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Analysis sentiment about islamophobia when Christchurch attack on social media Windu Gata; Achmad Bayhaqy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14179

Abstract

Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare Supervised Machine Learning Naïve Bayes and SVM algorithm. Addition of SMOTE for Imbalanced Data. As result, SVM with SMOTE is proven the highest performance value and short processing time.
Analisis Sentimen Dewan Perwakilan Rakyat Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization Anas Faisal; Yuris Alkhalifi; Achmad Rifai; Windu Gata
JOINTECS (Journal of Information Technology and Computer Science) Vol 5, No 2 (2020)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (909.958 KB) | DOI: 10.31328/jointecs.v5i2.1362

Abstract

Penggunaan internet terutama media sosial telah menjadi bagian dari kehidupan bernegara. Hal ini salah satunya karena Anggota Dewan Perwakilan Rakyat Republik Indonesia (DPR RI) banyak yang menyampaikan ide, kebijakan maupun memberikan komentar atas kebijakan pemerintah melalui media sosial. Penelitian ini dilakukan untuk mengukur pendapat atau memisahkan antara sentimen positif dan sentimen negatif terhadap DPR RI. Data yang digunakan dalam penelitian ini didapatkan dengan melakukan crawling pada media sosial twitter. Penelitian dilakukan dengan menggunakan dua Algoritma yaitu Algoritma Support Vector Machine (SVM) dan Naive Bayes (NB). Kedua algoritma tersebut masing-masing dioptimasi menggunakan Particle Swarm Optimization (PSO). Hasil pengujian k-fold cross validation SVM dan NB mendapatkan nilai accuracy 71,04% dan 70,69% dengan nilai Area Under the Curve (AUC) 0,817 dan 0,661. Sedangkan hasil pengujian k-flod cross validation dengan menggunakan PSO, untuk SVM dan NB masing-masing mendapatkan nilai accuracy 75,03% dan 73,49% dengan nilai AUC 0,808 dan 0,719. Penggunaan PSO mampu meningkatkan nilai accuracy algoritma SVM sebesar 3,99% dan 2,8% pada algoritma NB. Hasil dari pengujian kedua algoritma tersebut nilai accuracy tertinggi adalah SVM dengan PSO sebesar 75,03%.
ANALISIS SENTIMEN TERHADAP WARGA CHINA SAAT PANDEMI DENGANALGORITMATERM FREQUENCY-INVERSE DOCUMENT FREQUENCY DAN SUPPORT VECTOR MACHINE Efid Dwi Agustono; Daniel Sianturi; Andi Taufik; Windu Gata
Jurnal Informatika dan Rekayasa Elektronik Vol. 3 No. 2 (2020): JIRE Nopember 2020
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v3i2.258

Abstract

Sejak merebaknya virus Covid-19 secara global terjadi aksi anti China di berbagai negara. Tingkat kematian atas virus Covid-19 yang cukup tinggi menyebabkan banyak negara mengambil langkah pencegahan yang membatasi aktivitas setiap individu. Di Indonesia virus tersebut sudah menjangkit 34 provinsi dan 415 kabupaten/kota. Berdasarkan penelitian dari Wearesosial Hootsuite yang dipublikasikan pada Januari 2019 jumlah pengguna media sosial di Indonesia mencapai 150 juta pengguna atau mencapai 56 persen jumlah penduduk Indonesia. Twittermerupakan salah satu media sosial populer di mana pengguna dapat membuat status atau disebut "tweets". Kicauan tersebut mengandung banyak ekspresi suka, tidak suka, dan kontribusinya pada berbagai topik. Penelitian ini bertujuan untuk mengetahui sentimen warga Indonesia terhadap warga china yang ada di Indonesia dengan permodelan Term Frequuency-Inverse Document Frequency dan Algoritma Support Vector Machine pada media sosial twitter.
Implementasi Finite State Automata Dalam Siklus Pembelajaran Magister Ilmu Komputer STMIK Nusa Mandiri Angelina Puput Giovani; Faried Zamachsari; Efid Dwi Agustono; Muhammad Ilham Prasetya; Windu Gata
CESS (Journal of Computer Engineering, System and Science) Vol 5, No 2 (2020): JULI 2020
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.462 KB) | DOI: 10.24114/cess.v5i2.16696

