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Sriyanto -
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Computer Science & IT Economics, Econometrics & Finance Veterinary

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Penentuan Siklus Estrus pada Kancil (Tragulus javanicus) Berdasarkan Perubahan Sitologi Vagina Najamudin -; Rusdin -; Sriyanto -; Amrozi -; Srihadi Agungpriyono; Tuty Laswardi Yusuf
Jurnal Veteriner Vol 11 No 2 (2010)
Publisher : Faculty of Veterinary Medicine, Udayana University and Published in collaboration with the Indonesia Veterinarian Association

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Abstract

The aim of this study was to determine the estrus cycle and the length of estrus in Tragulus javanicuson the basis of its vaginal cytology. Vaginal swabs were collected daily at 7 am for two months. Smears ofthe swab were then prepared on glass slide and they were stained with Giemsa. Vaginal epithelial cells;parabasal, intermediet and superficial cells were counted and their percentages during proestrus, estrusand diestrus were determined. Diestrus was characterized by the absent of superficial cells in the vaginalepithel. Proestrus was characterized by the progressive increase in percentage of intermediet/superficialcells in vaginal epithel, whereas estrus was characterized by the presence of superficial/cornification cellsin most vaginal epithel. On the basis of its vaginal cytology, it was determined that the estrous period offemale Tragulus javanicus was 3 days (48-60 h) and the length was 11 days (10-14 day). The change in thecytology of vaginal epithelial cells appeared to a good marker to determine the estrus cycle phase of theTragulus javanicus
An Analysis of theComparative Method of Classificationin Determining Characteristics of Non-Active Students Fitra Luthfie Averroes; Jaka Fitra; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Classification is a data mining technique that aims to know the data model being used. By using a data mining classification technique, we can classify existing information into their classes. Classification methods can also be applied in education, for example to group active and inactive students into higher education programs, and classify them based on their characteristics. This paper presents a comparison of several classification methods, which are: Naïve Bayes, k-NN and C 4.5. This paper uses the data from the inactive students of AMIK DCC campus C on 2013-2016 periods as the criteria to evaluate the group performance. The inactive students are divided into three groups: first-year, first-two-years, and three years inactive students. The results of this study indicate that the Bayes naive method provides higher accuracy than k-NN and C 4.5. The accuracy classification is Naïve Bayes 79.22%, while k-NN and C 4.5 are 77.21% and 74.94%, respectively.Key word: Decision Tree; C4.5; classification; Naïve Bayes; AMIK DCC Campus C
Implementation of Data Mining Using Association Rules for Transactional Data Analysis Muhamad Brilliant; Dwi Handoko; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Data is an important property for everyone. Large amount of data is available in the world. There are various repositories to store the data into data warehouses, databases, information repository, etc. This large amount of data needs to process so that we can get useful information. Data mining is a technique to get information that hidden from collections of data. There are several major functions in data mining such as estimation, prediction, classification, clustering and association.This research use association rule to find the interconnections of the association between the data items in data transaction. The technique used to find the ruleis the FP-Growth. FP-Growth is one of the algorithms used to find frequent item sets in the set of transaction data.This study aims to create a simulation using a data mining association rule with the FP - growth algorithm as a reference to determine a list of product packages that offered to consumers. Testing that has been done based on the results of functional testing with black box method, it can be concluded that by implementing data mining with association rule method can help the company in finding consumer pattern. It is expected that the company can create a list of entertainment service, product packages that can be offered to consumers based on the rules generated at competitive pricesKeywords: Association Rule, Data Mining, FP-Growth
Comparison Of Data Mining Methods For Recipient Prediction Poor Student Assistance (BSM) In MAN 2 North Lampung Ovi Naeni; Resy Anggun Sari; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

MAN 2 North Lampung is a State Madrasah Aliyah or equivalent to Senior High School which has implemented poor student assistance (BSM),  system by considering the economic condition of the students or the criteria that have been set. Selection of  BSM acceptance is a semi-structured problem type meaning that this process is not a routine agenda of a school but the agenda held at a certain time that is when students are in class X. Determined the BSM recipient candidate must collect the data file of candidate selection of BSM recipients from students’ data coming from poor family to very poor family. So it takes a relatively long time, as well as high accuracy in making decisions. In predicting students who receive BSM, the authors apply the data mining process using the Naive Bayes method, Decision Tree, K-NN. The attributes used consist of siblings, Parent Occupation, parental income, smart indonesian card (KIP) recipients or not, the status of the family as an orphan or not. To perform the process of data mining in need of tools aids that is RapidMiner 5. The Implementation of data mining using a comparison of  3 methods can be seen based on the sample number of 393 students. the results of the precision value of the Naive Bayes method are better used for this study compared to other methods. While based on recall and accuracy values, Decision Tree is better used than other methods. But when viewed from the overall results of BSM receiver predictions, the most influential variable is parent income and receiver the KIP card.Keywords: Data Mining , Decision Tree, Naive Bayes, and K-NN
RAINFALL PREDICTION USING DATA MINING TECHNIQUES Riko Herwanto; Rosyana Fitria Purnomo; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Rainfall is an important factor in agrarian countries such as Indonesia. Rainfall prediction has become one of the most challenging technological challenges and challenges in the world. And also the most significant and difficult task for researchers in recent years. In Data Mining, the classification algorithm is primarily used to predict rainfall, temperature, various methods of available rainfall estimation that will be used to determine the cultivation time for a particular crop, a particular crop varieties.The reliability of this prediction depends on accuracy in choosing correlated variables. If existing historical databases fail to record the most correlated variables, then the reliability of these data-driven forecast approaches is questionable. In this paper, an attempt has been made to develop a methodological framework that leverages the power of a predefined data mining analysis (decision tree). The decision-based rainfall prediction model developed maps climate variables, namely; a) temperature, b) humidity, and c) wind speed over the observed rainfall database.This paper uses data mining techniques such as Clustering Technique, Decision Tree and classification for rainfall prediction. Keywords: Rainfall,  Rainy Season, Data Mining,  Classification, Decision Tree, Bayesian Technique.
Prediction Of Student Performance Using Decision Tree C 4.5 Algorithm Rames Krisnan Kuntoro; Rukin Sudarwanto; Sriyanto -
Prosiding International conference on Information Technology and Business (ICITB) 2017: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 3
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

This paper aims to make predictions of student achievement based on socioeconomic status of parents, student discipline and student achievement using data mining method with algorithm decision tree classifier C 4.5. For comparison, the research data were analyzed also with CHAID (Chi Squared Automatic Interaction Detection) and multiple regression. The research approach used is quantitative. The subject of this research is the elementary school students in SD Negeri 4 Trimulyo. Data collection techniques used are documented. The results of this study are very helpful for educational institutions to monitor the early improvement of student academic achievement, so that can be accompanied the learning process in order to achieve the expected performance Keywords: Data Mining, Classifier, Decision Tree, C45, CHAID, and Multiple Regression.
Co-Authors Amrozi Dwi Handoko Fitra Luthfie Averroes Fitra, Jaka Muhamad Brilliant Ovi Naeni Rames Krisnan Kuntoro Resy Anggun Sari Riko Herwanto Rosyana Fitria Purnomo Rukin Sudarwanto Rusdin - Solly Aryza Srihadi Agungpriyono Tuty Laswardi Yusuf