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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika SIGMA: Information Technology Journal SAINTEK
Karina Imelda
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Penerapan Metode Klasifikasi Dengan Algoritma Decision Tree C4.5 Untuk Mendiagnosa Awal Penyakit Ginjal Kronis Karina Imelda
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.5070

Abstract

Patients with kidney disease find it difficult to know because they have to go through a series of laboratory tests with a considerable amount of time at the hospital. The complexity of the detecting process can be made easier using technology with data processing or Data Mining. Data Mining is the process of mining or discovering new information that aims to overcome certain conditions by looking for certain patterns and rules of a large amount of data. To diagnose early patients with chronic kidney disease with Data Mining using the classification method with the Decision Tree C4.5 algorithm. Decision Tree or meaning a decision tree is a prediction model with an hierarchical structure that has the concept of converting data into rules and decision trees, data in decision trees are expressed in tables with attributes and records that state parameters as tree formation criteria. The study used Chronic Kidney Disease data as a dataset and applied the classification method with the Decision Tree C4.5 algorithm. This study uses RapidMiner 9.0.3 data mining tools. The results obtained from this study show an accuracy of 89.05%.
Komparasi Algoritma Klasifikasi Machine Learning Dengan Penerapan Metode Ensemble Stacking untuk Menganalisa Sentimen terhadap Kesehatan Mental Annisa Maulana Majid; Karina Imelda; Ismasari Nawangsih
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3561

Abstract

Mental health often goes undetected due to the absence of physical symptoms, which hinders timely and appropriate intervention. Many individuals choose to express their emotions on social media rather than access professional services. However, the use of social media can potentially worsen mental health conditions and even impact physical well-being. Therefore, early detection through the analysis of digital data, particularly social media posts, using machine learning approaches is essential. Previous research on mental health sentiment analysis has utilized classification algorithms, but accuracy improvement remains necessary. This study compares single classification algorithms and applies an ensemble stacking method that combines multiple classifiers as base learners and a meta-learner. The results show that the stacking method achieves a higher accuracy of 88.13%.
Analisis Opini Konsumen terhadap Testimoni Produk Pelapak pada Online Marketplace dengan Pendekatan Sentimen Analisis Harahap, Handala Simetris; Suprapto; Suprianto, Asep; Karina Imelda
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Testimoni dan produk review, dimana setiap calon pembeli dapat melihat testimoni atau review yang diberikanoleh pembeli lain terhadap produk yang telah dibeli sehingga calon pembeli baru akan mengetahui baikburuk nya produk tersebut sebelum memutuskan untuk membeli atau tidak. Selain itu testimoni userberguna untuk menilai reputasi sebuah online marketplace, dengan online marketplace yang begitu beragamdiperlukan sebuah model untuk membantu pelanggan dalam memilih toko dan online marketplace yang tepat.Pada tulisan ini penulis menggukan sentiment analisis dalam menganalisa product review dari tiap pelanggan.Sehingga mengetahui kelebihan dan kekurangan dari tiap online marketplace yang sedang diteliti.
Co-Authors Annisa Maulana Majid Harahap, Handala Simetris Nawangsih, Ismasari SUPRAPTO Suprianto Suprianto