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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

Author-ID : 2376497

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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Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair Adikara, Putra Pandu; Wihandika, Randy Cahya; Utaminingrum, Fitri; Sari, Yuita Arum; Fauzi, M Ali; Syauqy, Dahnial; Maulana, Rizal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): Januari-Juni (3/7)
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Â Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.Â Â Â This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchairâs pathway based on the line shape of reflected line laser. Obstacleâs distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm.

Klasifikasi Berita Berbahasa Indonesia Mengggunakan Seleksi Fitur Dua Tahap Dan NaÃ¯ve Bayes Fauzi, M Ali; Gosario, Sony; Arifin, Agus Zainal
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 | DOI: 10.29080/systemic.v3i2.240

Jumlah dokumen digital telah meningkat secara pesat, sehingga klasifikasi dokumen secara otomatis menjadi sangat penting. Pemilihan fitur diperlukan dalam klasifikasi dokumen otomatis. Salah satu metode seleksi fitur yang terbukti handal adalah Maximal Marginal Relevance for Feature Selection (MMR-FS), namun metode ini memiliki kompleksitas yang tinggi. Dalam penelitian ini, diusulkan sebuah metode baru dalam pemilihan fitur untuk klasifikasi dokumen. Metode yang diusulkan terdiri dari dua tahap, yang pertama adalah Information Gain dan yang kedua adalah MMR-FS . Pada proses klasifikasinya digunakan metode Naïve Bayes. Dalam percobaan yang dilakukan, metode yang diusulkan bisa mencapai akurasi 86%. Metode baru ini dapat menurunkan kompleksitas MMR-FS namun tetap mempertahankan keakuratannya.

Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair Adikara, Putra Pandu; Wihandika, Randy Cahya; Utaminingrum, Fitri; Sari, Yuita Arum; Fauzi, M Ali; Syauqy, Dahnial; Maulana, Rizal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v3i1.587

Â Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.Â Â Â This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchairâ€™s pathway based on the line shape of reflected line laser. Obstacleâ€™s distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm.

Arabic Book Retrieval using Class and Book Index Based Term Weighting M. Ali Fauzi; Agus Zainal Arifin; Anny Yuniarti
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic Fiqh (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%.

Word2Vec model for sentiment analysis of product reviews in Indonesian language M. Ali Fauzi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.

Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair Fitri Utaminingrum; Hurriyatul Fitriyah; Randy Cahya Wihandika; M Ali Fauzi; Dahnial Syauqy; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.

Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor M. Rizzo Irfan; M. Ali Fauzi; Tibyani Tibyani; Nurul Dyah Mentari
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used twitter specific features, textual features, Parts of Speech (POS) features, lexicon based features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion M. Ali Fauzi; Ro'i Fahreza Nur Firmansyah; Tri Afirianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7751

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not be suitable for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these problems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can be applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts using Naïve Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naïve Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The best sentiment classification performance using proposed feature expansion is obtained by accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large number of them.

OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM dwi taufik hidayat; Isnan .; Muhammad Ali Fauzi
Jurnal Ilmu Komputer dan Informasi Vol 6, No 1 (2013): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.

Klasifikasi Teks Bahasa Indonesia Pada Dokumen Pengaduan Sambat Online Menggunakan Metode K-Nearest Neighbors Dan Chi-square Claudio Fresta Suharno; M. Ali Fauzi; Rizal Setya Perdana
Systemic: Information System and Informatics Journal Vol. 3 No. 1 (2017): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

K-Nearest Neighbors (K-NN) merupakan metode klasifikasi yang mudah untuk dipahami. Akan tetapi metode tersebut memiliki beberapa kekurangan, salah satunya dalam aspek komputasi perhitungan yang besar. Oleh karena itu, seleksi fitur digunakan sebagai salah satu cara untuk mengurangi besarnya komputasi adalah dengan mengurangi jumlah fitur yang tidak relevan dalam klasifikasi teks. Metode seleksi fitur yang digunakan adalah menggunakan metode Chi-Square untuk menghitung tingkat dependensi fitur. Proses yang dilakukan adalah mengumpulkan dokumen latih dan dokumen uji, melakukan tahap preprocessing dan seleksi fitur, kemudian dilakukan klasifikasi, dan pada tahap akhir dilakukan pengujian dan analisis terhadap hasil klasifikasi oleh sistem terkait nilai precision, recall, dan F-Measure. Dari penelitian ini dihasilkan bahwa seleksi fitur dapat meningkatkan nilai F-Measure dalam klasifikasi teks berbahasa Indonesia pada dokumen pengaduan SAMBAT Online dengan menggunakan metode klasifikasi K-Nearest Neighbors

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' 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'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

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