APLIKASI PENDUKUNG KEPUTUSAN PENENTUAN PROMOSI JABATAN MENGGUNAKAN METODE PROFILE MATCHING (STUDI KASUS DI PT. PETROKIMIA GRESIK)

Human resources play an essential role in helping companies to achieve their vision and mission. As a large scale company, PT Petrokimia Gresik obviously needs to invest in Employees’ Performance Assessment System. It could act as a decision-making tool or a measure to evaluate and assess employees' performance at work so that theemployees’ promotion would be moreobjective and organized. Decision support system could be used to reduce the subjectivity in decision-making process. The decision support system that uses profile matching method or competency gap analysis was created based on the data which refers to the decree of the board of directors issued by PT Petrokimia Gresik. This system analyzes and assesses employee’s competencies by grouping and calculating core factors and secondary factors in each variable. The output of the calculation is a ranking of the candidates. By implementing decision support system which uses Profile Matching method, it assists company decision-making process in promotion decision based on employees’ competency scores more optimally.

KLASIFIKASI UMUR LAHAN PERKEBUNAN KELAPA SAWIT PADA CITRA FOTO UDARA BERDASARKAN TEKSTUR MENGGUNAKAN METODE NAÏVE BAYES

Developments and advancements in the field of Technology and Information have a considerable influence in the world of image analysis. At present, the process of image manipulation is easier to do, one of the factors in the emergence of various methods in image segmentation. Image segmentation is the first step in doing image processing, pattern recognition, computer vision, because most image processing processes depend on the results of the enhancement operation or image repair process. This final project will be implemented in the process of determining the type of oil palm plantation land using the Naïve Bayes method. The repair process starts from the RGB image to Greyscale, then proceed to the histogram equalization process, then proceed with the inverse image process. The feature extraction process is carried out after image repair operations using the co-occurrence matrix method. The extraction process of the co-occurrence matrix features 6 features, namely angular second moment value, contrast, correlation, varience, inverse different moment, and entropy. The Naïve Bayes process is one process for classifying a class data. There are four classes used in this system test, namely Young Palm Oil, Mature Palm Oil, and Old Palm Oil. Class determination is based on the largest value as the appropriate class. Based on the above objectives, a system can be created using the Matlab R2011b application program. The computation is done by using image images of various types of oil palm trees on plantations in Kalimantan which are taken from aerial photographs which are then cropped to be sampled with a pixel size of 60X60 in 400 images.

PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TIMUR BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA MENGGUNAKAN METODE SOM

Human Development Index (HDI) explains how residents can access development results in obtaining income, health, education, and so forth. HDI is based on indicators of health, education and decent living standards. In 2010 to 2014, the achievement of HDI of East Java province rose by 2.78 points from 65.36 into 68.14. But behind the development achievements of HDI, East Java province was ranked 21 out of 34 provinces in Indonesia. Three indicators have relevance in forming HDI value formation. In East Java province high and low HDI regencies or cities are shown only through a composite index, but it was not indicated where the dominant indicator of high or low rank on the HDI. Clustering regencies or cities based on indicators IPM needs to be done to ascertain the achievement of each indicator. Clustering method that can be used are Self-Organizing Maps (SOM). Based on the results of research and discussion conducted, for health indicator, 6 regency/city in the group of low, 12 regency/city in the group of medium, 16 regency/city in the group of high, and 4 regency/city in the group of very high. For educational indicator, 2 regency/city in the group of low, 15 regency/city in the group of medium, 13 regency/city in the group of high, and 8 regency/city in the group of very high. For educational indicator, 2 regency/city in the group of low, 15 regency/city in the group of medium, 13 regency/city in the group of high, and 8 regency/city in the group of very high. For decent living standard indicators, 2 regency/city in the group of low, 15 regency/city in the group of medium, 13 regency/city in the group of high, and 8 regency/city in the group of very high. For educational indicator, 4 regency/city in the group of low, 21 regency/city in the group of medium, 10 regency/city in the group of high, and 3 regency/city in the group of very high.

