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Minarni
Universitas Darwan Ali
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Evaluasi Performa Algoritma Naïve Bayes Dalam Mengklasifikasi Penerima Bantuan Pangan Non Tunai Mohammad Mastur Alfitri; Nurahman Nurahman; Minarni Minarni; Depi Rusda
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6151

Abstract

The improvement of the standard of living of the community in Bapinang Hulu Village is carried out through various social assistance programs. However, the realization of the implementation of social assistance programs did not go smoothly. Social jealousy often occurs among the community during the distribution of social assistance. The distribution of assistance is carried out based on the assessment of the village officials and the Village Consultative Body, which is then validated by the heads of RT and RW. The quota provided by the government is often not in accordance with the actual number of eligible recipients in the village. Another difficulty is determining the criteria or attributes used for the selection of Non-Cash Food Assistance recipients. This study aims to obtain a classification model from which the classification pattern can be applied to the population data of Bapinang Hulu Village for the selection of social assistance recipients. To solve this problem, the classification method is applied using the Naive Bayes Algorithm. The research results show that the performance of the Naive Bayes algorithm model before feature selection had the highest accuracy in the 8th test with an accuracy of 89.80%. Meanwhile, after feature selection, the highest accuracy was found in the 3rd test with an accuracy of 88.37%. The feature selection using the Information Gain algorithm reduced the number of attributes from 16 to 6. Therefore, it is known that the highest accuracy is obtained before feature selection, but in selecting social assistance recipients, more criteria need to be applied, which is time-consuming. Meanwhile, after feature selection, only 6 criteria are used to determine social assistance recipients.
MEMPREDIKSI JUMLAH PRODUKSI KEDAI KOPI KAGANANGAN MENGGUNAKAN METODE FUZZY TSUKAMOTO Herlina Kristin Rahel; Hana Silviana Sutanto; Imelda Susilawaty; Minarni
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01 APRIL.28

Abstract

Kaganangan Coffee Shop is a trading business whose business line is a shop. Because the high level of competition makes more and more similar businesses pop up, Kedai Kopi Kaganangan is required to design a strategy that can provide more value to consumers at a more affordable cost. The habit of drinking coffee among Indonesian people has become a lifestyle in society. From the past until now, this coffee business continues to be enjoyed by teenagers to parents. One of the coffees that have been popular with coffee lovers in the city of Sampit is the Kaganangan Coffee Shop. This coffee is often visited by coffee lovers when teenagers come home from school and adults during recess or go home from the office which is located on H.M Arsyad Street, Sampit. One of these coffee shops makes it difficult for business owners to determine how much coffee production they must have to meet sales demand. To predict coffee production for one week, the data used may include, coffee sales data during the previous period can be used to predict coffee demand in the coming week. This data can be obtained from in-store sales records or consumer surveys, current coffee inventory data can be used to predict how much coffee to produce in the coming week. These data can be combined and analyzed using the Tsukamoto fuzzy method, to make predictions of coffee production for one week. In addition, external factors such as weather, raw material prices, and market conditions also need to be considered in making predictions. The purpose of this research is to overcome the problem with the Tsukamoto fuzzy method used by the author, and the results obtained are said to produce 821 Kaganangan Coffee. Therefore, the results of this study can be used as a consideration in determining the average production amount of Kaganangan Coffee.
Co-Authors Agung Purwanto Agung Purwanto Andy Ismail Bayu Ramadandy Depi Rusda Dody Erlangga Eki Juliana Elika Thea Kirana Fery Setiawanto Hana Silviana Sutanto Hasan, Patmawati Herlina Kristin Rahel Imam Zasuli Imelda Susilawaty Juanda Prastiya Risma Kaurie, Fazriyanor Linda Norhan Mohammad Mastur Alfitri Mustaqiem, Mustaqiem Nadi Herlambang Nadia Sari Nadia Sari Nadia Sari Nindi Ernawati Nindi Ernawati Nindi Ernawati Nurahman Nurani Aisiyah Tanjung Prasetyaningrum, Eka Pratama, Nanda Putra Bayu Riko Hidayat Rizal Adi Saputra Rizky Amalia Sherly Mudrika Bahri Sigit Sigit Susi Santya Tedi Kustandi Wulandari, Antika Yunita, Selviana Zain Khairullah