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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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DECISION SUPPORT SYSTEM FOR THE NUMBER OF BLOOD REQUESTS PREDICTION AT THE BLOOD DONOR UNIT PMI JEMBER USING LINEAR REGRESSION AND DOUBLE EXPONETIAL SMOOTHING METHODS Priza Pandunata; Oktalia Juwita; Carolus Rahmadita P. P
UNEJ e-Proceeding 2022: E-Prosiding Kolokium Hasil Penelitian dan Pengabdian kepada Masyarakat
Publisher : UPT Penerbitan Universitas Jember

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

Abstract

One in four people in the world needs a blood transfusion during their lifetime, but only 37% of the population qualify as donors, and only 10% donate blood regularly. The fact that more blood needs than donating blood makes UDD PMI challenging to meet demand when the existing bloodstock is insufficient or empty. And based on the results of research interviews with sources, there was a problem of increasing blood stocks that decreased in certain months, followed by a decrease in blood demand, which caused a reasonably high change in the following month. This caused blood demand could not be met due to reduced demand and supply but increased in the next month without sufficient stock increase. Based on the problems described above, researchers see the need to estimate the number of blood demand needs in the Jember PMI UDD with linear regression and Double Exponential Smoothing methods. With the forecasting and understanding of past time series models, it is possible to predict future values. From there, researchers want to minimize stakeholder decision-making errors and provide options to stakeholders concerning the minimum number of blood bags that need to be provided. In conclusion, the results of the prediction implementation predict data that is different from the data found in the field. The approximate accuracy is 60.32% and 46.96% for linear regression and double exponential smoothing. The worst absolute error value of the linear regression method is data 0 and 15 using actual data. The worst absolute error is the worst double exponential data 0 and 27.7. Keywords: Blood transfusion, demand and supply, forecasting, linear regression, Double Exponential Smoothing
Pelatihan Pemanfaatan Google Sites dan Integrasi Nama Domain Sebagai Sarana Publikasi Informasi pada TKIT Buah Hati Kita Jember Mohammad Zarkasi; Priza Pandunata; Muhammad 'Ariful Furqon; Diah Ayu Retnani Wulandari; Yudha Alif Auliya
Ilmu Komputer untuk Masyarakat Vol 3, No 1 (2022)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v3i1.1255

Abstract

Taman Kanak-kanak Islam Terpadu (TKIT) Buah Hati Kita Jember terletak kurang lebih 2 km dari Universitas Jember. Dalam kegiatan penerimaan siswa baru, selama ini TK masih melakukan publikasi dengan cara-cara yang konvensional, seperti dengan memasang banner dan menyebar pamphlet dengan mencantumkan kontak telepon TK bagi orang tua yang menginginkan informasi lebih lanjut. Hal ini dinilai kurang efisien pada kondisi masyarakat yang semakin terbiasa menggunakan perangkat berbasis teknologi informasi dalam kesehariannya berupa smartphone dan komputer. Oleh karena itu, pada kegiatan Pengabdian kepada Masyarakat (PkM) ini dilakukan pelatihan kepada para guru di TKIT Buah Hati Kita Jember dalam memanfaatkan layanan Content Management System (CMS) yang disediakan oleh Google, yaitu Google Sites untuk membuat web profile TKIT. Melalui web profile tersebut, pihak TKIT secara mandiri dapat mempromosikan keunggulan sekolah, album kegiatan dan juga pengumuman penerimaan siswa baru dengan cara menuliskannya pada Google Sites. Selain itu, tim pelaksana pengabdian juga membantu mengintegrasikan web profile yang telah dibuat dengan nama domain dengan akhiran .sch.id agar web tersebut lebih mudah diakses dan diingat oleh masyarakat.
Analisis Sentimen Opini Publik Terhadap Undang-Undang Cipta Kerja pada Twitter Menggunakan Metode Naive Bayes Classifier Yanuar Nurdiansyah; Fatchur Rahman; Priza Pandunata
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol 3 (2021): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.328 KB) | DOI: 10.54706/senastindo.v3.2021.158

Abstract

Sentiment analysis or Opinion Mining is a way of solving a problem based on public opinion that is widely circulated on social media which is expressed in text form. Sentiment analysis is very helpful for the government / an agency in knowing public opinion about a policy that has just been issued without using conventional survey methods. The sentiment analysis carried out focuses on trending tweet topics on Twitter with trending topics on October 5 to 10 are #Omnibuslaw, #tolakruuciptakerja, #UUCiptaKerja, and #tolakomnibuslaw, and the trending topic on November 21 and 22 is "obl makmurkan buruh" . The sentiment analysis process is carried out after the data is obtained at the data crawling stage, followed by word cleaning in the preprocessing process, and word weighting with the TF-IDF algorithm. Sentiment analysis using the naive bayes classifier method aims to obtain a classification of public opinion on the job creation law on twitter. There are two classes in this study, there are positive and negative classes. The 2000 dataset consisting of 1400 tweets that have negative sentiments & 600 positive tweets used will be divided between training data and testing data with a ratio of 60%: 40%, 70%:30%, 80%:20%, and 90 %:10%. From the evaluation results on sentiment analysis regarding public opinion on the copyright law on Twitter, the highest accuracy value is 94% with training data used at 90%, testing data at 10%. In its implementation, the results of the sentiment test show that negative sentiment results are higher than positive sentiment.
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 SENTIMEN PROGRAM MERDEKA BELAJAR KAMPUS MERDEKA MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER: SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA USING NAÏVE BAYES CLASSIFIER ALGORITHM Pandunata, Priza; Ali, Saifur Rifqi; Nurdiansyah, Yanuar
Jurnal Sistem Informasi dan Bisnis Cerdas Vol. 16 No. 1 (2023): Februari 2023
Publisher : Program Studi Sistem Informasi, Fakultas Ilmu Komputer, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/sibc.v16i1.191

