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All Journal Dinamik Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) ANDHARUPA CESS (Journal of Computer Engineering, System and Science) Journal of Information System Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Advance Sustainable Science, Engineering and Technology (ASSET) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika Prosiding SeNTIK STI&K Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Junta Zeniarja
Departemen Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Indonesia
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes Anugrah, Muhammad Ikhsan; Zeniarja, Junta; Setiawan, Dicky Setiawan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25838

Abstract

Diabetes is a chronic disease that arises from excess sugar levels in the body and lack of exercise intensity resulting in a buildup in the blood. Indonesia ranks fifth as the country with the largest number of people with diabetes based on a report from the International Diabetes Federation (IDF). The reason is that people with diabetes do not realize that they have diabetes, so there is a need for early detection in knowing this. The purpose of this research is to improve the performance of the Hard Voting Classifier model combining the Decision Tree, Random Forest, and XGBoost algorithms with the ADASYN oversampling technique that handles data imbalance in diabetes prediction. This study uses patient information data with a total of 1000 data and 14 features from the Medical City Hospital laboratory, Iraq. The results of this study show an increase in the performance of the prediction model with an accuracy value of 99.0%, precision 99.1%, recall 99.0%, and f1-score 98.98% without using ADASYN. Then get an accuracy value of 99.8%, precision 99.8%, recall 99.8%, and f1-score 99.8% by using ADASYN as an oversampling technique. This shows that there is an increase in the performance of the Hard Voting Classifier model so that it produces accurate predictions of diabetes, where the correctness of diabetes prediction is very good.
Optimasi Convolutional Neural Networks untuk Deteksi Kanker Payudara menggunakan Arsitektur DenseNet Mas'ud, Ryan Ali; Junta Zeniarja
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25883

Abstract

Breast cancer is a disease commonly suffered by women worldwide, ranking as the second-largest disease burden. In response to the urgent need for improved detection accuracy, Convolutional Neural Networks (CNNs) promise significant advancements. The objective of this research is to optimize the use of CNNs with the DenseNet architecture for breast cancer detection. The study employs quantitative methods, leveraging Deep Learning through CNNs. Mammography data is sourced from Kaggle, specifically the “Breast Histopathology Images” dataset. This dataset comprises 90,000 digital mammography images, which are preprocessed and divided proportionally for training, validation, and model testing. Research variables encompass CNN model parameters, training techniques, and the integration of imaging modalities to enhance breast cancer detection performance. The research focuses on processed mammography data, with accuracy and image quality as key evaluation metrics for breast cancer sample identification. Our findings demonstrate that the DenseNet architecture within CNNs achieves an impressive 92% accuracy in breast cancer detection. This remarkable performance signifies success in enhancing image quality and class prediction, aligning with the DenseNet architecture’s flow diagram. Ultimately, these results contribute significantly to effective breast cancer diagnosis by optimizing CNNs with the DenseNet architecture to improve image quality during breast cancer sampling.
Co-Authors Abu Salam Abu Salam Adhitya Nugraha Adhitya Nugraha Adi Wibowo Afridiansyah, Rahmanda Agus Winarno Agus Winarno, Agus Ahmad Alaik Maulani Ailsa Nurina Cahyani Ainul Yaqin Alan Ma’ruf, Farda Alya Nurfaiza Azzahra Anisatawalanita Ukhifahdhina Anugrah, Muhammad Ikhsan Ardytha Luthfiarta Ardytha Luthfiarta Asih Rohmani Asih Rohmani Asih Rohmani Atika Rahmawati Bayu Aryanto Budi Warsito Cahyani, Ailsa Nurina Candra, Rejka Aditya Catur Supriyanto Catur Supriyanto Debrina Luna Arghata Mangkawa Deby Arida NiMatus Sa’adah Devi Ayu Rachmawati Dianti, Reza Nur Diyan Adiatma Dzaky, Azmi Abiyyu Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Esmi Nur Fitri Esmi Nur Fitri Esmi Nur Fitri Fajarudin Zakariya Farda Alan Ma'ruf Farda Alan Ma’ruf Ferry Bintang Nugroho Fikri Budiman Fikri Budiman Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Guruh Fajar Shidik Haresta, Alif Agsakli Harun Al Azies Ida Ayu Putu Sri Widnyani Ika Novita Dewi Jaya, Sava Irhab Atma Khoirunnisa, Emila Kiki Widia Kurniawan Ridwan Surohardjo Kurniawan, Defri L. Budi Handoko Luh Putu Ratna Sundari Lutfi Kharisma M Hafidz Ariansyah M. Hafidz Ariansyah Manurung, Ayub Michaelangelo Mas'ud, Ryan Ali Maulani, Ahmad Alaik Mufida Rahayu Muhammad Jamhari Muhammad Joyo Satrio Muljono Muljono Nabila, Qotrunnada Nitho Alif Ibadurrahman Novi Hendriyanto Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pratama, Rifky Ariya Pulung Nurtantio Andono Putra, Vander Mulya Putri, Rusyda Tsaniya Eka Raden Arief Nugroho Rama Eka Saputra Ramadhan Rakhmat Sani Ramadhan, Ahnaf Irfan Ramadhan, Muhammad Eky Restu Agung Pamuji Rezaroebojo, Rizal Riyan Ardiansyah Rohman, Adib Annur Savicevic, Anamarija Jurcev Setiawan, Dicky Setiawan Sindhu Rakasiwi Sri Winarno Sri Winarno Sri Winarno Syabilla, Mutiara Utomo, Danang Wahyu Valentina Widya Suryaningtyas, Valentina Widya Wibowo Wicaksono Wibowo Wicaksono