Abstract

Menyelesaikan Pendidikan Magister Ilmu Komputer pada STMIK Nusa Mandiri dalam waktu 4 semester merupakan harapan setiap mahasiswa. Untuk dapat lulus tepat waktu setiap mahasiswa wajib memenuhi semua persyaratan yang telah ditentukan oleh pihak kampus. Dalam tiap semester terdapat berbagai kegiatan diluar kegiatan belajar mengajar yang wajib diikuti oleh mahasiswa. Kegiatan tersebut meliputi Seminar, Workshop, dan Tes TOEFL. Hal-hal tersebut seringkali tidak diketahui mahasiswa sehingga tidak lulus mata kuliah tertentu. Apabila seorang mahasiswa dinyatakan tidak lulus mata kuliah tertentu maka diwajibkan untuk mengulang di semester berikutnya. Mengulang mata kuliah akan menambah pengeluaran dan tentunya menambah waktu belajar sehingga tidak dapat lulus tepat waktu sesuai yang diharapkan. Pada paper ini akan membahas tentang bagaimana Finite State Automata (FSA) jenis Nondeterministic Finite Automata (NFA) dapat diimplementasikan dalam siklus pembelajaran Magister Ilmu Komputer pada STMIK Nusa Mandiri. Dengan diterapkan metode ini  diharapkan dapat membantu mahasiswa dalam pemenuhan persyaratan untuk mencapai kelulusan
Optimasi Minimum Pola Baju Khas Kain Tenun Sarung Samarinda Menggunakan Algoritma Greedy Hani Subakti; Windu Gata
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 11, No 1 (2021): Jurnal Inspiration Volume 11 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v11i1.2602

Abstract

Nowadays, the typical clothes of the Samarinda sarong woven cloth have become a secondary requirement in the daily activities of the people in Samarinda City. In making the typical woven cloth, the sarong of Samarinda requires a sufficient area of the woven cloth to attach it to the pattern of clothes of various sizes. This research requires an appropriate method in measuring the area of woven fabric required for the installation of clothing patterns of various sizes, namely by using the greedy algorithm. Greedy algorithm is the right method to solve optimization problems, including the size of the fabric. With the method used, it is indicated that it can make it easier for producers or makers of typical clothes patterns for the Samarinda sarong woven cloth to find out the size of the woven cloth they need. Therefore, in this study several stages were carried out, including the first stage analyzing the medium-sized clothing pattern, the second stage measuring the area of each pattern on the shirt section, and the third stage installing a medium-sized pattern on the woven cloth typical of Samarinda sarong by optimizing the width of the cloth. there is. The application of the typical cloth woven sarong pattern of Samarinda by using the greedy algorithm on the medium size can be concluded to be able to optimize the use of these fabrics. This systematic stage can make it easier for producers or makers of typical Samarinda sarong woven cloth to utilize the woven fabric to the minimum so that there is no waste of the Samarinda sarong woven fabric. Keywords: Shirt, Typical fabric woven sarong Samarinda, Patterns, Greedy Algorithm
Analisis Sentimen Stakeholder Atas Layanan HAIDJPB Pada Media Sosial Twitter Dengan Menggunakan Metode Support Vector Machine Dan Naïve Bayes Muhammad Luthfiy Kurniawan Harsono; Yuris Alkhalifi; Nurajijah; Windu Gata
Infoman's : Jurnal Ilmu-ilmu Manajemen dan Informatika Vol. 14 No. 1 (2020): Infoman's
Publisher : STMIK Sumedang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1200.97 KB) | DOI: 10.33481/infomans.v14i1.126

Abstract

Pembuatan akun khusus pada media sosial instagram dan twitter yang bertujuan untuk menampung penyampaian pertanyaan, masukan, kritik dan saran dari stakeholder seputar proses bisnis berjalan serta penggunaan aplikasi dalam rangka perencanaan anggaran, pembuatan komitmen, pencairan APBN, pembukuan penerimaan serta pelaporan keuangan didasarkan pada fakta bahwa media sosial tidak dapat dipisahkan dari aktivitas masyarakat dikarenakan keberadaan perangkat digital (smartphone) dan akses internet yang terjangkau membuat berbagai kalangan masyarakat dapat memperoleh informasi dengan cepat dan mudah. Organisasi mempunyai kepentingan untuk mendapatkan tolak ukur atas layanan yang telah diberikan demi peningkatan kualitas layanan kedepannya berdasarkan data tweets yang didapatkan dari media sosial twitter. Penelitian kali ini membahas tentang proses pengumpulan dan pengolahan data tweet pada akun @haiDJPb dalam rangka melakukan analisis sentimen stakeholder atas layanan haiDJPb pada media soasial twitter menggunakan algoritma Support Vector Machine dan Naïve Bayes dan didapatkan hasil nilai akurasi untuk algoritma Support Vector Machine adalah 74,55% dan 77,18% untuk algoritma Naïve Bayes.
Pengamanan Ruangan Dengan Dfrduino Uno R3, Sensor Mc-38, Pir, Notifikasi Sms, Twitter Siswanto Siswanto; Gunawan Pria Utama; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 3 (2018): Desember 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1251.822 KB) | DOI: 10.29207/resti.v2i3.592

