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All Journal INFORMAL: Informatics Journal UNEJ e-Proceeding Sistemasi: Jurnal Sistem Informasi Jurnal Komputer Terapan JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) JAST : Jurnal Aplikasi Sains dan Teknologi J-Dinamika: Jurnal Pengabdian Kepada Masyarakat IJAIT (International Journal of Applied Information Technology) TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Melek IT: Information Technology Journal Ilmu Komputer untuk Masyarakat Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Journal of Applied Agricultural Science and Technology Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Informatics Development JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) DIGIMAS: Jurnal Transformasi Digital Masyarakat
Priza Pandunata
Program Studi Teknologi Informasi, Fakultas Ilmu Komputer, Universitas Jember
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : INFORMAL: Informatics Journal

 1 
Tourism, Art, and Unique Using Culture Products Promotion with Android-Based Applications Priza Pandunata
INFORMAL: Informatics Journal Vol 2 No 2 (2017): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Government of Banyuwangi is actively promoting tourism, one of the most intensively promoted and most desirable is tourism and the unique art of Using where the Using (or Osing) tribe is known as the indigenous people of Banyuwangi. Some of Osing's popular cultural and artistic products that are often promoted include several dance performances, traditional rituals and traditional villages. One of the potential promotional media that can be used is the use of mobile devices, due to the large number of mobile device users. Therefore, to support the promotion of tourism, art and cultural products of Osing and to help the tourists in obtaining tourism information in Banyuwangi, then an application based on Android tourism is made. To develop an Android-based application is done through a series of stages: Requirement Analysis, Literature review, Data Collection and Data Processing, Application Design and code, and Testing and Evaluation.
Evaluasi Kesuksesan Web Desa Pada Kecamatan Maesan Menggunakan Information System Success Model (ISSM) Oktalia Juwita; Vian Elfada; Priza Pandunata
INFORMAL: Informatics Journal Vol 4 No 2 (2019): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v4i2.11284

Abstract

Web Desa adalah situs berbasis online yang di dalamnya terdapat informasi seputar desa yang dioperasikan oleh PPID (Petugas Pengelola Informasi Desa). Saat ini PPID di Kecamatan Maesan jarang melakukan update informasi setiap harinya. Hal itu disebabkan karena PPID kebanyakan belum bisa untuk melakukan posting dan kesulitan untuk membuat beritanya. Penelitian ini akan mengevaluasi Web Desa dengan menggunakan metode Information System Success Model (ISSM). Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang mendukung dan menghambat kesuksesan dari Web Desa. Data yang digunakan yaitu 61 responden yang terdiri dari PPID dari setiap desa. Penelitian ini menggunakan aplikasi GeSCA untuk uji hipotesisnya dan SPSS untuk uji validitas dan reliabelitasnya. Hasil dari penelitian ini menunjukkan jika variabel Use berpengaruh positif dan signifikan terhadap variabel Net Benefits dan Net Benefits berpengaruh positif dan signifikan terhadap Use.
Sistem Pendukung Keputusan Seleksi Beasiswa Situbondo Unggul Menggunakan Metode Simple Additive Weighting dan Profile Matching Moh Febri Nurul Qorik; Slamin Slamin; Priza Pandunata
INFORMAL: Informatics Journal Vol 3 No 1 (2018): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v3i1.9853

Abstract

This study is about the use of simple additive weighting (SAW) and profile matching methods for the construction of information systems supporting the decision selection of scholarships. The case study of this research is the selection of superior situbondo scholarships in the Education Office of Situbondo Regency. The purpose of this study was to apply the profile matching method and SAW in resolving the problem of selecting the Situbondo Superior Scholarship recipients who had 2 aspects of assessment, namely aspects of poverty and academics which made it difficult to select scholarships, this difficulty is caused by the value of the aspect of poverty must be the smaller the difference in the value of students with the value of the scholarship it will be better while the academic value must go far beyond the minimum value, the better. The profile matching method is used to calculate the value of the aspect of poverty and SAW is used to calculate the value of the academic aspects. To determine the final results of this study using the criteria for decision-making scale determined by the District Education Office of Situbondo, one of the final methods of the method is less than 30 worth not funded, one of the final method values less than 60 is considered to be funded. The development of information systems supporting superior decision making for the Situbondo scholarship using the profile matching and simple additive weighting methods using a website-based system with system design using the SDLC waterfall model, implementation of the system using the laravel framework and program code using the hypertext pre-processor programming language (PHP ), while for managing the database using MySQL DBMS and system testing using black box and white box ( testing unit). The results of this research method of profile matching and simple additive weighting can be applied properly in a superior situbondo scholarship decision support system.
Sistem Prediksi Jumlah Permintaan Produk Darah Menggunakan Metode Least Square Regression Line (Studi Kasus : Utd Pmi Kabupaten Jombang) Dzurrotun Nasyika; Slamin Slamin; Priza Pandunata
INFORMAL: Informatics Journal Vol 3 No 2 (2018): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v3i2.9989

