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All Journal Jurnal Simetris Telematika Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Mitra Masyarakat (JPMM) Tematik
Rastri Prathivi
Universitas Semarang
Computer Science & IT Education Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Telematika

 1 
Optimasi Algoritme Naive Bayes Untuk Klasifikasi Data Gempa Bumi di Indonesia Berdasarkan Hiposentrum Rastri Prathivi
Telematika Vol 13, No 1: Februari (2020)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v13i1.928

Abstract

Abstract: The Hiposentrum or epicentre is the source of an earthquake which is at a certain depth on earth. The classification of earthquake powers based on the depth of Hiposentrum needed to examine the potential earthquake powers spread in Indonesian territory. The results of the classification process often experience problems, namely inaccuracy in classification. To solve that problem, then algorithms optimising classification must be increased. This research uses the Naïve Bayes algorithm, which is optimized using the Adaboost algorithm. Evaluation of the results of the optimized classification algorithm is needed to determine the level of accuracy using prescriptions and recall. In this study, the object of research is earthquake data in Indonesia which will be used as training data and testing data. The average accuracy of the Naïve Bayes algorithm is 72.3%, and the Naïve Bayes and Adaboost algorithm is 85.3%.Abstrak: Hiposentrum atau pusat gempa merupakan sumber gempa yang terdapat pada kedalaman tertentu di bumi. Klasifikasi kekuatan gempa berdasarkan kedalaman hiposentrum diperlukan untuk mengetahui potensi kekuatan gempa yang tersebar di wilayah Indonesia. Hasil dari proses klasifikasi seringkali mengalami masalah yaitu ketidaktepatan dalam klasifikasi. Untuk mengatasi masalah tersebut maka algoritme klasifikasi perlu ditingkatkan optimasinya. Penelitian ini menggunakan algoritme Naive Bayes yang dioptimasi menggunakan algoritme Adaboost. Evaluasi terhadap hasil dari algoritme klasifikasi yang telah dioptimasi diperlukan untuk mengetahui tingkat akurasi menggunakan presicion dan recall. Dalam penelitian ini objek penelitian berupa data gempa bumi di Indonesia yang akan digunakan sebagai data training  dan data testing. Hasil rata - rata akurasi algoritme Naïve Bayes sebesar 72,3% dan algoritme Naïve Bayes dan Adaboost sebesar 85,3%.
Performance Evaluation of Naive Bayes Algorithm for Classification of Fertilizer Types Rastri Prathivi; April Firman Daru; Sara Sharifzadeh
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1410

Abstract

Determining the right fertilizer is very important to get optimal plant growth results. Each plant requires different nutrient requirements. Different soil types cause the soil's nutrient content and PH value to differ from one type to another. Regional conditions in a place will also cause the need for plant absorption of nutrient content to be more varied. By using the classification of the problems that have been mentioned, it can be solved by studying patterns from existing fertilizer use data into knowledge that can be used to determine decisions. In this study, modeling with the Naïve Bayes algorithm has been applied to the existing fertilizer use data where the probability value of each class has been calculated to get the highest probability value of a class. The measurement of the accuracy value of the modeling used is measured using the Split Validation method, where the training data will be divided into training data and testing data so that the accuracy value of the model is obtained. From the applied modeling, an accuracy value of 60% is obtained, which shows the level of accuracy of the model obtained from the classification results in the form of the name of the fertilizer, which is expected to help in determining the name of the fertilizer that needs to be used.
Co-Authors April Firman Daru Astrid Novita Putri, Astrid Atmoko Nugroho Edi Widodo Febrian Wahyu Christanto Huizen, Lenny Margaretta Saifur Rohman Cholil Sara Sharifzadeh Soiful Hadi Titis Handayani Titis Handayani Tria Ardianita Victor Gayuh Utomo Yunita Kurniawati, Yunita