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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI
Shinta Puspasari
Universitas Indo Global Mandiri, Palembang
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Prediksi Angka Kemiskinan Desa Kemang Bejalu Menggunakan Metode Naive Bayes Prediksi Angka Kemiskinan Desa Kemang Bejalu Menggunakan Metode Naive Bayes Arda Damayanti; Shinta Puspasari; Nazori Suhandi
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 4 No. 3 (2024): RESOLUSI January 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/resolusi.v4i3.1498

Abstract

Poverty is one of the problems faced by all countries, especially developing countries such as Indonesia. To find out the extent of poverty in Kemang Bejalu Village, research must be carried out to determine the poverty rate in Kemang Bejalu Village using the Naive Bayes method. The Naive Bayes method is used to classify data and calculate the probability of poverty based on certain factors. This study aims to determine whether the accuracy of the results of the Naive Bayes method can be used in predicting poverty rates. So this confusion matrix calculation obtained an accuracy of 86% of 258 with data for 3 variables, while in testing new test data obtained an accuracy of 90% using the same variables (namely dependents, employment and income). Based on 2022 population data where the poor family is 33% while the well-off family is 67% which is used to produce a poverty rate of 76% of well-off families and 24% of poor families using a test size of 0.4. the prediction process for poverty in Kemang Bejalu village using the Naive Bayes method. So that it can be used by the Kemang Bejalu village government to make a decision.
Perbandingan Algoritma Decision Tree dan Support Vector Machine Dalam Pemilihan Calon Mahasiswa Penerima KIP-K Nayaka Al Syahreal Kanaka; Rudi Heriansyah; Shinta Puspasari
TIN: Terapan Informatika Nusantara Vol 4 No 9 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i9.4902

Abstract

KIP Kuliah is tuition assistance from the government for high school / equivalent graduates who have good academic potential but have economic limitations. There are many things that should be considered by universities before selecting prospective students who receive KIP Lecture so that selection can be done using machine learning and classification algorithms. In this research, two machine learning algorithms will be used including: Decision Tree and Support Vector Machine (SVM). Furthermore, these two algorithms will be tested and compared the final results. Both algorithms have different results. The highest level of accuracy, precision, recall, and F1 score is 100%. This value can be achieved by the Decision Tree algorithm because the dataset used is suitable for it to solve. Therefore, the Decision Tree algorithm is recommended to be used in selecting KIP College student candidates.
Co-Authors Arda Damayanti Nayaka Al Syahreal Kanaka Nazori Suhandi Rudi Heriansyah