming adalah teknik yangdigunakan untuk mereduksi term suatu dokumen.Penggunaan stemming akan berpengaruh terhadap sintaksisdan semantik suatu pertanyaan. Penelitian ini bertujuan untukmengetahui pengaruh stemming terhadap akurasi SVM. Telahdilakukan dua percobaan klasifikasi pertanyaan, yaitu denganmenggunakan SVM dan SVM+stemming. Hasil rata-rataakurasi dari percobaan diperoleh sebesar 86.75% untuk SVMdan 87.48% SVM+stemming sehingga telah terjadi kenaikanakurasi sebesar 0.73%. Walaupun peningkatan akurasi tidaksignifikan tetapi stemming dapat mereduksi fitur tanpamenurunkan akurasi SVM.Keywords—question classification, question answering system,support vector machines, stemming
Survei: Question Classification untuk Question Answering System Abdiansah Abdiansah; Anny K. Sari
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstract—Question Classification (QC) merupakan salahsatu dari tiga komponen utama yang ada dalam QuestionAnswering System (QAS). QC berfungsi untuk mereduksi ruangpencarian sehingga dapat meningkatkan kecepatan dan akurasiQAS. Secara umum kajian tentang QC dapat dibagi menjadi duabidang yaitu memperdalam analisis fitur yang meliputi analisisleksikal, sintaksis dan semantik serta improvisasi algoritmaklasifikasi. Artikel ini berisi laporan survei tentang algoritmaklasifikasi untuk QC berdasarkan tiga pendekatan yang seringdigunakan yaitu: Knowledge-Based (KB), Machine Learning (ML)dan Hybrid antara KB dan ML, serta dengan metode FeatureSelection (FS). Tujuan dari survei adalah untuk melihatperkembangan QC melalui pendekatan metode yang digunakanserta melihat peluang penelitian yang dapat dilakukan untukmeningkatkan performa QC. Dari hasil survei dapat diperolehgambaran secara umum bahwa kajian di bidang QC masih bisadieksplorasi lebih dalam lagi terutama pada wilayah open-domaindengan ukuran data yang besar serta domain bahasa yangdigunakan.Keywords—Question Classification; Question AnsweringSystem; Knowledge-Based; Machine Learning; Hybrid; FeatureSelection.
Analisis Ekstraksi Pengetahuan Eksternal untuk Question Answering System Abdiansah Abdiansah; Sri Hartati
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstract—Question Answering System (QAS) merupakan sistemtanya jawab yang dapat memberikan jawaban secara langsungkepada pengguna dalam bentuk bahasa alami. Untuk mencarisebuah jawaban, QAS menggunakan pengetahuan baik internalmaupun eksternal. Salah satu pengetahuan eksternal adalahInternet yang memiliki sumber informasi yang berlimpah.Penelitian ini mencoba untuk melakukan analisis penggunaanpengetahuan eksternal untuk digunakan oleh QAS. Ada tigasumber corpus yang digunakan yaitu: Wikipedia, Google danBing. Hasil dari penelitian ini adalah banyaknya data yangberhasil diperoleh dan jumlah jawaban yang dapat diekstraksi.Bing memperoleh hasil retrieval dan ekstraksi jawaban lebihbanyak dari Google yaitu sebesar 372 dokumen dan 72kemungkinan jawaban, sedangkan Google sebesar 345 dokumendengan 68 kemungkinan jawaban. Sedangkan Wikipediamemberikan sedikit dokumen karena corpus yang digunakanberjumlah 13 file html berbeda dengan Google dan Bing yangberjumlah 130 file html. Walaupun dokumen dan ekstraksijawaban Bing lebih besar dari Google tetapi Bing gagalmengekstraksi jawaban untuk dua corpus, sedangkan Googlehanya gagal untuk satu corpus.Keywords—question answering system; corpus; wikipedia; Google;Bing; retrieval; ekstraksi jawaban
Query Reformulation for Indonesian Question Answering System Using Word Embedding of Word2Vec Alvi Syahrini Utami; Novi Yusliani; Mastura Diana Marieska; Abdiansah Abdiansah
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.716 KB) | DOI: 10.18495/comengapp.v11i1.394

Abstract

Query reformulation is one of the tasks in Information Retrieval (IR), which automatically creates new queries based on previous queries. The main challenge of query reformulation is to create a new query whose meaning or context is similar to the old query. Query reformulation can improve the search for relevant documents for Open-domain Question Answering (OpenQA). The more queries are given to the search system, and the more documents will be generated. We propose a Word Predicted and Substituted (WPS) method for query reformulation using a word embedding word2vec. We tested this method on the Indonesian Question Answering System (IQAS). The test results obtained an E-1 value of 81% and an E-2 value of 274%. These results prove that the query reformulation method with WPS and word-embedding can improve the search for potential IQAS answers.
Fuzzy Logic Implementation on Enemy Speed Control to Raise Player Engagement Abdiansah abdiansah; Anggina Primanita; Frendredi Muliawan
ICON-CSE Vol 1, No 1 (2014)
Publisher : ICON-CSE

