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All Journal Jupiter Compiler J-SAKTI (Jurnal Sains Komputer dan Informatika) Infotekmesin J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Polinema (JIP)
Wilda Imama Sabilla
Politeknik Negeri Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Identify Level of Welfare Population Based on Income Levels Using Decision Tree Method Yunita Ardilla; Wilda Imama Sabilla; Sarah Astiti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 2 (2021): JUPITER Edisi Oktober 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/3375.jupiter.2021.10

Abstract

Identification of population welfare influenced by several factors. This identification is useful to assist the government in classifying the level of welfare population which is useful for providing subsidies to be targeted. Therefore this study aims to determine the level of welfare population based on the level of income per capita using decision tree method. The selection of the best model is based on the calculation value of accuracy, precision, and recall with k-fold cross validation method. Based on experiments that have been done, it can be concluded that the decision tree model produced has good performance with a tree shape model has 622 leaves with tree size 705 of nodes, the model has an accuracy of 86,97%, precision 0.897 and recall 0.917.
Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna Yunita Ardilla; Wilda Imama Sabilla; Nurissaidah Ulinnuha
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.616

Abstract

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.
Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular Wilda Imama Sabilla; Candra Bella Vista; Dhebys Suryani Hormansyah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.425

Abstract

Cardiovascular disease is one of the leading causes of death in the world. The risk of death is important to predict to determine treatment or behavior and lifestyle changes in cardiovascular patients. Medical record data of cardiovascular patients can be used as input in predicting life expectancy. This study offers the construction of a life expectancy prediction system for cardiovascular patients. Prediction using multilayer perceptron method by testing various scenarios. In addition, feature selection methods, namely correlation based filter (CBF), linear discriminant analysis (LDA), and principal component analysis (PCA) are applied to obtain relevant features to improve classification performance. Based on the experiments conducted, the average accuracy using CBF and LDA feature selection is 84% and 84.7%, respectively. In the best trial, CBF is able to produce accuracy, precision, recall, and f-measure with value of 91.7% 85% 89.5% and 87.2%. Based on these results, it can be concluded that this prediction system is able to provide fairly accurate results
Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular Wilda Imama Sabilla; Candra Bella Vista; Dhebys Suryani Hormansyah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.425

Abstract

Cardiovascular disease is one of the leading causes of death in the world. The risk of death is important to predict to determine treatment or behavior and lifestyle changes in cardiovascular patients. Medical record data of cardiovascular patients can be used as input in predicting life expectancy. This study offers the construction of a life expectancy prediction system for cardiovascular patients. Prediction using multilayer perceptron method by testing various scenarios. In addition, feature selection methods, namely correlation based filter (CBF), linear discriminant analysis (LDA), and principal component analysis (PCA) are applied to obtain relevant features to improve classification performance. Based on the experiments conducted, the average accuracy using CBF and LDA feature selection is 84% and 84.7%, respectively. In the best trial, CBF is able to produce accuracy, precision, recall, and f-measure with value of 91.7% 85% 89.5% and 87.2%. Based on these results, it can be concluded that this prediction system is able to provide fairly accurate results
Sistem Pendeteksi Kualitas Daging Segar dengan Metode Naive Bayes Wilda Imama Sabilla; Muhammad Adisa Putra Perkasa; Dimas Wahyu Wibowo
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.5006

Abstract

Daging sapi merupakan salah satu sumber protein yang banyak dikonsumsi masyarakat Indonesia. Daging banyak dijual baik di pasar tradisional maupun pasar modern. Beberapa penjual yang tidak jujur mencampur daging segar dan tidak segar pada produknya untuk mendapatkan keuntungan lebih. Sebagian konsumen akhirnya mendapatkan daging yang kurang segar karena tidak semua konsumen memiliki pengetahuan mengenai kesegaran daging. Penelitian ini mengembangkan sistem pendeteksi kualitas daging untuk membantu pengguna yang tidak memahami tingkat kesegaran daging. Di samping itu keterbatasan mata manusia memungkinkan kesalahan dalam menentukan daging merupakan daging segar atau tidak segar. Aplikasi yang dibuat akan mendeteksi kesegaran daging melalui warna dan tekstur daging, Data yang digunakan pada penelitian ini adalah citra daging sapi segar dan tidak segar yang diperoleh dari berbagai sumber. Metode pengolahan data meliputi praproses citra dilanjutkan dengan ekstraksi fitur. Fitur yang digunakan adalah fitur warna melalui perhitungan HIS serta fitur tekstur menggunakan metode Gray-Level Co-occurrence Matrix (GLCM). Fitur warna dan tekstur tersebut selanjutnya diklasifikasikan ke dalam daging segar atau tidak segar menggunakan metode Naïve Bayes. Berdasarkan hasil pengujian, diperoleh nilai akurasi sebesar 92%. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem untuk mendeteksi kualitas daging segar dan dapat membantu menginformasikan tentang kualitas daging bagi pengguna yang tidak memiliki pengetahuan tentang kesegaran daging.
Recommendation System for Clustering to Allocate Classes for New Students Using The K-Means Method Yuri Ariyanto; Wilda Imama Sabilla; Zidan Shabira As Sidiq
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.1962

Abstract

SMAN 1 Durenan has a plan to organize the allocation of classes for new students using a system to achieve practical and efficient student grouping. The reason for implementing this class allocation system is SMAN 1 Durenan aims to create a new system to process student data for class allocation according to specific needs. This research involves the development of a Recommendation System for Clustering to Allocate Classes for New Students using the K-Means method. The system processes data of newly enrolled students at SMAN 1 Durenan based on specific attributes. The results of this student data processing serve as considerations and references for SMAN 1 Durenan to perform class allocation as needed. The analysis in this research utilizes the K-Means method to obtain data clusters that maximize the similarity of characteristics within each group and maximize the differences between the collections created. The developed recommendation system website provides information about the student data clustering results from the K-Means process at SMAN 1 Durenan.
Co-Authors Astiti, Sarah Candra Bella Vista Dimas Wahyu Wibowo Muhammad Adisa Putra Perkasa Nurissaidah Ulinnuha Suryani, Dhebys Yunita Ardilla Yuri Ariyanto Zidan Shabira As Sidiq