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All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak
Ryan Hamonangan
STMIK IKMI Cirebon
Computer Science & IT

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Implementasi Algoritma Decision Tree Dalam Klasifikasi Kompetensi Siswa Hilman Rifa'i; Ryan Hamonangan; Dian Ade Kurnia; Kaslani; Mulyawan
KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer Vol. 6 No. 1 (2022): KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer
Publisher : Puslitbang Kopertip Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32485/kopertip.v6i1.131

Abstract

Kompetensi adalah suatu kemampuan atau kecakapan yang dimiliki oleh seseorang dalam melaksanakan suatu pekerjaan atau tugas di bidang tertentu, sesuai dengan jabatan yang disandangnya. Pendapat lain mengatakan arti kompentesi adalah suatu keterampilan, pengetahuan, sikap dasar, dan nilai yang terdapat dalam diri seseorang yang tercermin dari kemampuan berpikir dan bertindak secara konsisten. Dengan kata lain, kompetensi tidak hanya tentang pengetahuan atau kemampuan seseorang, namun kemauan melakukan apa yang diketahui sehingga menghasilkan manfaat. Dalam penelitian ini difokuskan pada klasifikasi siswa berkaitan dengan kompetensi yang dimiliki, pendekatan penelitian ini menggunakan algoritma decission tree, dengan tujuan mendapatkan pola rekomendasi kompeten dan tidak kompeten. Berdasarkan hasil penelitian menjelaskan bahwa akurasi yang didapat yaitu sebesar 76,96 %
Analisis Keadaan Stunting pada Kelompok Balita di Kecamatan Tukdana dengan Pendekatan Decision Trees Asep Budiyanto; Dodi Solihudin; Ryan Hamonangan; Cep Lukman Rohmat; Ade Rizki Rinaldi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10230

Abstract

The impact of stunting on babies is an important parameter for assessing the health and welfare of children in an area. Stunting, often triggered by demographic and health factors, has serious implications for children's physical and cognitive growth. This research aims to understand the impact of demographic and health factors on stunting in children in Tukdana District, Indramayu Regency. Through data analysis, factors such as maternal age, access to clean water, sanitation facilities, and baby weight and length status were identified as significant contributors to stunting. The Decision Trees method was used to identify factors that play a role in stunting in babies, with an accuracy rate of 95.43%. The implications of this research include planning more effective interventions to deal with stunting, both in Tukdana District and in similar areas in Indonesia. Even though the majority of babies in Tukdana District have good nutritional status, further monitoring and prevention efforts are still needed to ensure optimal nutritional well-being for them. In conclusion, this research highlights the importance of identifying factors that cause stunting in infants in Tukdana District, as a basis for planning more effective interventions.
Klasifikasi Algoritma KNN dalam menentukan Penerima Bantuan Langsung Tunai Ryan Hamonangan; Risa Komala Sari; Saeful Anwar; Tuti Hartati
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10298

Abstract

Poverty is a condition where an individual or a group experiences economic incapacity to meet their basic needs, and this study involves broader aspects than just expenditures. The focus of this research is on Ciherang and Ciaro Villages, located in the Nagreg Subdistrict, Bandung Regency, which are areas with significant levels of poverty. This research responds to poverty issues by utilizing the K-Nearest Neighbor (KNN) Algorithm in the data mining classification process. KNN considers the proximity of a new object to its nearest neighbors, and as a supervised learning algorithm, KNN requires target information or classes in the analyzed dataset. The aim of this research is to provide information regarding the classification of recipients of Direct Cash Assistance (BLT) in Ciherang and Ciaro Villages. The research results present data on criteria for determining whether recipients are considered eligible or ineligible for BLT, with an accuracy rate reaching 81.56%. Additionally, the performance of this algorithm is demonstrated through true recall values for both eligible and ineligible recipients, with recall for true ineligible recipients at 88.43%, recall for true eligible recipients at 74.80%, precision for eligible recipients at 86.79%, and precision for ineligible recipients at 77.54%. These findings provide a basis for more accurate decision-making in determining BLT recipients in both villages. This can contribute to the design of more targeted and effective social policies in reducing the impact of poverty, providing in-depth insights into the characteristics of BLT recipients, and demonstrating the relevance and efficiency of the KNN algorithm in addressing complex social issues.
Co-Authors Ade Rizki Rinaldi Asep Budiyanto Cep Lukman Rohmat Dian Ade Kurnia Dodi Solihudin Hilman Rifa'i Kaslani Mulyawan Risa Komala Sari Saeful Anwar Tuti Hartati