Abstract

Security officers can not monitor the security of the financial space at all times especially outside working hours or evenings because the financial room is on the 3rd floor. Security guards are not warned quickly if there are people who do not have the right to enter the financial room at the time of blank or after hours. The purpose of this research is to design an application that can monitor the security of financial space by giving information or giving warning in the form of alarm and sending SMS and Twitter notification to security officer if there are people who do not have access rights of financial space, using DFRduino Uno R3 microcontroller, MC-38 magnets, PIR sensors, Alarms, Sony Ericsson Z530i phone, wifi modem, and Bluetooth. If a door or movement is detected the computer will send a command to DFRduino which is then forwarded to the alarm to give a sound alert. And with the Hanphone Sony Ericsson Z530i with a connection via Bluetooth Mobile can connect with the application so that the computer can send notification SMS alert to the number that has been registered as the recipient of SMS and notification via Twitter to the username that has been registered. With the magnet sensor it is possible to detect if the door or window is forced and PIR sensor is used as a support if the magnetic sensor is not working or if the thief enter the room not through the door or window. PIR Sensor installed in the room allows all activities that occur will be able to monitor well. If there is a security breach or infiltration it will be quickly known because there are warnings via SMS and Twitter that can provide information to security personnel to perform actions quickly so that cases can be resolved thoroughly.
Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online Sigit Kurniawan; Windu Gata; Dewi Ayu Puspitawati; Nurmalasari -; Muhamad Tabrani; Kadinar Novel
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.706 KB) | DOI: 10.29207/resti.v3i2.935

Abstract

General elections are an important part of the political process so that many political figures participate in the process. Electability is one of the concerns, various things are done to be able to increase the electability of political figures who participate in general elections. Media has become one of the important tools used to increase electability, one of which is online news media. Reader comments can be used as an assessment of political figures in the form of sentiment analysis. However, it is not easy to analyze sentiments from comments on online news media, because comments contain unstructured text, especially in Indonesian text. Text pre-processing in text mining is an important part of getting the basic information contained in the comments. This research uses Indonesian text pre-processing using the Gata Framework Tetmining. Then proceed with extracting information using the Naïve Bayes classification algorithm and Support Vector Machine which are optimized using Particle Swarm Optimization. Tests carried out with both methods get the results that, Particle Swarm Optimization based on Support Vector Machine is the best method with an accuracy of 78.40% and AUC 0.850. This study found an algorithm that was effective in classifying positive and negative comments related to political figures from online news media.
Sistem Pakar Untuk Mengidentifikasi Kerusakan Perangkat PABX Panasonic NS1000 Dengan A* Pathfinding Siswanto Siswanto; Helmy Ligaputra; M. Anif; Windu Gata; Basuki Hari Prasetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (686.718 KB) | DOI: 10.29207/resti.v4i3.1882

Abstract

Someone can be said to have the ability to handle damage to the Panasonic NS1000 PABX device and perform a configuration of at least 3 years or already have a certificate of competence and training. The problem to be investigated is the number of experts there is only one and can only identify 5 damage per day and 1 day there are 25 damage. PABX Panasonic NS1000 has 30 rules and 130 data symptoms of damage as its knowledge base. This Expert System is designed to identify damage to PABX Panasonic NS1000 mobile application-based applications using the A * (A star) pathfinding algorithm and the forward chaining method with the PHP (Jquery Mobile) programming language and the database using MySQL, as an application program used by PT. Mediatama Anugrah Citra to facilitate the process of troubleshooting and effective problem solving on target. The features in this expert system include the identification of problems, the identification process, up to the achievement of the goal state and solutions effectively, quickly and precisely, the user as the user or admin has its own login and access rights in identifying, entry and editing. In the programming case testing process it can be seen that the application of the A * Pathfinding algorithm with the heuristic function has proven ineffectively implemented in the expert system and the results of the UAT testing process, the respondents agree (above 91.23%) that overall the expert system helps the expert and can deduce damage to PABX devices correctly.
Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection Algoritma Naive Bayes dan Support Vector Machine Faried Zamachsari; Gabriel Vangeran Saragih; Susafa'ati; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.834 KB) | DOI: 10.29207/resti.v4i3.1942

Abstract

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.