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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi Jurnal Sistem Komputer Proceedings of KNASTIK Journal The Winners Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) CogITo Smart Journal INOVTEK Polbeng - Seri Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Jurnal ULTIMA InfoSys MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) International Journal of Social Science Indexia J-SAKTI (Jurnal Sains Komputer dan Informatika) KINGDOM : Jurnal Teologi dan Pendidikan Agama Kristen Jurnal Minfo Polgan (JMP) Jurnal Teknologi Sistem Informasi Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies Jurnal INFOTEL Journal of Technology Informatics and Engineering Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Scientific Journal of Informatics CSRID Society Jurnal DIMASTIK Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Hendry
Universitas Kristen Satya Wacana
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
IMPLEMENTATION OF GENERATIVE ADVERSARIAL NETWORKS FOR CREATING DIGITAL ARTWORK IN THE FORM OF ABSTRACT IMAGES eric secada purba; Hendry
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.262

Abstract

Abstract painting always has its own place for the fans, the irregular shape in it, and the emotions depicted in the painting, make many people amazed to see it. The success of this abstract image sparked the idea of ​​being able to create an abstract image using Deep Learning Technology. Generative Adversarial Networks (GANs) is one of the Deep Learning technologies that can create it. With the GANs method which has Generator and Discriminator functions in it, it is possible for someone to be able to create it. The generator functions to generate new data through training the data(train), and the Discriminator functions to determine whether the new data is fake or not data through training (train) comparing the generator results with the original data. These two functions are used to create abstract images. Abstract images were obtained through training in 1369 paintings of nature, landscapes, and flowers. The images are trained by comparing the number of epochs used and the results of the abstract images generated from each epoch. The epoch will be divided into three parts, namely the first training using 10 epochs, the second training using 100 epochs, and the third training using 1000 epochs. In this journal, we will compare the results of the three trainings and reach a conclusion which training produces the best abstract image according to the author. From the training, 1000 epoch training was obtained which produces good abstract images.
COMPARISON OF PREDICTION ANALYSIS OF GOFOOD SERVICE USERS USING THE KNN & NAIVE BAYES ALGORITHM WITH RAPIDMINER SOFTWARE Agista Nindy Yuliarina; Hendry Hendry
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.2022.3.4.294

Abstract

GoFood is a service provider that has a very important role in human life, especially in this growing era. Currently, many service providers are competing to meet the needs of users, including GoFood. However, not all service providers can meet and know the needs needed by users, because they focus on the services offered and only the quality of services provided. Therefore, survey analysis is needed to obtain customer satisfaction data that will be used to satisfy GoFood service users. The classification method uses the KNN and Naive Bayes algorithms, which are good algorithms for testing 1,000 records of GoFood user data that have been obtained previously. The test results using Cross Validation and T-Test show that the KNN algorithm is the best algorithm with 98.80% Accuracy and 100% Recall, while Naive Bayes obtains 94.10% Accuracy and 94.43% Recall.
DATA MINING TECHNIQUE USING NAÏVE BAYES ALGORITHM TO PREDICT SHOPEE CONSUMER SATISFACTION AMONG MILLENNIAL GENERATION Margaretha Intan Pratiwi Hant; Hendry Hendry
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.2022.3.4.295

Abstract

Shopee is one of the largest e-commerce platforms currently being used by Millennials. The use of Shopee itself makes it very easy for consumers to process transactions. Shopee itself is committed to maintaining and improving customer satisfaction so they don't switch to other competitors. However, it is undeniable that there are still many cases that can harm consumers when using the platform. With the cases that occur, it is very possible that there will be a big influence on the level of consumer satisfaction on the platform. Consumers will feel satisfied when the product or service used can meet consumer expectations. This study was made with the aim of predicting the level of consumer satisfaction of Shopee Indonesia among the Millennial Generation. This study applies data mining using the Naive Bayes Algorithm. The Naive Bayes algorithm itself is a simple probability classification that can calculate all possibilities by combining a number of combinations and the frequency of a value from the database obtained. The attributes used in conducting this research include Name, Gender, Age, Price, Performance and Efficiency, Fulfillment, Reliability, Control and Security, Responsiveness, Compensation, Contact, and Description of Satisfaction Value. In this study, the results obtained from several input attributes that create a causal relationship when classifying satisfied and dissatisfied consumers. The results obtained can provide benefits for the Shopee company in increasing customer satisfaction. After carrying out the testing process, it can be concluded that the Naive Bayes Algorithm is an algorithm that is suitable for use in the classification process for measuring Shopee Indonesia's consumer satisfaction level among the Millennial Generation, with an accuracy rate of 89.65%.
PREDICTION OF BABY BIRTH RATE USING NAÏVE BAYES CLASSIFICATION ALGORITHM IN RANDAU VILLAGE Magda Kitty Hartono; Hendry Hendry
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.2022.3.4.302

