determined from several factors. One factor is the knowledge of the age of trees. Age of trees can be known through the circle of years on wood or also called growth. From the considerations above the formula issued is how to make a growth calculation system that is able to calculate by computer calculation, so we need an analysis to determine the age of the tree with growth. To support growth in wood one method that can be used is edge detection. The first stage in the process is grayscale which is done changing the RGB color image to gray, then the sobel edge detection process works to display the outline / edge of the image, then the calculation process is carried out with the formula.Keywords : image processing, annual ring, growthring, edge detection, sobelKualitas kayu yang baik dapat ditentukan dari beberapa faktor. Salah satu faktor itu adalah dengan mengetahui usia pohon. Usia pohon dapat diketahui berdasarkan lingkaran tahun pada kayu atau disebut juga dengan growthring. Dari permasalahan diatas rumusan masalahnya adalah bagaimana membuat sistem penghitungan growthring yang mampu menghitung dengan perhitungan komputer, maka diperlukan suatu analisis untuk mengetahui umur pohon dengan growthring. Untuk mendeteksi growthring pada kayu salah satu metode yang bisa digunakan adalah menggunakan edge detection. Tahapan yang pertama dilakukan proses dilakukan adalah proses grayscale yang dimana berfungsi mengubah gambar warna RGB menjadi abu abu, kemudian proses edge detection sobel yang berfungsi menampilkan garis tepi/tepian gambar, kemudian dilakukan proses perhitugan dengan rumus.Kata Kunci : pegolahan citra, lingkar tahun, growthring, edge detection, sobel

Presence using face already widely adopted as a way of monitoring employee attendance. Research on using facial Presence never been done before by applying algorithms and algorithms Eigenface linear discriminant analysis (LDA). However, previous research has found that there are still weaknesses in the algorithms used. The weakness is that the process of identifying which takes a long time because the process of calculating the value carried on the overall image or image and the distance of the face of the webcam can affect the process of identifying faces. In this study, the algorithm used is haar cascade classifier algorithm. Haar classifier cascade or known by other names haar-like features are the rectangular features (square function), which gives an indication of the specifics on a picture or image. Principle Haar-like features are recognizing objects based on simple values of the features but not the pixel values of the object image. This method has the advantage that the computation is very fast, because it depends on the number of pixels in a square instead of each pixel value of an image. Haar classifier cascade also still be able to identify faces even if the distance face with the webcam is considerably due to the value of the facial features can still be identified. Results from this study that the system can identify the face with a good degree of accuracy. Tests carried out to 13 employees Starcross Store with each employee doing 30 times the experiment presence. Attendance successful has the success rate is 87% and 13% of the total failure of the experiment 390 times. Some absences failed to happen because there are several factors that can affect attendance as high luminance, uplifted head position, and the use of attributes (hats, glasses, etc.).Keywords : Presence, face recognition, Haar cascade classifier algorithmPresensi menggunakan wajah sudah banyak diterapkan sebagai cara untuk pemantauan kehadiran pegawai. Penelitian tentang presensi menggunakan wajah pernah dilakukan sebelumnya dengan menerapkan algoritma eigenface dan algoritma linear discriminant analysis (LDA). Namun dari penelitian sebelumnya telah ditemukan kelemahan yaitu pada proses pengidentifikasian yang membutuhkan waktu cukup lama dikarenakan proses perhitungan nilai dilakukan pada keseluruhan citra atau image dan jauhnya jarak wajah dari webcam dapat mempengaruhi proses pengidentifikasian wajah tersebut. Pada penelitian ini algoritma yang digunakan adalah algoritma haar cascade classifier. Haar cascade classifier atau yang dikenal dengan nama lain haar-like features merupakan rectangular features (fungsi persegi), yang memberikan indikasi secara spesifik pada sebuah gambar atau image. Prinsip Haar-like features adalah mengenali obyek berdasarkan nilai sederhana dari fitur tetapi bukan merupakan nilai piksel dari image obyek tersebut. Metode ini memiliki kelebihan yaitu komputasinya sangat cepat, karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah image. Haar cascade classifier juga masih dapat mengidentifikasi wajah walaupun jarak wajah dengan webcam terbilang jauh dikarenakan nilai fitur wajah masih dapat diidentifikasi. Hasil dari penelitian ini bahwa sistem dapat mengidentifikasi wajah dengan tingkat akurasi baik. Pengujian dilakukan kepada 13 karyawan Starcross Store dengan masing-masing karyawan melakukan 30 kali percobaan presensi. Absensi yang berhasil memiliki nilai keberhasilan 87% dan 13% gagal dari total percobaan 390 kali. Beberapa absensi yang gagal terjadi karena ada beberapa faktor yang dapat mempengaruhi absensi seperti pencahayaan yang tinggi, posisi kepala yang mendongkak dan penggunaan atribut (topi, kacamata, dsb).Kata Kunci : Presensi, Pengenalan Wajah, Algoritma Haar Cascade Classifier

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The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.

The purpose of this research is to design an e-CRM application that serves as an Internet-based services for customers or patients to provide information needed by the customer or patient without being limited by distance or time. With the increased competition and technological development is rapidly increasing, then every hospital strives to provide the best service to customers, or patients with a hope to get customers or new patients and retain old customers or patients. One solution that appears is electronic-Customer Relationship Management (e-CRM) is applied by using the Internet and SMS technology. The method used is waterfall method which includes, analysis and systems engineering, requirements analysis, design, programming, testing and maintenance. This application was built using the programming language PHP and MySQL to design database.Hasil achieved from the writing of this research is the application of customer service or patient that provides information (schedule doctor's office, clinic services, patient medical records, consultation and sms notifications checkup), provide member features pages to provide personalized services to customers or patients. With the application of e-CRM customer service Internet-based hospital is expected to maintain good long-term relationship with its customers.