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All Journal DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) IJIIS: International Journal of Informatics and Information Systems Jurnal Akrab Juara
Irfan Santiko, Irfan
STMIK AMIKOM Purwokerto
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Medicine & Pharmacology Social Sciences

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PERBANDINGAN ALGORITMA CART DAN NAÏVE BAYESIAN PADA KASUS DIAGNOSIS PENYAKIT DIABETES Hermawan, Hellik; Santiko, Irfan
Jurnal Akrab Juara Vol 4 No 2 (2019)
Publisher : Yayasan Akrab Pekanbaru

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

Abstract

Diabetes mellitus is a disease that threatens serious health, can cause death and the World Health Organization (WHO) estimates that every 10 seconds there is one diabetes patient who dies of this disease. This makes researchers and practitioners focus their attention on detecting / diagnosing diabetes mellitus and preventing it because this disease can cause complications. The method used in this research is problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion drawing. The algorithm used in this study is CART and Naïve Bayes by using a dataset taken from the UCI Indian Pima database repository which consists of clinical data of patients who detected positive and negative diabetes mellitus. The validation and evaluation methods used are 10-cross validation and confusion. Matrix for precision, recall and F-Measure. The results of calculations that have been done, the results of the accuracy of the CART algorithm are 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. While the diabetes dataset tested by the Naïve Bayes algorithm gets an accuracy value of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus it is recommended to use the CART algorithm.
Comparison of Cart and Naive Bayesian Algorithm Performance to Diagnose Diabetes Mellitus Santiko, Irfan; Subarkah, Pungkas
International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.9

Abstract

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.
Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease Santiko, Irfan; Honggo, Ikhsan
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.14

Abstract

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.
Classification of Low Birth Weight Baby Under Anthropometry uses Algorithms K-Means Clustering on Maternity Hospital Santiko, Irfan; Kurniawan, Deni
International Journal of Informatics and Information Systems Vol 3, No 1: March 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i1.5

Abstract

LBW infants with birth weight less than 2500 grams regardless gestation period. Low birth weight is the weight of a baby who weighed within 1 hour after birth. World Health Organization (WHO) since 1961 states that all newborns are underweight or equal to 2,500 g called low birth weight infant (low birth weight). According to WHO. Statistically, morbidity and mortality in neonates in developing countries is high, with the main causes is associated with LBW. To facilitate medical personnel in determining the risk of LBW. From the testing that has been done by the author, the k-means clustering algorithm has accuracy in classifying LBW babies by spacing the proximity between variables and the similarities in the test data,
Pendampingan Penggunaan Aplikasi Bank Sampah Pada Kelompok Swadaya Masyarakat (KSM Bima) Kelurahan Teluk Kecamatan Purwokerto Selatan Krisbiantoro, Dwi; Santiko, Irfan; Riyanto, Riyanto
DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Vol 5, No 1 (2021)
Publisher : Politeknik Negeri Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32486/jd.v5i1.581

Abstract

Tujuan dari kegiatan pengabdian ini adalah selain untuk melaksanakan tridharma perguruan tinggi juga salah satu kewajiban membantu permasalahan yang ada ditengah masyarakat yaitu masalah sampah yang merupakan masalah hampir ditemui diberbagai kota di indonesia. Berbagai upaya telah dilakukan oleh berbagai kalangan untuk menanggulangi sampah salah satunya dengan mendirikan Bank sampah secara swadaya masyarakat. Permasalahan yang timbul muncul jika jumlah nasabah sampah sudah cukup banyak tentu dibutuhkan penanganan yang serius agar pengelolaan data sampah dan nasabah dapat terkontrol dengan baik.Melihat dari sisi ekonomi dan sosial dengan adanya bank sampah di tengah warga tentunya sangat membantu warga tetapi akan lebih baik lagi apabila manajemen bank sampah dikelola dengan memanfaatkan teknologi informasi saat ini, dalam hal ini adalah berupa aplikasi berbasis website dimana nantinya warga dapat melihat saldo sampah mereka secara real time dan petugas bank sampah dapat dengan mudah melakukan pelaporan dan pencatatan ke dalam sistem sehingga akan lebih cepat dan efisien dalam manajemen data sampah warga. Pendampingan pengggunan aplikasi bank sampah berbasis web perlu dilakukan kepada petugas bank sampah KSM Bima dan masyarakat sekitar agar pengelolaan manajemen bank sampah dapat berjalan dengan baik secara administrasi dan transparan secara pelaporan hasil keuangan. Adapun kegiatan pendampingan dilakukan dengan cara ceramah, tutorial  dan praktik langsung ke masyarakat dan petugas bank sampah secara terjadwal dan berkesinambungan agar khususnya petugas bank sampah dapat memahami dan mengimplementasikan langsung aplikasi bank sampah berbasis web. Kata kunci : Bank sampah, aplikasi, web
Co-Authors Deni Kurniawan, Deni Dwi Krisbiantoro, Dwi Hellik Hermawan, Hellik Honggo, Ikhsan Riyanto Riyanto Subarkah, Pungkas