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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Sri Yulianto Joko Prasetyo
Universitas Kristen Satya Wacana
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE Indra Yunanto; Sri Yulianto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.292

Abstract

The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%.
IDENTIFICATION OF THE COVID-19 DISTRIBUTION AREA ON THE ISLAND OF KALIMANTAN USING THE K-MEANS SPATIAL CLUSTERING METHOD Fabian Valerian; Sri Yulianto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.314

Abstract

Based on the map of the spread of COVID-19 in Indonesia, Kalimantan Island is the second island with the number of COVID-19 distributions after Java Island. The purpose of this study is to provide information to the entire community and government, especially the Kalimantan region regarding the clustering of the spread of COVID-19. The K-Means algorithm method used in the grouping is based on data on positive, recovered, and deceased people collected by each province on the island of Kalimantan, then a geographic information system (GIS) is applied in mapping to display the clustered distribution area of ​​each district on the island of Kalimantan. The result of this research is that the k-means algorithm is able to classify data with low, medium, and high distribution levels so that later the distribution area can be mapped using GIS based on the results of the clustering. With the results of this application, it is hoped that it can be used as information for the government and also the public to think about what efforts should be made if bad things happen later, based on the level of spread to be used as a priority scale in controlling the spread of the COVID-19 virus.
ANDROID-BASED EDUCATIONAL GAME: RECOGNITION OF PAPUA ENDEMIC ANIMALS Kristia Yuliawan; Gunawan Prayitno; Sutarto Wijono; Sri Yulianto Joko Prasetyo; Suryasatriya Trihandaru
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.319

Abstract

Papua is the largest island in Indonesia; several animals are included in the endemic group. These animals are only found in certain areas and not in other areas. To study the endemic animals of Papua, children can explore them through books that display pictures of endemic animals in Papua. Children often experience difficulties learning from books taught by teachers and parents caused by children who are less enthusiastic about participating in learning. Another problem is that learning about Papua's endemic animals through books is impractical and inefficient because thick books provide a heavy burden for children to carry. Hence, children are reluctant to study them. With educational games, media is a medium that can be used by children so that it is easy to give lessons about the endemic animals of Papua. This educational game increases efficiency and effectiveness in terms of the learning process at home and school. Learning this educational game can be done anywhere at any time so that children can learn about Papua's endemic animals innovatively and efficiently. The method used in making this educational game introducing Papua's endemic animals uses the Agile Development method. Based on testing the educational game application using the black box method, it was found that this educational game was following what was expected because there were no errors found in the menu on the system, so it worked properly.
CLASSIFICATION OF REGIONAL LANGUAGES USING METHODS GRADIENT BOOTS AND RANDOM FOREST Patasik, Eva Sapan; Yulianto, Sri
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1459

Abstract

Indonesia is one of the countries that has the most regional languages ​​in the world, ranking second most. The large number of regional languages ​​that are owned makes it difficult for people between regions to recognize the origins of the regional language, so the author aims to conduct research by identifying a regional language. Identifying a language using data mining, one of the data mining techniques is classification. Classification is a technique used to find the value of data. Classification will build a model from samples of data into groups of the same type. There are two classification methods used in this research, namely gradient boots and random forest, where the two methods will be compared using regional language data from Java, Nias and Toraja. The results of calculating the accuracy values ​​for the two methods used are quite good in classifying languages ​​with results of an accuracy level of 0.8 or 80%, where the results of the gradient boots research have an accuracy value of 0.8850 or 88.5%, while the random forest method has an accuracy value. random forest is lower, namely 0.8794 or 87.94%, so in this study the gradient boots method is the best method.
Co-Authors Adenia Kusuma Dayanthi Anna Simatauw Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Ayuningtyas, Fajar Baali, Gabriel Megfaden Kenisa Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Devianto, Yudo Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Evi Maria Evi Maria Fabian Valerian Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Joko Siswanto Joko Siswanto Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Manongga, Daniel HF Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nusantara, Bandhu Otniel, Marcelinus Vito Patasik, Eva Sapan Patrick Simbolon Permatasari, Aurilia Dinda Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priatna , Wowon Priyadi Priyadi Purwoko, Agus Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Sarassati, Dwi Sinta Sebastian, Danny Septian Silvianugroho Septio, Pius Aldi Solly Aryza Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wahani, Puteri Justia Kardia Momuat Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo Yuliawan, Kristia