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All Journal Tech-E SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) PROFICIO: Jurnal Pengabdian Masyarakat Bulletin of Information Technology (BIT) International Journal Software Engineering and Computer Science (IJSECS) Innovative: Journal Of Social Science Research
Suwaryo, Niko
Universitas Medika Suherman
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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DETEKSI SERANGAN PADA INTRUSION DETECTION SYSTEM ( IDS ) UNTUK KLASIFIKASI SERANGAN DENGAN ALGORITMA NAÏVE BAYES, C.45 DAN K-NN DALAM MEMINIMALISASI RESIKO TERHADAP PENGGUNA Suwaryo, Niko; Nawangsih, Ismasari; Rejeki, Sri
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 8 No 2 (2021): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v8i2.732

Abstract

ABSTRACT Intrusion Detection System is the ability possessed by hardware or software that serves to detect suspicious activity on the network and analyze and search in general. The purpose of this study is to classify attack detection on the Intrusion Detection System using the C.45, Naïve Bayes and K-NN algorithms to see how big the attack is. The benefits gained in this study are as a test and learning material in analyzing, classifying attacks so that they can prevent and minimize attacks to users. To overcome this problem, this study uses the C.45 algorithm, Naïve Bayes, K-NN, K-NN algorithm produces an accuracy rate of 82.58%, Recall 81.73% and Precision 84.11% while the Naïve Bayes accuracy 96.91%, Recall 97,45% and Percision 96.18% and the algorithm produces an optimal value of C.45 accuracy 97.80% Recall 98.18% and Precision  97.60%. On the attribute (attack) which has the number of classes or normal labels, dos, probes, r21. The results of the lowest K-NN algorithm are caused or normal to be considered yes(an attack) which should be No(no attack)and the C.45 algorithm attribute(attack) normal, dos, probe and r21, normal(no attack), yes(the presence of an attack) is optimal in the classification of attack detection data on Intrusion Detection System(IDS). Keywords: Data Mining, C.45, Naïve Bayes and K-NN, Intrusion Detection System(IDS)
Pelatihan prediksi penyakit diabetes untuk pencegahan dini dengan metode regresi linear Niko Suwaryo
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 1 (2024): March
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i1.17240

Abstract

Abstrak                                                                                 Pengabdian masyarakat yaitu guru yang mengikuti pelatihan penggunaan aplikasi. Umpan balik tersebut bisa berupa pertanyaan, saran atau  pemasukan yang bisa membantu dalam mengevaluasi keberhasilan kegiatan PKM. Selain itu, evaluasi juga bisa dilakukan dengan cara mengevaluasi hasil akhir dari pengabdian masyarakat, yaitu aplikasi android yang dibuat. Evaluasi ini bisa dilakukan dengan cara mengecek apakah aplikasi yang dibuat sudah sesuai dengan yang diharapkan, serta juga apakah aplikasi yang dibuat sudah sesuai dengan yang dibutuhkan, serta juga apakah aplikasi tersebut sudah bisa diakses. Prediksi diabetes dimasa mendatang dapat diketahui melalui pemanfaatan dataset dengan melalui pendekatan metode prediksi melalui tahapan yang terstruktur dalam menganalisis data yang digunakan menghasilkan nilai nilai RSME saat melakukan evaluasi model sebesar 0.000 +/- 0.000. Pengujian performa terhadap model dan algoritma yang digunakan dalam evaluasi dapat menghasilkan gambaran yang relevan dengan skenario yang dimodelkan. Nilai RMSE didapat saat melakukan evaluasi performa model sebesar 0.000 +/- 0.000 melalui aplikasi. Kata Kunci : data mining; prediksi;  linear regression; diabetes; estimasi AbstractCommunity service, namely teachers who take part in training in using the application. This feedback can be in the form of questions, suggestions or input that can help in evaluating the success of PKM activities. Apart from that, evaluation can also be done by evaluating the final results of community service, namely the Android application created. This evaluation can be done by checking whether the application created meets expectations, whether the application created meets what is required, and whether the application can be accessed. Predictions of diabetes in the future can be known through the use of datasets using a prediction method approach through structured stages in analyzing the data used to produce an RSME value when evaluating the model of 0.000 +/- 0.000. Performance testing of the models and algorithms used in the evaluation can produce images that are relevant to the scenario being modeled. The RMSE value obtained when evaluating model performance is 0.000 +/- 0.000 through the application.. Keywords: data mining; prediction; linear regression; diabetes; estimation
Co-Authors Amat Basri Amat Basri Arif Rahman Arif Rahman Hakim Arif Rahman Hakim Atmaja, Dewi Marini Umi Dewi Marini Umi Atmaja Dewi Marini Umi Atmaja Ilham Roni Yansyah Koniasari Kurniawan, Avip Masgo, Masgo Nawangsih, Ismasari Nugraha Nugraha SANTOSO SANTOSO Sri Rejeki Tugiman Tugiman Wijoyo, Sandy Gunarso