Stingless bee is one of the bees that produce expensive honey because stingless bee only produces less than 1kg honey per year. Due to improper management and security been imposed, stingless beehive is continuously been targeted by thief for its valuable honey production. The bee farmer also is facing difficulty in tracking and monitoring the number of their beehives that are scattered in certain area in their farm. Taking serious consideration on a good quality of honey production, temperature is the major issues to concern. Honey production can be affected by extreme temperature. When the temperature is too high, the stingless bees become irritable or contradictory, when temperature is too low, the stingless bees become motionless. Therefore, the need for continuously tracking and monitoring of the beehive is crucial to conserve the quality honey production. The objective of the research is to analyze the suitable temperature for bee productivity. Next is to design a system that gives ability for the bee farmer in monitoring of their bee hive from anywhere and anytime via web based. This paper presents the development of an IOT technology in our targeted stingless beehive system at Nature n Trigona Garden, a stingless bees company in Kemaman, Terengganu, Malaysia. We develop a device which is coded using Arduino IDE 1.8.9 underlying with a sensor and microcontroller with a low battery powered, and installed at each particular beehive. The results shows that our design and implementation of the IOT devices in each beehive has achieved a success. The main contribution of this research is such that the bee farmer is able to monitor their beehive in real time as well as the system can trigger notification to user when any abnormal incidents happens to their hive.

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.

I-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining