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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) ELINVO (Electronics, Informatics, and Vocational Education) Creative Information Technology Journal CogITo Smart Journal Voice Of Informatics Majalah Ilmiah UNIKOM Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat CSRID (Computer Science Research and Its Development Journal) METIK JURNAL SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) IT (INFORMATIC TECHNIQUE) JOURNAL Informatics and Digital Expert (INDEX) INFOSYS (INFORMATION SYSTEM) JOURNAL JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) PUBLIDIMAS CSRID Jurnal Pengabdian Masyarakat (JURDIMAS) Jurnal Sistem Informasi dan Teknologi Informasi
Teuku Mufizar
Universitas Perjuangan Tasikmalaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : CSRID

 1 
Analisis Sentimen Pengguna Twitter Terhadap Vaksin Sinovac (Covid-19) Dengan Menggunakan Metode Naïve Bayes Evi Dewi Sri Mulyani; Teuku Mufizar; N. Nelis Febriani SM; Hendri Julian Pramana; Intan Hartiwan
Computer Science Research and Its Development Journal Vol. 15 No. 1: February 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

In 2020 Indonesia became one of the countries affected by this corona virus. The government has made various efforts to suppress the spread of the corona virus, one of which is by taking vaccinations. The existence of this vaccination, of course, received a response from the community. Many opinions that appear ranging from hopes to worries. One of the forums where the public can express themselves is through the social network Twitter. In the process of processing public opinion data from Twitter social media, a preprocessing process is needed which can then be classified. The method used to analyze public opinion on Covid-19 vaccination is Naive Bayes. The results of the analysis of public sentiment on the Sinovac vaccine using the Naive Bayes method on Twitter showed that of the 1,139 tweet data, 82% were positive and 18% were negative, so it can be concluded that public sentiment tends to be positive. With accuracy or model testing with Confusion Matrix and K Fold Validation, data accuracy is 80%.
PENERAPAN DATA MINING CLASSIFICATION UNTUK PENENTUAN JENIS BANTUAN SOSIAL MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Shinta Siti Sundari; Evi Dewi Sri Mulyani; Cepy Rahmat Hidayat; Dede Syahrul Anwar; Teuku Mufizar
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

The social assistance program is a program held by the government as an effort to overcome poverty. Mekarjaya Village is one of the villages running the program. In carrying out this social assistance process, there are obstacles in terms of collecting data on its citizens because there are often discrepancies in the recipient data collected by the community with the type of assistance. To make it easier to determine the appropriate type of social assistance, an analysis of the data on the recipients of the social assistance is needed. The data analysis method in this research uses Data Mining including Data Selection and Preprocessing, while the classification method uses the Naïve Bayes Classifier. Testing using the Confusion Matrix produces an accuracy of 94.53% with a comparison of training data and testing 80:20. With this model, it is hoped that village officials can determine the type of social assistance that is appropriate for the community.
SISTEM PAKAR DIAGNOSA PENYAKIT PADA SAPI BERBASIS WEB MENGGUNAKAN METODE FORWARD CHAINING Evi Dewi Sri Mulyani; N Nelis Febriani SM; Teuku Mufizar; Shinta Siti Sundari; Cepi Rahmat Hidayat; Gilang Muhammad Nur Alip; Kurdiman
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

Beternak sapi merupakan bisnis yang memiliki potensi ekonomi yang sangat menjanjikan, namun tingginya permintaan daging sapi dan air susu sapi tidak disertai dengan laju pertumbuhan ternak. Selain itu, kendala yang sering dialami oleh para peternak adalah proses merawat sapi agar terhindar dari penyakit berbahaya dan menular dengan cepat yang dapat berakibat pada kematian. Untuk mencegah agar sapi tidak sakit, maka pemilik sapi harus senantiasa berkonsultasi dengan dokter hewan agar dapat dilakukan pencegahan dan pengobatan terhadap hewan sapi sedini mungkin, namun terbatasnya pakar dan tingginya biaya konsultasi menjadi kendala utama bagi para peternak. Aplikasi pakar ini dirancang sebagai solusi dari kendala yang dihadapi oleh peternak, agar para peternak dapat melakukan konsultasi mengenai penyakit sapi sehingga peternak dapat melakukan penanganan sedini mungkin dari diagnosis yang dihasilkan. Dengan menggunakan metode Forward Chaining, proses pengumpulan fakta dimulai dari gejala yang ditemukan sampai menghasilkan diagnosis sebagai konklusinya dan dengan metode Certainty Factor, hasil diagnosis tersebut diberi nilai persentase atau tingkat keyakinannya
Analisis Sentimen Pengguna Twitter Terhadap Vaksin Sinovac (Covid-19) Dengan Menggunakan Metode Naïve Bayes Evi Dewi Sri Mulyani; Mufizar, Teuku; SM, N. Nelis Febriani; Pramana, Hendri Julian; Hartiwan, Intan
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 1: February 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.1.2023.12-21

Abstract

In 2020 Indonesia became one of the countries affected by this corona virus. The government has made various efforts to suppress the spread of the corona virus, one of which is by taking vaccinations. The existence of this vaccination, of course, received a response from the community. Many opinions that appear ranging from hopes to worries. One of the forums where the public can express themselves is through the social network Twitter. In the process of processing public opinion data from Twitter social media, a preprocessing process is needed which can then be classified. The method used to analyze public opinion on Covid-19 vaccination is Naive Bayes. The results of the analysis of public sentiment on the Sinovac vaccine using the Naive Bayes method on Twitter showed that of the 1,139 tweet data, 82% were positive and 18% were negative, so it can be concluded that public sentiment tends to be positive. With accuracy or model testing with Confusion Matrix and K Fold Validation, data accuracy is 80%.
PENERAPAN DATA MINING CLASSIFICATION UNTUK PENENTUAN JENIS BANTUAN SOSIAL MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Shinta Siti Sundari; Evi Dewi Sri Mulyani; Cepy Rahmat Hidayat; Dede Syahrul Anwar; Teuku Mufizar
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.1.2024.13-24

Abstract

The social assistance program is a program held by the government as an effort to overcome poverty. Mekarjaya Village is one of the villages running the program. In carrying out this social assistance process, there are obstacles in terms of collecting data on its citizens because there are often discrepancies in the recipient data collected by the community with the type of assistance. To make it easier to determine the appropriate type of social assistance, an analysis of the data on the recipients of the social assistance is needed. The data analysis method in this research uses Data Mining including Data Selection and Preprocessing, while the classification method uses the Naïve Bayes Classifier. Testing using the Confusion Matrix produces an accuracy of 94.53% with a comparison of training data and testing 80:20. With this model, it is hoped that village officials can determine the type of social assistance that is appropriate for the community.
SISTEM PAKAR DIAGNOSA PENYAKIT PADA SAPI BERBASIS WEB MENGGUNAKAN METODE FORWARD CHAINING Evi Dewi Sri Mulyani; N Nelis Febriani SM; Teuku Mufizar; Shinta Siti Sundari; Cepi Rahmat Hidayat; Gilang Muhammad Nur Alip; Kurdiman
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.1.2024.35-44

Abstract

Beternak sapi merupakan bisnis yang memiliki potensi ekonomi yang sangat menjanjikan, namun tingginya permintaan daging sapi dan air susu sapi tidak disertai dengan laju pertumbuhan ternak. Selain itu, kendala yang sering dialami oleh para peternak adalah proses merawat sapi agar terhindar dari penyakit berbahaya dan menular dengan cepat yang dapat berakibat pada kematian. Untuk mencegah agar sapi tidak sakit, maka pemilik sapi harus senantiasa berkonsultasi dengan dokter hewan agar dapat dilakukan pencegahan dan pengobatan terhadap hewan sapi sedini mungkin, namun terbatasnya pakar dan tingginya biaya konsultasi menjadi kendala utama bagi para peternak. Aplikasi pakar ini dirancang sebagai solusi dari kendala yang dihadapi oleh peternak, agar para peternak dapat melakukan konsultasi mengenai penyakit sapi sehingga peternak dapat melakukan penanganan sedini mungkin dari diagnosis yang dihasilkan. Dengan menggunakan metode Forward Chaining, proses pengumpulan fakta dimulai dari gejala yang ditemukan sampai menghasilkan diagnosis sebagai konklusinya dan dengan metode Certainty Factor, hasil diagnosis tersebut diberi nilai persentase atau tingkat keyakinannya
Rancang Bangun Game Edukasi Eksplorasi Wisata dan Budaya Tasikmalaya Anwar, Dede Syahrul; Mufizar, Teuku; Shafarulloh, M. Hisyam; Maulana, Akmal
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.127-135