Abstract

Blood Transfusion Unit (UTD) as the provider of blood supply is required to fulfill blood demand. However in reality, the blood stock does not always meet the demand for blood. The amount of blood group stock in UTD depends on blood donors who voluntarily donate blood. Red blood cells only have a life span of 35 days since they were donated, if they have passed that time the blood cannot be used for transfusion. Based on these problems, the authors applied the Lesat Square Regression Line method to predict the number of requests for blood products in the coming month. The calculation results obtained prediction of demand with the smallest MAPE value that is blood product of PRC blood type A with a value of 14.40%. The biggest MAPE value obtained is 180.66% for WB blood type AB. Factors or parameters that affect the amount of blood product demand, that is the environment, disease outbreaks and differences in body resistance of each blood group. In addition, the types of blood products also have a number of different requests depending on the level of blood needs for health.
Pencarian Rute Terpendek untuk Pengoptimalan Ditribusi Sales Rokok Gudang Garam di kecamatan Wuluhan Kabupaten Jember Menggunakan Algoritma Genetika Rachmad Agung Bagaskoro; Agung Ilham Bachtiar; Ani Andriani; Priza Pandunata
INFORMAL: Informatics Journal Vol 2 No 3 (2017): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah satu kegiatan dalam pemasaran adalah pendistribusian suatu produk dari satu tempat ke tempat lain. Dalam mendistribusikan suatu produk, faktor jarak tempuh dan waktu tempuh menjadi hal yang cukup penting untuk diperhatikan, karena melibatkan banyak hal dalam pengoperasianya. Algoritma genetika memiliki fungsi yaitu mendapatkan nilai solusi optimal terhadap permasalahan yang mempunyai banyak kemungkinan solusi , Hasil dari penelitian ini , setiap kali pengujian muncul rute yang berbeda beda tetapi dari beberapa kali pengujian ditemukan rute yang paling pendek adalah dengan jarak 69,75 Km
Analisis Sentimen Opini Publik Terhadap Pekan Olahraga Nasional Pada Instagram Menggunakan Metode Naïve Bayes Classififer Priza Pandunata; Caesarina Kurnia Ananta; Yanuar Nurdiansyah
INFORMAL: Informatics Journal Vol 7 No 2 (2022): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v7i2.33928

Abstract

National Sports Week (Pekan Olahraga Nasional) held in October 2021 in Papua has brought many pros and cons from the public. This topic allows public for give criticism, suggestions, and opinions about the National Sport Week 2021. Instagram in one of social media that popular place for deliver public opinion. The process of sentiment analysis can find and solve problems based on public opinion on social media such as Instagram. The classification method used in this research is Naïve Bayes Classifier. The dataset can be obtained from data crawling process using the google chrome extension: IGCommentExport. The data the labelled as positive, neutral, or negative. The labelling process result showed 965 negative data, 256 neutral data, and 770 positive data. Then pre-processing is carried out on the data that has been labeled before, also word weighting process using TF-IDF. After that modelling is carried out using Naïve Bayes Classifier and the last process is evaluation-testing. The high accuracy of the result from fourth experiment which compare 90% data training with 10% data testing produce 75% accuracy. While the result of sentiment test show that negative sentiment more than positive sentiment and neutral sentiment.
Analisis Sentimen Opini Publik Terhadap Program Vaksinasi Covid-19 Di Indonesia Pada Twitter Menggunakan Metode Naive Bayes Classifier Priza Pandunata; Kukuh Tri Winarno N; Yanuar Nurdiansyah; Nova El Maidah
INFORMAL: Informatics Journal Vol 7 No 3 (2022): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v7i3.34930

Abstract

The COVID-19 virus emerged in December 2019 in China and actively spread throughout the world including Indonesia in early 2020. Its spread is very fast and has caused millions of deaths. Therefore, the Indonesian government is actively holding a COVID-19 vaccination program to prevent the spread of the virus and make the public immune to the virus. But the program invites pros and cons among the community. Twitter is one of the social media that is famous for being a medium of opinion from the general public. The process of sentiment analysis can find and solve problems based on public opinion on social media such as Instagram. The classification method used in this research is Naïve Bayes Classifier. The dataset can be obtained from data crawling process using Google Collabs and python programming language. The total dataset obtained is 2000. The data the labelled as positive, neutral, or negative. The labelling process result showed 1579 positive data, 277 negative data, and 144 neutral data. Then pre-processing is carried out on the data that has been labeled before, also word weighting process using TF-IDF. After that modelling is carried out using Naïve Bayes Classifier and the last process is evaluation-testing. The high accuracy of the result from fourth experiment which compare 90% data training with 10% data testing produce 86% accuracy. While the result of sentiment test show that positive sentiment more than negative sentiment and neutral sentiment.
Analisis Komentar Toxic Terhadap Informasi COVID-19 pada YouTube Kementerian Kesehatan Menggunakan Metode Naïve Bayes Classifier Romadina, Vira Nindya; Juwita, Oktalia; Pandunata, Priza
INFORMAL: Informatics Journal Vol 9 No 1 (2024): INFORMATICS JOURNAL (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i1.48126

