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All Journal Jurnal Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Responsif : Riset Sains dan Informatika Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa Indonesian Journal of Networking and Security - IJNS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi J-Intech (Journal of Information and Technology) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY JURSIMA Sinergi: Jurnal Pengabdian Kepada Masyarakat Journal of Accounting Information System Bulletin of Informatics and Data Science Jurnal Ilmiah Manajemen Ekonomi Dan Akuntansi (JIMEA) Jurnal Sistem Informasi dan Manajemen Media Teknologi dan Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Riska Aryanti
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : Journal of Informatics Management and Information Technology

 1 
Analisis Sentimen Pengguna Terhadap Aplikasi Indodana Di Google Play Store Menggunakan Metode Naive Bayes Classifier Rifqi Rizaldi; Aryanti, Riska
Journal of Informatics Management and Information Technology Vol. 4 No. 3 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i3.400

Abstract

This research aims to evaluate user responses to the Indodana: Paylater & Pinjaman application through sentiment analysis using the naive bayes algorithm. Online lending apps such as Indodana have changed the way individuals access finance by providing a quick and easy process. However, the user's decision to choose a legal app and pay attention to the transparency of fees and loan terms is crucial. With more than ten million downloads and two million reviews, it is important to understand user sentiment so that developers can improve services and maintain public trust. A sentiment analysis method using multinominal naive bayes was used with two labelling approaches inset lexicon and rating. The evaluation was conducted on 500 Indodana: Paylater & Pinjaman reviews, dividing the data into training and testing and using TF-IDF features. The results show that inset lexicon labelling achieved 86% accuracy, whereas rating-based labelling achieved 87% accuracy. These results provide an in-depth view of user responses, aiding in the identification of factors that influence positive or negative perceptions of the app. As such, this research is important for guiding the development of safe, reliable, and compliant online lending applications, as well as for improving overall user satisfaction
Optimisasi Model Deep Learning untuk Deteksi Penyakit Daun Tebu dengan Fine-Tuning MobileNetV2 Aryanti, Riska; Agustiani, Sarifah; Wildah, Siti Khotimatul; Arifin, Yosep Tajul; Marlina, Siti; Misriati, Titik
Journal of Informatics Management and Information Technology Vol. 4 No. 4 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i4.411

Abstract

Sugarcane leaf diseases are a serious threat in sugarcane farming because they can significantly reduce productivity and can cause major losses in yields if not detected early. Therefore, fast and accurate disease management is needed to prevent further losses. This study aims to develop a deep learning model based on MobileNetV2 with fine-tuning techniques to effectively detect sugarcane leaf diseases. Fine-tuning is a method used to adjust the parameters of a pre-trained model on a more specific target dataset. The dataset contains images of sugarcane leaves that have been classified per class based on the type of disease. In this study, fine-tuning was performed on the MobileNetV2 architecture that had been previously trained using the sugarcane leaf dataset. The fine-tuning process was carried out by rearranging the top few layers of MobileNetV2 and adding a special classification layer to predict the class of sugarcane leaf diseases. The model was trained through two stages: initial training to obtain a baseline performance and fine-tuning by opening several layers of MobileNetV2. In the initial evaluation, the model achieved a validation accuracy of 93.12%. After fine-tuning, the accuracy increased to 95.01%, indicating that this technique was able to significantly improve disease detection capabilities. The results of this study provide important contributions in the field of agriculture, especially in supporting the sustainability of sugarcane production through artificial intelligence-based technology. The implementation of the proposed model is expected to help farmers detect diseases more quickly and take timely preventive measures, thereby reducing losses.
Co-Authors Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andreas Roy Prasetya Ari Sulistiyawati Arifin, Yosep Tajul Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Cindy Sri Wahyuni Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Deni Gunawan Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Fachri, Muhamad Haliza Ramadhanti, Pristya Harefa, Kristine Haryani Hasan, Fuad Nur Herdian Pratama I Gede Iwan Sudipa Irfan Ridwan Jananto Watori KOMALASARI, YULI Masjuwita Aulia Munthe Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Nova Damai Yanti Bancin Oktaviyani Oktaviyani Oprasto, Raditya Rimbawan Pasaribu, A. Ferico Octaviansyah Perani Rosyani Permana, Rifky Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi Rahmat Hidayat Rahmat Hidayat Ramadhani, Arya Richardus Eko Indrajit Rifky Permana Rifqi Rizaldi Rina Martenia Rizqi Nur Esmeralda Rosiun Universitas Bina Sarana Informatika Roy Prasetya, Andreas Royadi - Royadi Royadi Royadi, Royadi Salman Alfarizi SALMAN ALFARIZI Samudi Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina, Siti siti rodiah Sopiyan Dalis Sumanto, Sumanto Titik Misriati tri wahyuni Tri Wahyuni Ulum, Faruk Walim Walim Wang, Junhai Yanto, Andika Bayu Hasta Yarimani Laia