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All Journal Progresif: Jurnal Ilmiah Komputer Journal of Innovation and Future Technology (IFTECH) Urecol Journal. Part E: Engineering
Taftazani Ghazi Pratama
Universitas Muhammadiyah Kudus
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Application of the C4.5 Algorithm for Early Cervical Cancer Classification Taftazani Ghazi Pratama; Achmad Ridwan; Agung Prihandono
Urecol Journal. Part E: Engineering Vol. 1 No. 1 (2021): January - June
Publisher : Konsorsium LPPM Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.127 KB) | DOI: 10.53017/uje.4

Abstract

Cervical cancer is one of the cancers that is of global concern because of the high mortality rate in women. Preventive measures such as early detection are needed so that patients can get treatment more quickly. Fortunately, this disease can be prevented with the role of technology to help doctors in early detection of various types of cancer. The technology developed by the researchers is using machine learning algorithms. Therefore, in this study using the C4.5 algorithm to classify cervical cancer. This algorithm aims to classify 2 classes: people who have cervical cancer, people who are healthy. The results of the experiment obtained from the C4.5 algorithm are getting an accuracy of 98.61%, precision of 98.08%, and recall of 95.24% ROC curve shows 0.982%.
Application of the C4.5 Algorithm for Early Cervical Cancer Classification Taftazani Ghazi Pratama; Achmad Ridwan; Agung Prihandono
Urecol Journal. Part E: Engineering Vol. 1 No. 1 (2021): January - June
Publisher : Konsorsium LPPM Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53017/uje.4

Abstract

Cervical cancer is one of the cancers that is of global concern because of the high mortality rate in women. Preventive measures such as early detection are needed so that patients can get treatment more quickly. Fortunately, this disease can be prevented with the role of technology to help doctors in early detection of various types of cancer. The technology developed by the researchers is using machine learning algorithms. Therefore, in this study using the C4.5 algorithm to classify cervical cancer. This algorithm aims to classify 2 classes: people who have cervical cancer, people who are healthy. The results of the experiment obtained from the C4.5 algorithm are getting an accuracy of 98.61%, precision of 98.08%, and recall of 95.24% ROC curve shows 0.982%.
RANCANG BANGUN APLIKASI PENGINGAT MAKAN DAN MINUM BERBASIS ANDROID Taftazani Ghazi Pratama; Hermawan Abdillah Hamka; Fida Maisa Hana
Journal of Innovation And Future Technology (IFTECH) Vol 6 No 1 (2024): Vol 6 No 1 (February 2024): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v6i1.3162

Abstract

Eating and drinking are basic daily human needs which are used in various activities from waking up to going back to sleep. A person's activities can be carried out well if he regulates regular eating and drinking patterns. On the other hand, someone who does not have a regular eating and drinking pattern will cause the emergence of various diseases, one of which is gastritis. Therefore, we need a solution to improve eating and drinking patterns so that the body remains healthy and avoids various diseases. The methodology used to design this application is the ADDIE method. This research aims to design an Android-based eating and drinking reminder application. The application that has been built can make it easier for someone to manage their daily eating and drinking patterns and remind them of their daily eating and drinking consumption schedule by sending notifications to the user's smartphone.
Analisis Perbandingan Kinerja Algoritma Naïve Bayes Dan KNN Untuk Memprediksi Penyakit Diabetes Haq, Osama Maulana; Ridwan, Achmad; Pratama, Taftazani Ghazi
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1: Februari 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2424

Abstract

Diabetes is a chronic disease affecting various age groups with a risk of fatal complications. Accurate diagnosis is a crucial initial step in management; however, the gradual progression of symptoms often leads to delayed detection. This study compares the accuracy of the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms in predicting diabetes using a dataset from Kaggle. Naïve Bayes was chosen for its ability to handle large datasets, missing values, irrelevant attributes, and noise, while KNN offers simplicity in implementation. The results show that KNN achieves a higher accuracy of 79% compared to Naïve Bayes at 76%. Therefore, KNN is recommended for diabetes prediction based on this dataset.Keywords: Diabetes; Naïve Bayes, K-Nearest Neighbors; Accuracy AbstrakDiabetes merupakan penyakit kronis yang menyerang berbagai usia dengan risiko komplikasi fatal. Diagnosis yang akurat menjadi langkah awal penting untuk pengelolaan, namun gejala yang berkembang perlahan sering menyebabkan keterlambatan deteksi. Penelitian ini membandingkan akurasi algoritma Naïve Bayes dan K-Nearest Neighbors (KNN) dalam memprediksi diabetes menggunakan dataset dari Kaggle. Naïve Bayes dipilih karena kemampuannya menangani dataset besar, data hilang, atribut tidak relevan, dan noise, sedangkan KNN menawarkan kesederhanaan implementasi. Hasil pengujian menunjukkan bahwa KNN memiliki akurasi lebih tinggi sebesar 79% dibandingkan Naïve Bayes yang mencapai 76%. Dengan demikian, KNN lebih direkomendasikan untuk prediksi diabetes berdasarkan dataset ini.Kata Kunci: Diabetes; Naïve Bayes; K-Nearest Neighbors; Akurasi
Co-Authors Achmad Ridwan Agung Prihandono Fida Maisa Hana Haq, Osama Maulana Hermawan Abdillah Hamka Ridwan, Achmad