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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Kreativitas PKM Jurnal Abdimas Mahakam Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Eduvest - Journal of Universal Studies Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Nasional Inovasi Gagasan Abdimas & Kuliah Kerja Nyata
Indra, Jamaludin
Universitas Buana Perjuangan Karawang
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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

 1 
Kajian Model Backpropagation dan Hybrid ANFIS Dalam Memprediksi Pertumbuhan Penduduk di Kabupaten Karawang Tatang Rohana; Jamaludin Indra; Gugy Guztaman Munzi
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2547

Abstract

Population growth rate prediction is a process of estimating the population in the future. Predictions are made so that the government can prepare strategic steps in anticipating the negative impact of an uncontrolled population increase. The research data is the population of Karawang Regency from 2011 to 2020. Backpropagation and Hybrid ANFIS are the models used in this study. The purpose of this study was to determine the RMSE value and scatter data formed from the results of the ANFIS Backpropagation and Hybrid training models in predicting population growth rates in Karawang Regency. In addition, this study is intended to determine the level of accuracy of the two models. The research step begins with research data validation, preprocessing, training and testing, as well as accuracy testing. Accuracy testing uses the Mean Absolute Percentage Error (MAPE) method. Backpropagation and Hybrid models in predicting the rate of population growth have worked well. This can be seen from the training results of the two models. Backpropagation model has the best RMSE of 0.0328 and Hybrid has the best RMSE of 0.021884. The results of the analysis of the accuracy of predicting population growth rates for 2019 and 2020 that have been carried out, both models have a good level of accuracy. Backpropagation has an average accuracy rate of 84.76%, while the Hybrid model has an average accuracy rate of 93.71%. Based on the results of accuracy testing, the Hybrid model has a better level of accuracy than the Backpropagation model.
Analisis Prediksi Banjir di Indonesia Menggunakan Algoritma Support Vector Machine dan Random Forest Purnomo, Indarto Aditya; Indra, Jamaludin; Awal, Elsa Elvira; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5958

Abstract

Natural disasters frequently occur in Indonesia, such as floods, landslides, and volcanic eruptions. Geological factors, such as the convergence of four major tectonic plates, make Indonesia vulnerable to natural disasters. Statistical data from the National Disaster Management Agency show an increase in flood occurrences each year, peaking in 2021 with 1,794 incidents. Early anticipation is necessary to minimize the impact of natural disasters, and predictive patterns are becoming new knowledge for preventing and managing these disasters. This study applies the Support Vector Machine and Random Forest algorithms. The results of this study predict that the largest number of floods from 2024 to 2026 in Indonesia will occur in Aceh with 240 floods, North Sumatra with 215 floods, West Java with 210 floods, and Central Java with 160 floods. The best algorithm comparison results were achieved with Random Forest, which had an accuracy of 99.6% and an average RMSE value of 3.834.
Analisis Sentimen Pemboikotan Produk dengan Pendekatan Algoritma Naïve Bayes Media Sosial X Rifaldi, Rizky; Indra, Jamaludin; Pratama, Adi Rizky; Juwita, Ayu Ratna
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5420

Abstract

This research aims to analyze sentiment regarding the problem of product boycotting by the public using the Naive Bayes algorithm. 1426 data were collected from social media x to study consumer behavior towards certain products. Through the application of the Naive Bayes algorithm, sentiment analysis was carried out to identify patterns in consumer opinions regarding boycotting the products studied. Experimental results show that the Naive Bayes algorithm succeeded in achieving 81% accuracy in classifying sentiment towards products. This shows the algorithm's ability to analyze consumer sentiment effectively, which can provide valuable insights for companies in understanding public perception and managing the reputation of their products. The practical implication of this research is the importance of utilizing sentiment analysis techniques in marketing strategy and brand management to increase product competitiveness in a competitive market.
Co-Authors AA Sudharmawan, AA Abdul Gapur Achmad, Syifa Latifah Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Afifur Rahman Ahmad Fauzi Ahmad Fauzi Ahmad Rahman Al Fathir Rizal Januar Alif Kirana Amansyah, Ilham Anton Romadoni Junior Apriade Voutama April Hananto Ardiansyah, Fikri Arif Nurman Arip Solehudin Aris Martin Kobar Arum Puspita Lestari, Santi Asep Jamaludin Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Juwita Azis Saputra Azzahra, Wava Lativa Baihaqi, Kiki Ahmad Cici Emilia Sukmawati Dadang Yusup Deden Wahiddin Deny Maulana Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Eko Pramono Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Fauzi, Ahmad Firdaus, Thoriq Janati Firmansyah Maulana Fitri Nur Masruriyah, Anis Garno . Garno, Garno Gugy Guztaman Munzi Hananto, Agustia Hanny Hikmayanti Handayani Hanung Pangestu Rahman Hilda Fitriana Dewi Hilda Novita Hilda Yulia Novita Holila, Holila Irma Putri Rahayu Juwita, Ayu Ratna Karyanto, Dony Dwi Khoirull Munazzal Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M Andrian Agustyan Maharina, Maharina Maliah Andriyani Mudzakir, Tohirin Al Muhammad Arya Suhendi Muhammad Cesar Afriansyah Arief Muhammad Deden Miftah Fauzi Muhammad Imam Naufal Muhammad Khoiruddin Harahap Muhammad Raja Nurhusen Muhammad Romadhon Nazori AZ Novalia, Elfina Nugraha, Najmi Cahaya Nurdin, Cherry Januar Nurlaelasari, Euis Nursyawalni, Reva Paryono, Tukino Pratama, Adi Rizky Purnama, Ariya Purnomo, Indarto Aditya Rahmat Hidayat Rahmat Rahmat Rahmat Rahmat Ratna Juwita, Ayu Rifaldi, Rizky Rija Nur Hijriyya Rissa Ilmia Agustin Rizki, Lutfi Trisandi Robinson Nababan Rohana, Tatang Romlah Saefulloh, Nandang Sandi Susanto Santi Lestari Sihabudin Sihabudin, Sihabudin Siregar, Amril Mutoi Siti Robiah Suparno Sutan Faisal Syahrul Azis Tatang Rohana Tatang Rohana Tia Astiyah Hasan Tohirin Al Mudzakir Tohirin Mudzakir Toif Muhayat Tri Vicika, Vikha Ulfa Amelia Vikha Tri Vicika Wahiddin, Deden Wildan Amin Wiharja Yana Cahyana Yogi Firman Alfiansyah