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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Innovative: Journal Of Social Science Research VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Scientific Journal of Informatics
Rohana, Tatang
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PUBLIC SENTIMENT ANALYSIS ON ELECTRIC CARS USING MACHINE LEARNING ALGORITMS Damaiarta Tejayanda, Rigger; Prasetyo, Bayu; Faisal, Muhamad Agus; Abigael, Rakha; Rohana, Tatang; Sukmawati, Cici Emilia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2141

Abstract

The presence of electric vehicles has generated diverse opinions among the public, as widely discussed on social media. The lack of understanding about electric vehicle innovation can influence their perception. Issues such as infrastructure, high prices, pollution concerns, and adaptation to new technology present challenges for automotive companies in their innovation efforts. This study aims to analyze public sentiment towards electric vehicles through comments on the TikTok platform, which can serve as a reference for companies in evaluating and developing electric vehicle innovations. Six different classification algorithms were tested to determine the most effective and accurate one. The methods used include data collection of comments, pre-processing, data processing through stemming, tokenization, and stopwords removal techniques, as well as labeling and modeling stages. The results of the study show that Support Vector Machine are the most superior algorithms with the highest accuracy of 90%. This research provides new insights into public perception of electric cars and the effectiveness of various sentiment analysis algorithms in the context of social media.
CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM Hastari, Nazwa Putri; Rohana, Tatang; Masruriyah, Anis Fitri Nur; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2413

Abstract

The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.
CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS Fauzi, Farras Ahmad; Rohana, Tatang; Juwita, Ayu Ratna; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.3697

Abstract

Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values ​​of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agusti, Anggi Renata Agustin, Rachmayanti Tri Antoni, Alan Awal, Elsa Elvira Azhari, Febrian Akbar Azzahra, Fathimah Noer Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Bunga Tiara, Vira Butar Butar, Naomi Nova Meylica Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Faisal, Sultan Faisal, Sutan Farah Maulida, Oxana Fauzi Ahmad Muda Fauzi, Ahmad Fauzi, Farras Ahmad Fauzi, Rifqi Arul Fitri Nur Masruriyah, Anis Hakim, Mirwan Abdurrahman Hanny Hikmayanti Handayani Hartini, Dina Hastari, Nazwa Putri Indra, Jamaludin Irawan, Muhamad Anggi Jamal, Malikil Juwita, Ayu Ratna Kamilah, Nur Azizah Karyanto, Dony Dwi Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Laurentzia, Rini Beatrix Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Maulana, Moch Sigit Rizky Mudzakir, Tohirin Al Muhammad Khaerudin Munzi, Gugy Guztaman Niendha Biell Binna Novita, Hilda Yulia Nurlaelasari, Euis Nurlaila, Diah Nurmayanti, Trisya Nursyawalni, Reva NURUL AZIZAH Pratama, Adi Rizky Pratama, Irfan Nugraha Purnomo, Indarto Aditya Rahmat Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Naufal Cahya Ramadhan, Rizky Agung Ratna Juwita, Ayu Romadoni, Nurul Siregar, Amril Mutoi Sukmawati, Cici Emilia Syahril, Ade Tri Vicika, Vikha Wahiddin, Deden Winarni Yana Cahyana Yulia Novita, Hilda