Abstract

Program Merdeka Belajar – Kampus Merdeka (MBKM) adalah program yang dicanangkan oleh pihak Menteri Pendidikan dan Kebudayaan (Mendikbud) yang bertujuan untuk meningkatkan kualitas mahasiswa serta diharapkan dapat membantu mahasiswa dalam menguasai berbagai bidang keilmuan sehingga siap untuk memasuki dunia kerja. Program MBKM sendiri secara resmi digelar untuk pertama kali pada tahun 2020 silam, tentu seiring berjalannya waktu masyarakat telah merasakan dampak dari program MBKM ini sehingga tak jarang banyak menuai pro dan kontra. Topik penelitian analisis sentimen terhadap program MBKM ini bertujuan untuk menggali informasi sentimen yang terkandung dalam setiap komentar yang diperoleh dari penghimpunan data komentar masyarakat pada media sosial twitter, dari hasil penghimpunan data tersebut tentunya tujuan utama yang ingin dicapai adalah untuk melihat respons masyarakat serta berharap dapat menjadi bahan evaluasi dari pihak pemangku kepentingan khususnya pihak mendikbud. Algoritma Naïve Bayes Classifier difungsikan sebagai sebuah metode yang digunakan dalam proses pengklasifikasian dataset yang berjumlah 2000 data twit. Pengklasifikasian data dilakukan menggunakan tiga skema pengujian dengan perbandingan data training dan data testing sebesar 70%:30%, 80%:20% dan 90%:10%. Adapun nilai akurasi terbaik yang diperoleh dalam pengujian klasifikasi pada penelitian ini menunjukkan nilai sebesar 70% di mana nilai tersebut diperoleh dari pengujian menggunakan perbandingan data sebesar 90%:10%.
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.
Comparison Analysis of Dijkstra and A-Star Algorithms in NPC (Non-Playable Character) Movement on a Single-Player Game: Case Study: Chaos Crossing Game Dhaifullah, Dany Zaky; Adiwijaya, Nelly Oktavia; Pandunata, Priza
IJAIT (International Journal of Applied Information Technology) Vol 08 No 01 (May 2024)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v8i1.6053

Abstract

Artificial intelligence in a game plays a vital role in enhancing the player's gaming experience, especially in single-player games. NPCs are the primary means of interaction in single-player games, assisting and guiding players like interactions with other players. Chaos Crossing requires pathfinding technology for optimal NPC movement, allowing them to navigate the environment grid-based while avoiding static obstacles. The Dijkstra algorithm and the A-Star algorithm need to be compared because, based on previous research, the Dijkstra algorithm has proven effective for calculating the shortest distance to the destination point in a static environment based on a two-dimensional grid with characters moving in it, as well as the A-Star algorithm can avoid a static environment based on grid and is used to determine the shortest distance to the destination point in the character's movement. This quantitative research aims to find a solution that optimizes NPC movement by testing and comparing Dijkstra's and A-Star's algorithms in a static environment grid based on the game Chaos Crossing. The test results and comparative analysis show that the A-Star algorithm performs a faster route search with an average value of 36.37 seconds than Dijkstra's algorithm with an average matter of 20.76 seconds and utilizes memory more efficiently with an average value of 20.19 MB than Dijkstra's algorithm with a value 22.17 MB on average. However, Dijkstra's algorithm produces a slightly shorter track distance, with an average value of 42.26 units, compared to the A-Star algorithm, with an average value of 42.39 units.
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.
Analyzing the Performance of Multi Seeds Smart Dryer (MSSD) with Low Cost and Low Energy Consumption (LCEC) Indarto, Indarto; Pandunata, Priza; Nazdirah, Rufiani; Bahariawan, Amal; Sujarwo, Mohamad Wawan; Rahayu, Chairiyah Umi
Journal of Applied Agricultural Science and Technology Vol. 8 No. 3 (2024): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v8i3.257

Abstract

Seeds drying technology is essential for ensuring the quality of agricultural products, particularly seeds. Although technological developments have resulted in more effective drying methods, challenges such as high energy consumption persist among small-scale farmers with limited resources. Therefore, this research aimed to analyze the performance of Multi Seeds Smart Dryer with Low Cost and Low Energy Consumption (MSSD-LCEC), a machine designed to solve the inadequacies of traditional seeds drying methods. The machine used low-energy components and environmentally friendly concepts to achieve sustainable seeds drying while remaining affordable for small-scale farmers. Using corn, peanuts, and soybeans as test materials, the performance of MSSD-LCEC was analyzed through the use of factors such as drying rate, energy consumption, temperature, humidity, and moisture content (MC). The results showed that the machine effectively dried seeds in acceptable MC levels, meeting quality standards for seeds certification. This research also discussed the economic benefits of MSSD-LCEC, highlighting the stable performance and efficient energy use. By optimizing the machine's operation and minimizing energy costs, small-scale farmers could enhance their profitability while contributing to environmental sustainability. In conclusion, further refinement of the control system had the potential to enhance both economic benefits and environmental sustainability in seeds processing applications.
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