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All Journal Jurnal Informatika Scientific Journal of Informatics
Halim Fathoni, Halim
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Computer Science & IT

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DESAIN DAN IMPLEMENTASI SOFTWARE AS SERVICE PADA PENGELOLAAN ABSENSI KEDISIPLINAN MAHASISWA POLITEKNIK NEGERI LAMPUNG Fathoni, Halim; Kenali, Eko Win
Jurnal Informatika Vol 14, No 1 (2014): Jurnal Informatika
Publisher : IIB Darmajaya

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Abstract

Cloud computing and the Internet is the latest generation in the world of information technology (IT). This technology allows an organization to save costs IT infrastructure investments, in general, the organization that will use IT must build a data center that consists of multiple servers and build a whole needs supporters such as backup power, data backup and others are certainly in need of funds a little. With cloud computing technology was submitted burden on providers including peraawatan costs. Based on the type of service it, cloud computing is divided into three: SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (infrastruture as a Service) (Juan, 2012). SaaS is an application that is intended to be used multiple users and invested in cloud Infrastructure generally accessed through the Internet by using a browser (firefox, chrome, etc.). In the traditional way of software deployed by installing on a desktop computer and its common one license for each computer. In the SaaS application can be used without having to install on the computer (Juan, 2012). This research emphasizes on how to build software based on SaaS and it’s implementing POLINELA. Of these activities is expected to be dug deeper into the advantages and weaknesses of this technology. Attendance management discipline problems solved by the students try to build software based on SaaS. With this technology officer can enter data from anywhere and at anytime, except that students can check without having to make noise in the room departmentKeywords: Currently, Attendance, Lecturer Guardian, loud
Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.
Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Purpose: This study aims to developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC), which expected will give information and can use by the farmer and industrial food crops. Methods: On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. Result: The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438. Novelty: Prediction system of food crops by data mining.
Co-Authors Arifin, Oki Eko Win Kenali Kurniawan Saputra