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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sistem Informasi dan Bisnis Cerdas Indonesian Journal of Information System Jurnal Eksplora Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang INTECOMS: Journal of Information Technology and Computer Science Jurnal DISPROTEK Compiler Jurnal ULTIMA InfoSys JUTEKIN (Jurnal Manajemen Informatika) INTEK: Informatika dan Teknologi Informasi Dharma Bakti Jurnal Sistem Cerdas JMAI (Jurnal Multimedia & Artificial Intelligence) Antivirus : Jurnal Ilmiah Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Technologia: Jurnal Ilmiah Informatika Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Masyarakat Berdaya dan Inovasi Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal ABM Mengabdi International Journal of Community Service Society: Jurnal Pengabdian Masyarakat Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Journal Of Information System And Artificial Intelligence Jurnal Sistem Informasi dan Bisnis Cerdas Jurnal Ekonomi, Akutansi dan Manajemen Nusantara AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Exhibition and Seminar on Science and Creative Technology – Al Azhar Proceeding International Journal of Informatics Engineering and Computing
Arita Witanti
Universitas Mercu Buana Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
SISTEM PAKAR UNTUK DIAGNOSIS PENYAKIT ANEMIA MENGGUNAKAN TEOREMA BAYES Naftali Sulardi; Arita Witanti
Jurnal Teknik Informatika (Jutif) Vol. 1 No. 1 (2020): JUTIF Volume 1, Number 1, June 2020
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2020.1.1.12

Abstract

Teorema Bayes adalah teorema yang digunakan dalam statistika untuk menghitung peluang suatu hipotesis. Basis pengetahuan sistem pakar diperoleh dari akuisisi pengetahuan pakar yaitu dokter. Penelitian ini mengenai rancangan sistem pakar untuk mendiagnosa penyakit anemia pada manusia dengan mengimplementasikan teorema bayes sebagai alat ukurnya. Penelitian ini menggunakan 50 data yang didapat melalui rekam medis, lalu rekam medis yang ada diimplementasikan kedalam sistem. Hasil pada sistem dicocokan dengan pakar hingga mendapatkan angka kecocokan maksimal dan hasil identifikasi yang mendekati. Berdasarkan dari 50 data yang telah diujikan terhadap pakar dan sistem, sistem dapat mendeteksi 3 jenis penyakit Anemia yaitu Anemia Aplastik, Anemia Defisiensi Zat Besi, Anemia Kronis. Untuk pasien yang mengalami anemia dan sesuai dengan validasi pakar adalah 45 pasien dan yang tidak sesuai adalah 5 pasien. Sehingga untuk tingkat akurasi sistem berdasarkan hasil validasi pakar dan sistem adalah 90%.
SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER Akhmad Muzaki; Arita Witanti
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.51

Abstract

The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%
Co-Authors Abdul Rahman Wahid Agus Tri Widiyanto Akhmad Muzaki Alexander Radja Bria, Nyongki Apriani, Nana Ardhian Yulihandra Hanum Ari Widiyatmoko Arnanda Nuryasa Asshiddiq, Muh. Hasbi Bangkit Sasangka Bowo Nugroho Dharmawan, Thoriq Didik Kurniawan Djaelani Sosanto Domnina Rani Puna Rengganis Eko Hariyanto Fatimah, Malida Fatkhurrahman Hazmi, Shadrina Heri Ardiansyah Herli Setiawan Herwinsyah Huda, Maasyaril Kirom Mi’Rojul Ibnu Fajar Shiddiq Indah Susilawati Insan, Fariq Maulana Jeffri, Derry Meilana Krisna Bima Bagus Saputra M. Abdurozik Mahendra Wahyu Prihantoro Mahmudie, Bagus Muh. Fahrurrozi Mukhamad Arifin Munfaati, Eka Aenun Nisa Mutaqin Akbar Nadiyah, Raden Ayu Salma Naftali Sulardi Natanael Dimas Randy Nesya Rogawati Novriansyah Rosi Nur Lestari Puji Oktavia, Feliana Ozzi Suria Pambudi, Wahyu Tities Parjono, Parjono Pramana Adi Setiawan Prasetyaningrum, Putri Taqwa Pusaka, Semerdanta Putry Wahyu Setyaningsih Recoba Abednego Davinci Reizandi, Dwisatya Rengganis, Domnina Rani Puna Rina Dwiarti Rizqi Oktafiani Rogawati, Nesya Sadikin, Arif Maulana Salsabilla, Fadiah Nur Saputra, David Saputro, Fredy Siti Mutia Candra Soeharto, Triana N.E.D Sowanya Ardi Prahara Subarjo subarjo, Subarjo Supatman Supatman Supatman Supatman, Supatman Suryo, Rakhmanto Tito Rikanto Triana Noor Edwina Dewayani Soeharto Triana Noor Edwina Dewayani Soeharto vanrika, adena reis Wahyu Setyaningsih, Putry Wahyu Tities Pambudi Wahyu Tities Pambudi Wibowo, Ibnu Surya Widarta Widarta, Widarta Widatama, Krisna William, Haris Wulandari, Erika Yetti Lutiyan Suprapto