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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA Jurnal Teknologi dan Manajemen Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Paradigma Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA SMARTICS Journal Faktor Exacta Rang Teknik Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JURNAL TEKNOLOGI INFORMASI Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Scientific Journal of Informatics CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal of Intelligent Decision Support System (IDSS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Indonesia : Manajemen Informatika dan Komunikasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Ilmiah Multidisiplin (JUKIM) JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) SAGA: Journal of Technology and Information Systems Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Ratih Titi Komalasari
Informatika, Universitas Nasional Jakarta
Aerospace Engineering Humanities 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : saga journal of technology and information systems

 1 
Implementation of Naïve Bayes and K-NN Algorithms in Diagnosing Stunting in Children Wulan Widhari; Agung Triayudi; Ratih Titi Komala Sari
SAGA: Journal of Technology and Information System Vol. 2 No. 1 (2024): February 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v2i1.242

Abstract

Indonesia faces a huge potential risk of stunting, as revealed in the Indonesian Nutrition Status Analysis according to 2022 data, the stunting rate reached 24.22% in 514 districts / cities throughout Indonesia. To prevent stunting in children, early detection can be done. This research was conducted to compare the performance of two algorithms Naive Bayes and K-NN to predict stunting cases in children, to get a better picture of how classification algorithms predict stunting cases with a better level of accuracy and responsiveness, comparison experiments of several algorithms are needed using specific datasets to develop an optimal classification model. Based on the results of performance testing on the K-Nearest Neighbor and Naive Bayes methods in testing the performance of accuracy, precision, recall, and f1-score, the results of performance testing on the naïve bayes method obtained performance values on 30% testing data are accuracy of 71%, precision 71%, recall 76%, and f1-score 73%. The performance results of the K-NN method using the euclidean distance measurement obtained the best performance value, namely accuracy of 97%, precision of 98%, recall of 96%, f1-score of 97% at a value of k = 3. Based on the performance results of the comparison of the Naive Bayes and K-NN methods, it shows that the best classification method on the stunting dataset is the K-NN method because it gets better performance than the Naive Bayes method.
Implementation of K-Nearest Neighbour (KNN) Algorithm and Random Forest Algorithm in Identifying Diabetes Diranisha, Virly; Agung Triayudi; Ratih Titi Komalasari
SAGA: Journal of Technology and Information System Vol. 2 No. 2 (2024): May 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v2i2.253

Abstract

Diabetes, one of the noncommunicable diseases (NCDs), is currently a major health threat worldwide. So far, diabetes symptoms have only been diagnosed by people according to known physical characteristics without the support of factual evidence or other medical considerations. With the advancement of technology, it is possible to use algorithms to solve various kinds of problems. One of artificial intelligence (AI), machine learning, concentrates on creating systems that can learn from data. This research uses the K-Nearest Neighbor (KNN) and Random Forest algorithms that can be utilised as testing algorithms to identify diabetes. Classification is done based on training data that has been provided in the dataset. The purpose of this research is to determine the best classification in identifying diabetes with the K-Nearest Neighbor (KNN) algorithm and the Random Forest algorithm and is expected to provide more understanding of the implementation of machine learning models. comparing the two algorithms between the KNN algorithm and the Random Forest algorithm. By dividing the testing data and training data using a ratio of 20%: 80% randomised data 300 times. The results of the accuracy evaluation obtained from the Confusion Matrix show that the Random Forest Algorithm has the best accuracy value of 77%, Precision 89%, Recall 78% and F1-Score 83% with an estimator of 100 trees. While the KNN algorithm obtained accuracy of 73%, Precision 87%, Recall 73% and F1-Score 79% of the value of K = 7. Based on the comparison results of the two algorithms, it shows that the accuracy value obtained is greater than the Random Forest algorithm even though the value obtained is not much different.
Co-Authors Abdul Aziz Hasibuan Adinne Islamiyati Aditya Prasetya Afif Maulana Agung Triayudi Ahmad Fahreza Ahmad Khuzaifi Aldisa, Rima Tamara Alfian Cutryanto Alma Bryan Fitri Finika Andriana, Septi Andrianingsih Anwar Anwar Arief, Arya Asep Nurdin Asrul Sani Bayu Abrianto Risnadi Bayu Yasa Wedha Darmawan, Dika Rizki Dede Wandi Dika Rizki Darmawan Diranisha, Virly Doddy Prasetyo Endah Tri Esti Handayani Fachreza, Sodi Farhany, Nadia Muis Fauziah Fauziah Faza Nadhira Fikar Wahyu Tyas Tono Finika, Alma Bryan Fitri Firdhani Novrizal Firman Firman Ghirrid, Aria Andros Gusti Karnawan Handayani, Endah Tri Esti Hasibuan, Abdul Aziz Hindarto, Djarot Ikbal Danu Setiawan Indriawan, Rizal Ira Diana Sholihati Irfan Hadi Putra Kiki Vebiant Ladika, Anwar Wali M Alwi Saepul Zaman Mardiani, Eri Mochamad Hariadi Mohammad Iwan Wahyuddin Muhamad Reza Pahlevi Muhammad Hafizhan Muhammad Ikbal Muhammad Ikbal Muhammad Nurdin Muhammad Nurdin Muhammad Rafli Mustofa Kamal Syarifudin Naqiyah, Shofy Novi Dian Nathasia Nur Aprilia Nur Hayati Pamungkasari, Panca Dewi Pandyawan Eka Rizqullah Prasetya, Aditya Puspita, Anna Thasyia Putra, Irfan Hadi Raditya, Muhammad Maheswara Raffi Dima Sampurno Rahmat Azyad Samallo Raja Timor Purba Raka Adji Setiawan Ravi Anwar Rena Cahya Hutama Reza Rifqi Maulana Rifqi Aldy Al Hafizh Harahap Rifqi Naufal Senja Pratama Risnadi, Bayu Abrianto Rizal Maulana Yusuf Effendi Rizki Akbar Mahdafiki Salsabilah, Salsabilah Sampurno, Raffi Dima Sapto Wibowo Sari Ningsih Septi Andriana Septi Andryana Setiawan, Ikbal Danu Setiawan, Raka Adji Shobur Abdusalam Sholihati , Ira Diana Sholihati, Ira Diana Sodi Fachreza Suryana, Muhamad Fajar Teguh Riyono Adi Tyas Tono, Fikar Wahyu Ucuk Darusalam Vebiant, Kiki Wa'asaro Telaumbanua Wahyu Suratman Wahyuddin, M Iwan Wahyuddin, Moh. Iwan Wahyuddin, Mohammad Iwan Wedha, bayu Yasa Wildan Alfy Syahry Wilsen Wilsen Wilsen Wilsen, Wilsen Wulan Widhari Yanuar Bimantoro Yusuf Effendi, Rizal Maulana Zaman, M Alwi Saepul