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Abstract

Shoot em’ up game is the sub-genre of action game. Action game is attractive because the game play usually use the interesting user interface and easily affect human emotion. With the aim to eliminate all the enemy, this kind of game will be boredom the player if the enemy behavior are monotones. This game needs a controller to add dynamic system into the enemy such as the artificial intelligence. Therefore, this paper proposes Fuzzy Takagi Sugeno method that will take several input and give the response as the output. So, the game will manipulate the enemy behavior that make the game more challenging and interesting to be played.
Application Of Dynamic Segmentation In Stroke Detection Software With ANN Hastie Audytra; Julian Supardi; Abdiansah Abdiansah
IJNMT (International Journal of New Media Technology) Vol 8 No 2 (2021): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v8i2.2439

Abstract

One way to find out whether there is a stroke is to do a CT scan . But the results of the examination with a new CT scan can be obtained in quite a long time. In addition, sometimes there are differences of opinion between doctors and radiologists regarding what is seen from the results of the examination. This research was conducted to produce a software that can later be integrated with the existing system on the CT Scan tool so that it can immediately be known whether or not stroke is present from the CT Scan results. In this study, a dynamic image segmentation method is implemented, namely the watershed transformation method which will later produce regions as a feature for the stroke detection process carried out with the backpropagation algorithm. From experiments conducted on CT scan images of the brain, this method can detect stroke well. The results obtained are 100% for training data and 90% for test data.
Analisis Sentimen E-Wallet di Twitter Menggunakan Support Vector Machine dan Recursive Feature Elimination Elza Fitriana Saraswita; Dian Palupi Rini; Abdiansah Abdiansah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3118

Abstract

Grouping of positive or negative sentiments in text reviews is increasingly being done automatically for identification. The selection of features in the classification is a problem that is often not solved. Most of the feature selection related to sentiment classification techniques is insurmountable in terms of evaluating significant features that reduce classification performance. Good feature selection technique can improve sentiment classification performance in machine learning approach. First, two sets of customer review data are labeled with sentiment and then retrieved, processed for evaluation. Next, the supports vector machine (svm-rfe) method is created and tested on the dataset. Svm-rfe will be run to measure the importance of the feature by rating the feature iteratively. For sentiment classification, only the top features of the ranking feature sequence will be used. Finally, performance is measured using accuracy, precision, recall, and f1-score. The experimental results show promising performance with an accuracy rate of 81%. This level of reduction is significant in making optimal use of computing resources while maintaining the efficiency of classification performance
CLASSIFICATION METHODS ON SENTIMENT ANALYSIS OF TOURISTS ON AIRLINES IN TWITTER Elza Fitriana Saraswita; Dian Palupi Rini; abdiansah abdiansah
Sriwijaya Journal of Informatics and Applications Vol 2, No 1 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Sentiment analysis is one of the knowledge to find the opinions of society towards a topic of discussion particular. Text mining is the science that many performed by individuals or companies to improve performance and fix complaints public against the services or brand trademarks that exist in the world of business. One of them is business flight or airline flights. One of them is public complaints against certain airlines posted on twitter. It is certainly going to greatly affect the airline 's own because , media social is one of the means of advertising and trade are extensive. Machine learning methods such as Logistics Regression, Kneighbors Classifier, Support Vector Classifier (SVC), Decision Tree Classifier, Random Forest Classifier, and Gaussian. Several classification methods are used to compare the performance of each method to see the best results.
The Effect of Brill Tagger on The Classification Results of Sentiment Analysis Using Multinomial Naïve Bayes Algorithm Astero Nandito; Abdiansah Abdiansah; Novi Yusliani
Sriwijaya Journal of Informatics and Applications Vol 2, No 1 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Twitter is a good indicator for influence in research, the problem thatarises in research in the field of sentiment analysis is the large numberof factors such as the use of informal or colloquial language and otherfactors that can affect the results of sentiment classification. Toimprove the results of sentiment classification, an informationextraction process can be carried out. One part of the informationextraction feature is a part of speech tagging, which is the giving ofword classes automatically. The results of part of speech tagging areused for weighting words based on part of speech. This studyexamines the effect of Part of Speech Tagging with the method BrillTagger in sentiment analysis using the Naive Bayes Multinomialalgorithm. Testing were carried out on 500 twitter tweet texts andobtained the results of the sentiment classification with implementingpart of speech tagging precision by 73,2%, recall by 63,2%, f-measureby 67,6%, accuracy by 60,7% and without implementing part ofspeech tagging precision by 65,2%, recall by 60,6%, f-measure by62,4% accuracy by 53,3%. From the results of the accuracy obtained,it shows that the application of part of speech tagging in sentimentanalysis using the Multinomial Naïve Bayes algorithm has an effectwith an increase in classification performance.
Co-Authors Adi Kurniawan Ahmad Fali Oklilas Ahmad Gustano Alvi Syahrini Utami Anny K. Sari Ari Firdaus Ari Wedhasmara Arya Mulya Kusuma Astero Nandito Danny Matthew Saputra Dian Palupi Rini Dian Palupi Rini Edi Winarko Elza Fitriana Saraswita Elza Fitriana Saraswita Erwin, Erwin Fathoni - Frendredi Muliawan Gerald Plakasa Harisatul Aulia Hastie Audytra Julian Supardi Kanda Januar Miraswan Kanda Januar Miraswan Mastura Diana Marieska Megah Mulya Muhammad Alfaris Oktavian Muhammad Qurhanul Rizqie Muhammad Rizky Akbar Novi Yusliani Novi Yusliani Primanita, Anggina Putri Patricia Ridho Putra Sufa Sri Hartati