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All Journal Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Informatics for Educators and Professional : Journal of Informatics Jurnal Teknik Informatika STMIK Antar Bangsa Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Mantik Akasia: Artikel Ilmiah Sistem Informasi Akuntansi JUSTIAN - Jurnal Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Insan Journal of Accounting Information System SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Lisda Widiastuti
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Other

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Journal : Jurnal Mantik

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Classification of Citizens with Low Economic Level Using Naive Bayes Classification Method Artika Surniandari; Hilda Rachmi; Lisda Widiastuti
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The effort to reduce poverty, it is hoped that the economic growth of a region can be spread evenly. To equalize the economic level of its citizens, local governments provide many opportunities for their citizens to get help or subsidies and even help open businesses so that the welfare of their citizens can be improved. The availability of accurate and sustainable citizens' economic status data is one of the important instruments for evaluating government policies in alleviating poverty by distributing targeted aid. Therefore, this study will conduct a classification based on data from citizens with low economic levels obtained from Pasar Babakan Sub-district, Bogor. The data used in this study is the data of underprivileged residents who are in Pasar Babakan Sub-district Bogor using data mining techniques. The training data are taken from 214 citizens that has a category of underprivileged citizens with attributes used in the classification of income per day, occupation and number of dependents. The test results using the Naive Bayes Classifier method produce 100% accuracy including the excellent category with 100% precision and 100% recall.
Topsis Method To Determine Recipients Of Covid 19 Social Assistance Funds Artika Surniandari; Hilda Rachmi; Lisda Widiastuti
Jurnal Mantik Vol. 5 No. 1 (2021): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Pandemic and social restrictions are inseparable things, with the existence of these restrictions, several sectors of life have changed, including the economic sector, the number of unemployed due to layoffs has increased because a number of companies have difficulty covering operational costs. The central government finally took a policy to provide social assistance to residents who met the criteria of being poor and the determination was left to the local government. The method used to assist decision making to determine suitable beneficiaries is to use TOPSIS. With the TOPSIS method, it is hoped that the assessment will be more precise because it is based on predetermined criteria and weight values ??so that more accurate results will be obtained. In this research, the results obtained from ranking to determine the priority for citizens to get Covid 19 social assistance and the results of residents who do not earn because they do not work while having dependents of more than 5 people who are more than 30 years old get the top priority
Co-Authors Ahmad Al Kaafi Amanah, Delia Arsiadi, Febri Artika Surniandari Dini Nurlaela Ela Nurelasari Elvira Febriliani Ernawati, Siti Farid Shofli Wahnin Fauziah, Alisha Nur Febri Arsiadi Felisha, Angela Putri Hapsari, Amelia Dwi Haryani Hilda Rachmi Lila Dini Utami Lisda Mahmudah, Sintia Muhammad Habili Firdaus Nurfadillah, Siti Dalfa Rifki Azmi - UBSI Siti Ernawati Sri Wasiyanti, Sri Suparni Suparni Wasiyanti , Sri Zakia, Ade Ucu Tri