SISTEM INFORMASI TRACER STUDY ALUMNI PADA PRODI TEKNIK INFORMATIKA UNIVERSITAS MUHAMMADIYAH GRESIK BERBASIS WEB

The name of the Higher Education Institution will be big, because of the alumni. However, the Informatics Engineering University of Muhammadiyah Gresik (UMG) continues to be required to always improve the quality of its educational process accompanied by efforts to increase its relevance in the context of global competition. Efforts to make improvements both in the academic and infrastructure fields have been carried out. The management of alumni data at Muhammadiyah Gresik University especially Information Engineering has not been overlooked and is still done manually. Alumni data is only stored in piles of paper in the administrative space, this can cause data damage and even data loss. The UMG Informatics Engineering tracer study activity provides an increase in education in teaching and learning activities so that it can improve the subsequent learning process. Therefore, to get information on the indications of lack of implementation of the study program the success of professionalism (career, status, and income) of alumni is needed. The Web-based Tracer Study Information System in Informatics Engineering Muhammadiyah Gresik University will be made easier for study programs to get information on relevant knowledge and expertise (the relationship between knowledge and expertise with work requirements, work scope, professional position) of the alumni. The value of the convenience obtained from the application questionnaire by users is 75.95% rounded up to 76%.

SISTEM PENDUKUNG KEPUTUSAN UNTUK DETEKSI DINI RISIKO PENYAKIT STROKE MENGGUNAKAN LEARNING VECTOR QUANTIZATION

Stroke is any sudden neurologic disorder that occurs as a result of restriction or cessation of blood flow through brain arteries supply system. Stroke is the leading cause of death in Indonesia. Services pre-stroke is early detection activities, discovery and monitoring of risk factors for stroke in healthy individuals and at-risk communities that can be performed by physicians, nurses and health workers. Based on the results of the study said that when control stroke risk factors to do with the approach would reduce the number of defects by 60-90%. Works doctor for diagnosis process is not easy because of the many risk factors vary and affect each other, for example Low Density Lipoprotein cholesterol can lead to heart disease can also affect blood pressure, gender can affect the value of uric acid, uric acid can also influence blood pressure and sugar levels can affect blood pressure. Classification method is one solution that is deemed able to handle the process of classifying the status of early detection of the risk of stroke. Mechanical classification using Learning Vector Quantization (LVQ) has excess generating an error value is smaller than other artificial neural networks. Based on the results of research and discussion conducted, algorithms LVQ can recognize patterns and are able to predict the status of early risk of stroke using a variable blood pressure, blood sugar, total cholesterol, Low Density Lipoprotein, age, gender, gout, Blood Urea Nitrogen and creatinine with a total value of up to 82% accuracy.

SISTEM PREDIKSI PENGGUNAAN LISTRIK PELANGGAN DI PT.PLN (PERSERO) RAYON LAMONGAN AREA BOJONEGORO DENGAN METODE TRIPLE EXPONENTIAL SMOOTHING (BROWN)

Electricity is one of the means of fulfilling the needs of human life which is very important in this era. Excessive use of electricity will have an impact on the high use of electricity kWh. The process of recording kWh on the customer meter is carried out by officers from PLN who routinely visit the customer's homes once a month. The meter recording clerk cannot record when the customer's house cannot be reached resulting in empty customer kWh data. Prediction System Using Electricity Customers at PT. PLN Lamongan aims to determine the amount of electricity usage kWh of the customer for the next period. This research uses the Triple Exponential Smoothing method (Brown). The calculation is done on 10 different customers with 24 data, namely the use of electric kWh per period from January 2015 to December 2016 with 9 different alpha values, namely alpha 0.1 - 0.9 and uses a reference of 3 months, 6 months and 12 months before. Prediction results will be compared with the actual data of kWh to determine the failure value or error value in predictions using mean absolute deviaton (MAD) and mean absolute percentage error (MAPE). From the third average forecasting test analysis, it produces an average MAPE value of 3 months reference with an average value of 2.922%, 6 months reference with an average value of 3.092% and a 12-month reference with an average value of 4.175%. The smallest MAPE, which is a test using a 6-month reference, produces a value of 1.886% with alpha 0.1.