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All Journal Jurnal Riset Akuntansi dan Keuangan EPSILON: Journal of Electrical Engineering and Information Technology
Dede Irawan Saputra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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PERBANDINGAN FAMA AND FRENCH THREE FACTOR . MODEL DENGAN CAPITAL ASSET PHCING MODEL Dede Irawan Saputra; Umi Murtini
Jurnal Riset Akuntansi dan Keuangan Vol 4, No 2 (2008): Jurnal Riset Akuntansi dan Keuangan
Publisher : Fakultas Bisnis UKDW

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (26645.381 KB) | DOI: 10.21460/jrak.2008.42.148

Abstract

Penelitian ini bertujuan untuk menguji kemompuon Fama and Freneh three factor model dalom menjelaskan retum jortofolio dibandingkan dengan CAPM. Data yang digmakm pda penelitiot ini adatah d*a sekunder dari perusahaan yang masuk dalam LQ-45 dari periede Februari 2000 sampai Juli 2007- Sampel yang digunakan adaleh perusahaan yang selalu masuk datam Lg-45 selona periode penelitian- Hasil penelitian menwtjukkan batma betdasukmtnilai adjusted P dapat disimpulkan bahwa CAPM lebih mampu menjelaskot return partofolia dibandingkan dengan Fama and French three factor model Hal ini dryot dilihat dari nilai adjusted N CAPM yang lebih besar dibanding nilai adjusted,F Fama and Frqnch three factor modelKeywords: z Market, Size, BEIME, dan Adjusted R2
Implementasi Algoritma Gaussian Naive Bayes Classifier Untuk Prediksi Potensi Tsunami Berbasis Mikrokontroler Dede Irawan Saputra; Dadang Lukman Hakim
EPSILON: Journal of Electrical Engineering and Information Technology Vol 20 No 2 (2022): EPSILON: Journal of Electrical Engineering and Information Technology
Publisher : Department of Electrical Engineering, UNJANI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55893/epsilon.v20i2.94

Abstract

The classification carried out by the Gaussian Naive Bayes Classifier algorithm can use continuous data such as the parameters that are considered when a tsunami occurs. The data collected for the classification process is some earthquake data that has occurred in Indonesia in the last 20 years. Data from the occurrence of earthquakes that are taken include the time of occurrence, the place where the earthquake occurred, the magnitude of the earthquake, the depth of the earthquake, and also the distance from the epicenter to the nearest city where the earthquake occurred. The parameters needed in implementing the prediction process are the average value of the magnitude, the depth of the epicenter, and the distance to the epicenter. Next, the values ​​of each standard deviation of magnitude, depth of the epicenter, and distance of the epicenter are also required. The microcontroller can implement the Probabilistic Density Function equation to calculate the potential for a tsunami. the microcontroller-based Gaussian Naive Bayes Classifier algorithm with the classification "Tsunami Potential" and "No Tsunami Potential" has an accuracy of 96%.
Co-Authors Dadang Lukman Hakim Umi Murtini