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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Misykat: Jurnal Ilmu-ilmu Al-QurÆan, Hadist, Syariah dan Tarbiyah Systemic: Information System and Informatics Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) Kertha Widya : Jurnal Hukum
M. Ali Fauzi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Analisis Sentimen Pada Ulasan Aplikasi Mobile Menggunakan Naive Bayes dan Normalisasi Kata Berbasis Levenshtein Distance (Studi Kasus Aplikasi BCA Mobile) Ferly Gunawan; M. Ali Fauzi; Putra Pandu Adikara
Systemic: Information System and Informatics Journal Vol. 3 No. 2 (2017): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (956.948 KB) | DOI: 10.29080/systemic.v3i2.234

Abstract

Perkembangan aplikasi mobile yang pesat membuat banyak aplikasi diciptakan dengan berbagai kegunaan untuk memenuhi kebutuhan pengguna. Setiap aplikasi memungkinkan pengguna untuk memberi ulasan tentang aplikasi tersebut. Tujuan dari ulasan adalah untuk mengevaluasi dan meningkatkan kualitas produk ke depannya. Untuk mengetahui hal tersebut, analisis sentimen dapat digunakan untuk mengklasifikasikan ulasan ke dalam sentimen positif atau negatif. Pada ulasan aplikasi biasanya terdapat salah eja sehingga sulit dipahami. Kata yang mengalami salah eja perlu dilakukan normalisasi kata untuk diubah menjadi kata standar. Karena itu, normalisasi kata dibutuhkan untuk menyelesaikan masalah salah eja. Penelitian ini menggunakan normalisasi kata berbasis Levenshtein distance. Berdasarkan pengujian, nilai akurasi tertinggi terdapat pada perbandingan data latih 70% dan data uji 30%. Hasil akurasi tertinggi dari pengujian menggunakan nilai edit <=2 adalah 100%, nilai edit tertinggi kedua didapat pada nilai edit <=1 dengan akurasi 96,4%, sedangkan nilai edit dengan akurasi terendah diperoleh pada nilai edit <=4 dan <=5 dengan akurasi 66,6%. Hasil dari pengujian Naive Bayes-Levenshtein Distance memiliki nilai akurasi tertinggi yaitu 96,9% dibandingkan dengan pengujian Naive Bayes tanpa Levenshtein Distance dengan nilai akurasi 94,4%.
Neighbor Weighted K-Nearest Neighbor for Sambat Online Classification Annisya Aprilia Prasanti; M. Ali Fauzi; Muhammad Tanzil Furqon
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp155-160

Abstract

Sambat Online is one of the implementation of E-Government for complaints management provided by Malang City Government.  All of the complaints will be classified into its intended department. In this study, automatic complaint classification system using Neighbor Weighted K-Nearest Neighbor (NW-KNN) is poposed because Sambat Online has imbalanced data. The system developed consists of three main stages including preprocessing, N-Gram feature extraction, and classification using NW-KNN. Based on the experiment results, it can be concluded that the NW-KNN algorithm is able to classify the imbalanced data well with the most optimal k-neighbor value is 3 and unigram as the best features by 77.85% precision, 74.18% recall, and 75.25% f-measure value. Compared to the conventional KNN, NW-KNN algorithm also proved to be better for imbalanced data problems with very slightly differences.
Random Forest Approach for Sentiment Analysis in Indonesian Language M. Ali Fauzi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp46-50

Abstract

Sentiment analysis become very useful since the rise of social media and online review website and, thus, the requirement of analyzing their sentiment in an effective and efficient way. We can consider sentiment analysis as text classification problem with sentiment as its categories. In this study, we explore the use of Random Forest for sentiment classification in Indonesian language. We also explore the use of bag of words (BOW) features with some term weighting methods variation such as Binary TF, Raw TF, Logarithmic TF and TF.IDF. The experiment result showed that sentiment analysis system using random forest give good performance with average OOB score 0.829. The result also depicted that all of the four term weighting method has competitive result. Since the score difference is not very significant, we can say that the term weighting method variation in study has no remarkable effect for sentiment analysis using Random Forest.
Cyberbullying identification in twitter using support vector machine and information gain based feature selection Ni Made Gita Dwi Purnamasari; M. Ali Fauzi; Indriati Indriati; Liana Shinta Dewi
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1494-1500