Abstract

The birth rate is one of the factors increasing the rate of population growth. Birth or fertility can affect the population, getting more lower of birth rate in an area, the higher the life expectancy in that area. The number of births in Randau Jekak Village is increasing every year. The Naïve Bayes algorithm can be used to predict the future births rate because this algorithm is a simple algorithm and uses a lot of data as information in collecting data groups, and with data mining techniques it can see the predictive pattern of each variable and test. The testing data and the training data are expected to help the Village Institution or Office in Randau Jekak to suppressing the rate of population growth which increases every year. The results of this study can be concluded that the Naïve Bayes Algorithm is suitable for predicting the birth rate of babies in Randau Jekak Village with the classification technique, the birth rate in Randau Jekak Village in 2021 is High. The results of this data will be very useful for the Randau Jekak Village office in suppressing the rate of population growth in the coming year.
NAÏVE BAYES ALGORITHM CLASSIFICATION IN SENTIMENT ANALYSIS COVID-19 WIKIPEDIA Jessica Margaret Br Sembiring; Hendry h
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.2022.3.4.311

Abstract

In recent years during the pandemic Wikipedia created more than 5,200 new pages regarding COVID-19 cases, with an accumulation of more than 400 million pages by mid-June 2020. Wikipedia is one of the most popular websites of our time. In this case Wikipedia always integrates new and fast research. To get an opinion from wikipedia text, sentiment analysis is needed. The analysis was conducted using a classification containing public sentiment regarding the issue of COVID-19 in Indonesia. The classification method used in this study is naive bayes classifier (NBC). Naïve Bayes Classifier is a popular method of solving classification problems. This classification method is often used in sentiment analysis in both precision and data computing. This wikipedia classification is obtained from each label, namely positive, negative and neutral classes. The results of tests conducted in the classification of naive bayes get a high accuracy of 81%.
Co-Authors Ade Iriani Adenia Kusuma Dayanthi Adriyanto Juliastomo Gundo Agista Nindy Yuliarina Agus Susanto Amanda, M. F. Anton Hermawan April Lia Hananto Atik Setyanti, Angela Aviv Yuniar Rahman Baihaqi, Kiki Ahmad Benedictus Lanang Ido Hernanto Christine Dewi Daniel, Benny Danny Manongga Dewasasmita, Elsha Yuandini Dewi Puspitasari Eka, Muhammad Eko Sediyono eric secada purba Erick Alfons Lisangan Erits Talapessy Erwien Christianto Ester Caroline Dwi Wijaya Wijaya Fauzi Ahmad Muda Fenny Fenny Franly Salmon Pattiiha Fredryc Joshua Pa'o Gunawan, Ricardho Handoko Handoko Handoko, Andrew C Hanita Yulia Hendra Waskita Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Indriaty, Novica Irwan Sembiring Ismael Ismael Ivan Sukma Hanindria Iwan Setiawan Iwan Setyawan Jessica Margaret Br Sembiring Joko Siswanto Julians, Adhe Ronny Kesumawati, Ramadini Kho, Delvian Christoper Krismiyati Kristoko Dwi Hartomo Kurnia, Sri Kurniawan Teguh Martono Leni Marlina Liawatimena, S. Lidia Gayatri Madawara, Herdin Yohnes Mado, Priscianus Mikael Kia Magda Kitty Hartono Mahulete, Ebenhaezer Yohanes Abdeel Manongga, Daniel Margaretha Intan Pratiwi Hant Martaliana Putri Agustina Merryana Lestari Muhammad Khahfi Zuhanda Muhammad Rizky Pribadi Nadia Sofie Soraya Nalbraint Wattimena Nansy Stephanie Mongi Novrina, Putri Dwi Nugraha, Febrina Tesalonika Panja, Eben Paryono, Tukino Pratama Siregar, Hari Nanda Pratama, Arya Damar Purnomo, Hendryanto Dwi Raden Mohamad Herdian Bhakti Ramos Somya Ravensca Matatula Ravensca Matatula Richard V. Llewelyn Rizal, Chairul Robertus Bagaskara Radite Putra Ronny Julians, Adhe Rung Ching Chen Santoso, Joseph Teguh Saputri, Adelliya Dewi Septhiani, Angeline Sholikin, Muhammad Sjukun Suharyadi Suherman, Suherman Sukiman Sukiman Supiyandi Supiyandi Susanta, Vonny A. Sutarto Wijono Suvirocana, Suvirocana Syefudin Syefudin Tarigan, Aldi Ekin Arapenta Teddy Marcus Zakaria Theopillus J. H. Wellem Tukino, Tukino Uly, Novem Untung Rahardja Vanisa Meifari Wahyuningsih, Novia Wibowo, Kurniawan Indra Widi, Anugerah Wijaya, Elyzabeth Winny purbaratri Yandra Rivaldo Yessica Nataliani Zulham Zulham