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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Innovation Multidisipliner Research Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Irsyada, Amelia; Haerani, Elin; Irsyad, Muhammad; Wulandari, Fitri; Afriyanti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
Pemanfaatan Algoritma K-Means Dalam Menentukan Potensi Hasil Produksi Kelapa Sawit Wahyuni, Ayu Sri; Haerani, Elin; Budianita, Elvia; Afrianti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7226

Abstract

Considering the importance of oil palm cultivation now and in the future, as well as the increasing demand for palm oil by the world population, it is necessary to think about efforts to increase the quality and quantity of palm oil production appropriately in order to achieve the desired and achievable goals. Based on data on palm fruit production results from PT Salim Ivomas Pratama Tbk, it can be seen that fruit production varies in several places. The potential yield of oil palm fruit is based on the harvested area, actual production and year of planting. K-Means welding can help identify the potential of oil palm, with results that vary from day to day. This process allows locations with similar production patterns, which facilitates management decisions and production strategies. In this research, potential fruit planting areas were grouped using the K-Means algorithm. K-Means aims to facilitate the grouping of blocks with high and low fruit production. The data used is 180 data for the last 5 years, namely from 2018 to 2022, with the attributes Harvest Block, Area, Sheet Weight, and Product Realization or quantity. This research uses the help of Rapidminer and Google Colab software. The results of this research show that C1 (the highest) is 125 Harvest Block data in the sense that the first group is included in the good or high harvest yield category in 2018-2022, and C0 (the lowest) is 55 Harvest Block data in the sense that the second group is included low yield category 2018-2022.
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Afrianti, Liza Afriyanti, Liza Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Irsyada, Amelia Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Irsyad Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Rendi Ibnu Syahputra Sapitri, Janaria Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelfi Vitriani, Yelvi Wahyuni, Ayu Sri Yusra, Yusra zikrillah aulia, said rizki