Abstract

Cyberbullying is one of the actions that violate the ITE Law where the crime is committed on social media applications such as Twitter. This action is difficult to detect if no one is reporting the tweet. Cyberbullying tweet identification aims to classify tweets that contain bullying. Classification is done using Support Vector Machine method where this method aims to find the dividing hyperplane between negative and positive class. This study is a text classification where more data is used, the more features are produced, therefore this research also uses Information Gain as feature selection to select features that are not relevant to the classification. The process of the system starts from text preprocessing with tokenizing, filtering, stemming and term weighting. Then perform the information gain feature selection by calculating the entropy value of each term. After that perform the classification process based on the terms that have been selected, and the output of the system is identification whether the tweet is bullying or not. The result of using SVM method is accuracy 75%, precision 70.27%, recall 86.66% and f-measure 77.61% on experiment maximum iteration = 20, λ = 0.5, γ = 0.001, ε = 0.000001, and C = 1. The best threshold of information gain is 90%, with accuracy 76.66%, precision 72.22%, recall 86.66% and f-measure 78.78%.
Ensemble Method for Indonesian Twitter Hate Speech Detection M. Ali Fauzi; Anny Yuniarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp294-299

Abstract

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.
Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model M. Ali Fauzi; Agus Zainal Arifin; Sonny Christiano Gosaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 3: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i3.pp610-615

Abstract

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
Peringkasan Literatur Ilmu Komputer Bahasa Indonesia Berbasis Fitur Statistik dan Linguistik menggunakan Metode Gaussian Naive Bayes Muhammad Fhadli; Mochammad Ali Fauzi; Tri Afirianto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 4 (2017): April 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (953.144 KB)

Abstract

In this era which require big amount of data, text summarization becomes a needs. With text summarization, everyone can get information that describe all of big text in just few of sentences. The problem in text summarization is quality of the summarization result. One of the known method for text summarization is TFIDF, this method is a method for summarizing text using statistical approach. The other approach for summarizing text is statistical approach. In a general way, summarization result is consist of sentences with statistical features such as total of words, total of keywords, and sentence position in the original text. Those features can be used to classify a text into class of summary or class of non summary. The summarization result come from the composite of every sentence in summary class. In this research, writer combines the use of statistical feature and linguistical features to summarize text. The testing result of this research show that summarization with statistical and linguistical features using Naive Bayes method came with f-score average 0.206538 and realive utility average 0.116657.
Klasifikasi Tweets Pada Twitter Dengan Menggunakan Metode Fuzzy K-Nearest Neighbour (Fuzzy K-NN) dan Query Expansion Berbasis Apriori Joda Pahlawan Romadhona Tanjung; Mochammad Ali Fauzi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1372.995 KB)

Abstract

Twitter is a unique conversation tool that allows us to send and receive short messages called tweets in the Twitter community. Tweets are short messages that have a length of 140 characters. Tweets that appear on the homepage are all jumbled into one, posted variety ranging from the economy, sports, technology, automotive, healthcare and others. When users search for a news or information desired, the problem that arises is Twitter user difficult to find tweets. The classification process can be performed to categorize a tweets using an algorithm Fuzzy K-Nearest Neighbour. However, the process of classifying a tweets it is difficult to do because the tweets in the form of short-text. Therefore, before doing the classification process a tweets done preprocessing and word expansion beforehand with Query Expansion algorithms in order to provide maximum results in the classification. In the study conducted to produce the best accuracy by 82%. Best accuracy is obtained when using the Fuzzy KNN method with Query Expansion without preprocessing and threshold for the support value> = 0.15 and the value of confidence> = 1.
Implementasi Algoritma Genetika Untuk Penjadwalan Customer Service (Studi Kasus: Biro Perjalanan Kangoroo) Chusnah Puteri Damayanti; Rekyan Regasari Mardi Putri; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 6 (2017): Juni 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1310.161 KB)

Abstract

Due to the high demand of public transport, travel agency must be ready to serve the citizen. Travel need to be ready to serve the customer; called customer service. Customer service should provide information that is precise, accurate and fast to customers. At Travel Kangaroo which owns more than 300 fleet, has two locations namely central office and branches as well as long operating hours, a responsive customer service needed to serve the customers There are various rules that must be fulfilled in making the schedule of customer service too. Thus, in this study scheduling problems solved using genetic algorithms. Genetic algorithms can solve a complex problem as well as it has wide scope. Through the examination, it was obtained the best parameters that produce the most optimal fitness value with a population size of 110, 110 and comparison generation size crossover rate and mutation rate of 0.7: 0.3. By using these parameters, scheduling customer service have optimal results, although there are violations that occur with shorter computation time compared with the manual.
Klasifikasi Teks Bahasa Indonesia Pada Dokumen Pengaduan Sambat Online Menggunakan Metode K-Nearest Neighbors (K-NN) dan Chi-Square Claudio Fresta Suharno; Mochammad Ali Fauzi; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (961.194 KB)