Abstract

Tasikmalaya, a region rich in tourism and cultural diversity, is still not widely recognized by the public. In the digital era, interactive media such as games can serve as an effective tool to introduce and promote its potential. This research focuses on designing and developing an educational adventure game that explores the tourism and cultural aspects of Tasikmalaya. The novelty of this study lies in its interactive and educational approach, specifically designed to educate users, particularly the younger generation, about Tasikmalaya's tourist attractions and cultural heritage. By utilizing game technology, it is expected to provide an engaging and informative learning experience.The research method used is the Multimedia Development Life Cycle (MDLC). The concept stage involves identifying the game's needs and objectives. The design stage includes story development, character creation, and user interface design. The material collection stage focuses on gathering necessary information and media assets. The assembly stage involves the technical development of the game. Testing is conducted to ensure the game's quality and functionality, while the distribution stage delivers the game to users.The final result of this research is an educational game aimed at enhancing public knowledge and appreciation of Tasikmalaya's tourism and culture, while also contributing to scientific development through research publication.
Co-Authors Abdulrohman, Rijal Adilal Mahbub, Luthfi Agus Supriatman Agustin, Anggi Permana Ahmad Wakih, Agus Akbar Kasyfurrahman, Muhammad Alma Husna Hanifah Amelia Dewi Sani Septiani Andriani, Aan Aprianis, Epa Ardiani, Annisa Arianti Salama Arifatun Nasuha Awit Marwati Sakinah Aysicom Paraguay, Muhammad Ayu Rahmawati Cepi Rahmat Hidayat Cepi Rahmat Hidayat Cepy Rahmat Hidayat Cepy Rahmat Hidayat Chaeruddin, Rofi Dani Rohpandi Dede Sahrul Anwar Dede Syahrul Anwar Dede Syahrul Anwar Dede Syahrul Anwar Dede Syahrul Anwar, Dede Syahrul Dikdik Muhammad Siddiq Dinda Sofi Farhani Egi Badar Sambani, Egi Badar Estie Alfiyani Evi Dewi Sri Mulyani Fahroni, Rizal Farhani, Dinda Sofi Farid Hamzah Firna Pebrianti Fortuna Gilang Muhammad Nur Alip Gustaman, R Joni Gustiar Firmansyah, Nizar Hartiwan, Intan Herdi Muhammad Syaban Hidayatuloh, Ade Taopik Hikmatyar, Missi Indah Septianingrum Indradewa, Rhian Intan Hartiwan Kasyfurrahman, M. Akbar Khairul Anwar Kurdiman Kurdiman Kurdiman Ari Kurdiman, Kurdiman Lestari, Rima Listiani Lina Listiani linggar nursinggah Ma'ruf, Jamal Maulana, Akmal Mira Yuliani Muhamad Rizky Fadillah Muhamad Satrio Nugraha Muhammad Rizki Nugraha N. Nelis Febriani SM Nanang Suciyono, Nanang Nelis Nurjayanti, Nelis nursinggah, linggar Pramana, Hendri Julian Rahadi Deli Saputra Restu Adi Wiyono Rismansyah, Riki Roska Robby Awaludin Rudi Hartono Rustin Kania Dewi Ruuhwan Ruuhwan Ruuhwan Salsabila, Halda Sarmidi Sarmidi Sarmidi Sarmidi Shafarulloh, M. Hisyam Shinta Siti Sundari Sofiani, Efin Sudiarjo, Aso Susanto Susanto Syaban, Herdi Muhammad Teten Nuraen Tina Kumala Wahyu Kamaludin Wakih, Agus Ahmad Wulansari, Nindi Ayu Yuda Purnama Putra Yusep Rosmansyah Yusuf Sumaryana