Abstract

Countries around the world were shocked by the outbreak of a new virus in 2020, which quickly transmitted and attacks humans of all ages. The virus is COVID-19. The government has advised through social media to stay at home and got vaccinated. YouTube has become a platform for the government, especially the Ministry of Health, to share public information in the COVID-19’s pandemic. Public can put their comments on video uploaded by the Ministry of Health. An analysis of comments is needed so that the information in comments can be useful for those who read and evaluated by the government so that they can provide information that the public can understand. In analyzing toxic comments, it can used text mining. And one of that is the Naïve Bayes Classifier. This study uses the Naïve Bayes Classifier method to determine the results of the analysis. Measuring the value of accuracy, this study using the Confusion Matrix evaluation. From the final result, the highest accuracy value is in the comparison of 90%:10% with an accuracy value of 80%. And from the results of the analysis, the most toxic words used are the words ‘dead’, 'business', ‘public' and 'fool'. From the results show that there are still many people who do not believe in the existence of COVID-19 and think that vaccines can cause death in people who are vaccinated.
Topic Modeling and Sentiment Analysis of YouTube Podcast “Susahnya Jadi Perempuan” Using LDA and SVM Algorithms Satriyo, Levinda Caesarianty Putri; Hidayat, Muhammad Arief; Pandunata, Priza
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.51407

Abstract

Youtube Podcast “Susahnya Jadi Perempuan” which addresses feminist issues has garnered attention from viewers of Najwa Shihab Channel. In this digital era, sentiment analysis of audience response is needed to understanding public perception. Method that can used to determine discussion topics and analyze sentiments is using Latent Dirichlet Allocation and Support Vector Machine. Analysis of 10.979 comments using LDA identified two subtopics: discussion about the invented speakers and gender roles in daily life. Along with this, sentiment analysis using an optimized SVM (C=1, gamma=1, kernel=linear) which classified sentiments into Positive, Negative, and Neutral categories with an accuracy of 67%. The main challenge was the low recall value for Neutral sentiments classification. The results showed that in subtopic 0, there were 3.503 Negative sentiments, 3.255 Positive sentiments, and 822 Neutral sentiments. In subtopic 1, there were 1.485 Negative sentiments, 1.671 Positive sentiments, and 243 Neutral sentiments.
Co-Authors Aditya Kurniawan Adnan, Fahrobby Agung Ilham Bachtiar Ali, Saifur Rifqi Amalia, Karina Nine Ani Andriani Arifin, Fajrin Nurman Asnawati Bahariawan, Amal Bambang Bayu Taruna Widjaja Putra Caesarina Kurnia Ananta Candra Bumi Carolus Rahmadita P. P Dhaifullah, Dany Zaky Dwi Indarti Dzurrotun Nasyika Fajariyanto, Gama Wisnu Fatchur Rahman Furqon, Muhammad Ariful Gama Wisnu Fajarianto Hidayat, Muhamad Arief Hidayat, Muhammad Arief Indarto Indarto Kukuh Tri Winarno N Leba, Katarina Luh Putu Ratna Sundari M Firmansyah, Diksy Moh Febri Nurul Qorik Mohammad Zarkasi Najwa, Nina Fadilah Nazdirah, Rufiani Nova El Maidah Nova El Maidah, Nova El Oktalia Juwita Oktavia, Nelly Pradana Febrian Murtadlo Prakoso, Aryo Rachmad Agung Bagaskoro Rahayu, Chairiyah Umi Ramadhani, Salsabilla Razanah, Muhammad Rashif Romadina, Vira Nindya S Slamin Saputri, Yunita Maulida Saraswati, Devy Satriyo, Levinda Caesarianty Putri Soekma Yeni Astuti Sujarwo, Mohamad Wawan Vian Elfada Wijaya, Anggito Karta Yanuar Nurdiansyah Yanuar Nurdiansyah, Yanuar Yudha Alif Auliya Zarkasi, Mohamad Zarkasi, Mohammad