Abstract

K-Nearest Neighbors (K-NN) is one classification method that easy to learn. Although, this method has some drawbacks, one of them is this classification could provide a low accuracy casued by a large feature space with irrelevant features among them. Because of that drawback, feature selection is applied to reduce the feature space by reducing number of irrelevant features in text classification. Selection feature method that being used in this experiment is using Chi-Square method. Using Chi-Square method to select important features by measuring dependency level of each feature across classes and documents. The process including in this experiment is collecting training and testing documents, text preprocessing and feature selection, and classification. After classification is being done by the system, we make an observation and analysis towards classification result, including precision, recall, and F-Measure value. From 16 evaluations, the best precision and recall score obtained with 90% precision and 78% recall on k = 15 using 25% feature selection used. While the best F-Measure score obtained with 78% F-Measure on k = 15 and k = 5 using 25% feature selection used. From this experiment, its appear that feature selection take effect in increasing F-Measure value in text classification of SAMBAT Online complaint documents in bahasa using K-Nearest Neighbors classification method.
Co-Authors Adi Sukarno Rachman Adinugroho, Sigit Aditya Kresna Bayu Arda Putra Agnes Rossi Trisna Lestari Agung Setiyoaji Agus Wahyu Widodo Agus Zainal Arifin Ahmad Galang Satria Ahmad Wildan Attabi&#039; Akbar, Aldi Fandiya Alvandi Fadhil Sabily Amalia Kusuma Akaresti Andika Indra Kusuma Andro Subagio Anita Sumiati Annam Rosyadi Annisya Aprilia Prasanti Annisya Aprilia Prasanti Anny Yuniarti ari kusyanti Bayu Rahayudi Billy Sabilal Budi Darma Setiawan Budi Kurniawan Chusnah Puteri Damayanti Claudio Fresta Suharno Claudio Fresta Suharno Dahnial Syauqy Desfianti, Ruri Dhimas Anjar Prabowo Dian Eka Ratnawati Dimas Joko Haryanto Dwi Damara Kartikasari dwi taufik hidayat Edy Santoso Eka Dewi Lukmana Sari Elisa Julie Irianti Siahaan Eti Setiawati Fachrul Rozy Saputra Rangkuti Fakhruddin Farid Irfani Fathor Rosi Ferly Gunawan Ferly Gunawan Figgy Rosaliana Fitra Abdurrachman Bachtiar Galih Nuring Bagaskoro Gosario, Sony Hadiyan Hadiyan Hasbi Razzak Hidayat, Hasannudin Hilmy Khairi Idris Hurriyatul Fitriyah I Wayan Sudira Imam Cholissodin Imam Cholissodin Indriati Indriati Irma Pujadayanti Irwin Deriyan Ferdiansyah Ismiarta Aknuranda Isnan . Joda Pahlawan Romadhona Tanjung Komang Candra Brata Lailil Muflikhah Laksono Trisnantoro Liana Shinta Dewi Liana Shinta Dewi Lita Handayani Tampubolon M Yusron Syauqi Dirgantara M. Rizzo Irfan M. Rizzo Irfan Mahdarani Dwi Laxmi Mahendra Data Malahayati, Salsabila Nur Maulana, Muhammad Afif Moch. Yugas Ardiansyah Moh Fadel Asikin Moh Iqbal Yusron Muhammad Fhadli Muhammad Hakiem Muhammad Khaerul Ardi Muhammad Khatib Barokah Muhammad Mishbahul Munir Muhammad Sholeh Hudin Muhammad Tanzil Furqon Nanda Firizki Ananta Ni Made Gita Dwi Purnamasari Ni Made Gita Dwi Purnamasari Nining Nahdiah Satriani Nur Hijriani Ayuning Sari Nurul Dyah Mentari Nurul Dyah Mentari Nurul Hidayat Prananda Antinasari Primantara Hari Trisnawan Putra Pandu Adikara Qiindil, Audry Rachmad Indrianto Rahmat Yani Rakhman Halim Satrio Randy Cahya Wihandika Ratih Diah Puspitasari Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Resti Febriana Ria Ine Pristiyanti Rika Raudhotul Rizqiyah Rizal Maulana Rizal Maulana, Rizal Rizal Setya Perdana Ro&#039;i Fahreza Nur Firmansyah Robertus Santoso Aji Putro Rodhiya, Hanif Robby Rosy Indah Permatasari Safier Yusuf Saiful Bahri Shandy, Ryo Shima Fanissa Silalahi, Gifo Armando Silvia Aprilla Sonny Christiano Gosaria Sudin, Mahmudin Suryani Agustin Sutrisno Sutrisno Thio Marta Elisa Yuridis Butar Butar Tibyani Tibyani Tibyani Tibyani Tri Afirianto Tri Afirianto Ulfa Lina Wulandari Umi Rofiqoh Ummah Karimah, Ummah Uswatun Hasanah Utaminingrum, Fitri Veronica Kristina Br Simamora Vina Adelina Wahyuni Lubis Widhi Yahya Wildan Aulia Rachman Winda Estu Nurjanah Winda Fitri Astiti Yessivha Imanuela Claudy Yuita Arum Sari Yuita Arum Sari Zafran, Muhammad Abyan Zubaidah Al Ubaidah Sakti