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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EKSAKTA: Journal of Sciences and Data Analysis Katalogis Jurnal Raushan Fikr International Journal of Advances in Intelligent Informatics Al-Ahwal: Jurnal Hukum Keluarga Islam Prosiding Seminar Nasional INDOCOMPAC Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum JOIN (Jurnal Online Informatika) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Riset Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Dialog Jurnal Berdaya Mandiri Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Enthusiastic : International Journal of Applied Statistics and Data Science MAQASIDI Jurnal Ilmiah Mahasiswa Raushan Fikr Bulletin of Applied Mathematics and Mathematics Education Bulletin of Social Informatics Theory and Application Al-Hukama: The Indonesian Journal of Islamic Family Law Jurnal Akuakultur SEBATIN PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Muhammad Muhajir, Muhammad
Department Of Statistics, Universitas Islam Indonesia
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Journal : Jurnal Riset Informatika

 1 
ANALYSIS CLASSIFICATION SENTIMENT OF THE LARGE PRIEST OF FPI’S RETURN USING SVM CLASSIFICATION WITH OVERSAMPLING METHOD Zetta Nillawati Reyka Putri; Muhammad Muhajir
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1061.096 KB) | DOI: 10.34288/jri.v4i1.132

Abstract

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.
ANALYSIS OF DYNAMIC TIME WARPING IN THE DEVELOPMENT OF GROSS REGIONAL DOMESTIC PRODUCT YOGYAKARTA Inggrid Septa Narendra; Muhammad Muhajir
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (880.11 KB) | DOI: 10.34288/jri.v4i4.175

Abstract

Poverty in Indonesia has become a common thing that is still difficult to handle due to the presence of the covid virus outbreak attacks are causing the inability to buy and sell transactions, export and import goods and services, then the level of inequality increases. The tool measures the inequality level in an area seen from the Gini Ratio value. The Gini Ratio notes that the DI Yogyakarta province had the highest index value in Indonesia of 0,437 in September 2020. So this study aims to minimize the inequality in the DI Yogyakarta province by using the clustering method and Dynamic Location Quotient (DLQ). The clustering method with a hierarchical algorithm using the Dynamic Time Warping (DTW) distance and the DLQ method to predict regional economic sectors. Based on the result of the clustering analysis, there were 2 clusters, and the DLQ analysis obtained as many as 11 essential and 6 NPN-base sectors. Cluster 1 has 10 GRDP sectors with two industries that will become non-base sectors in the future, while cluster 2 has 7 GRDP sectors with three sectors it will become base sectors in the future.
Co-Authors Abdullah, Ahmad Hussain Aditya Rizq Herlandy Karjawan Akmalia Fitri Mafaza Alhaidar, Nihrul Bahi Ali, Asriyanti Alvita, Salma Annisa Ayunda Permata Sari Ayu, Devina Gilar Baehaqi Berky Rian Efanna Cahyani, Amalia Rizki Canas Rodrigues, Paulo Danardono Danardono Danardono, Danardono Dedi Rosadi Devi Andrian Fatahillah, Zahrul Guritno, Muhammad Wahyu Suryo Gusmayyeni, Gusmayyeni Hasibuan, Rizky Ardiansyah Henni Syawal Hermansah Hermansah Hermansah Iesje Lukistyowati Inggrid Septa Narendra Jaka Nugraha Julia Widiastuti Kamila, Juanita Jihan Makhtum, Ahmad Rohiqim Muhaimin Muhaimin Muhammad Fadli Kamil Mustain, Riefyal Arshyza Novendri Isra Asriny Nurirwan Saputra, Nurirwan Nurkamiden, Sukrin Pandri Ferdias Pandri Ferdias, Pandri Pratiwi, Lathifah Aliya Primananda, Dhea Laksmita Arsya Puspita, Ersa Riga Qurratul Uyun, Qurratul Rachmad Febrian Rafendra Agustianda Putra Rahmadi Yotenka Rahman, Iqbal Fathur Rahmatika, Tusriana Riefyal Arshyza Mustain Rohmatul Fajriyah Rosadi , Dedi Sari, Redita Anggita Syamsud Dhuha Uswatun Hasanah Widiastuti, Julia Wulandari, Kanth Yayi Diyah Palupi Yotenka, Rahmadi Zetta Nillawati Reyka Putri