Internet users are continually exposed to phishing as cybercrime in the 21st century. The objective of phishing is to obtain sensitive information by deceiving a target and using the information for financial gain. The information may include a login detail, password, date of birth, credit card number, bank account number, and family-related information. To acquire these details, users will be directed to fill out the information on false websites based on information from emails, adverts, text messages, or website pop-ups. Examining the website’s URL address is one method for avoiding this type of deception. Identifying the features of a phishing website URL takes specialized knowledge and investigation. Machine learning is one method that uses existing data to teach machines to distinguish between legal and phishing website URLs. In this work, we proposed a method that combines correlation and recursive feature elimination to determine which URL characteristics are useful for identifying phishing websites by gradually decreasing the number of features while maintaining accuracy value. In this paper, we use two datasets that contain 48 and 87 features. The first scenario combines power predictive score correlation and recursive feature elimination; the second scenario is the maximal information coefficient correlation and recursive feature elimination. The third scenario combines spearman correlation and recursive feature elimination. All three scenarios from the combined findings of the proposed methodologies achieve a high level of accuracy even with the smallest feature subset. For dataset 1, the accuracy value for the 10 features result is 97.06%, and for dataset 2 the accuracy value is 95.88% for 10 features.

Stock investment is a demand-driven and demanding monetary practice. Hence, the study of stock forecasts or, more precisely, the forecasting of stock prices, plays an essential role in the stock market. Mistakes in forecasting share prices have a significant impact on global finance; thus, they require an effective method of predicting changes in share prices. Machine learning is one of the methods that can be used to predict the stock price. To predict the stock price of five companies in the telecommunications sector, Bakrie Telecom Tbk (BTEL), PT. XL Axiata Tbk (EXCL), PT. Smartfren Telecom Tbk (FREN), PT. Telekomunikasi Indonesia Tbl (TLKM), and PT. Indosat Tbk (ISAT), two algorithms are used to predict the stock prices, which are the Gaussian Process and SMOreg and train dataset from January 1, 2017, to December 31, 2019. The result of this study is SMOreg has the best result than the Gaussian Process with an RMSE value of 0.00005, MAPE 1.88%, and MBE 0.00025.

Student data that are public and stored on Universitas Klabat currently be stored in a local database and can only be accessed by the database administrator. A RESTful web service acts as a medium that provides access to the data for those who have permission, improving the data's accessibility. Any user eligible by the web service can use the Universitas Klabat Information System data to develop a mobile or another web application without making another similar database, thus eliminating the redundant process of making a duplicate database. A JSON Web Token is also used in this web service to ensure that the person accessing the web service is authorized to access the specific data. The agile method is used to develop this application. In conclusion, the web service can create, read, update, delete operation regarding the student database and provide it to the user that is authorized with a valid JSON Web Token.

Helpdesk TI adalah perangkat lunak atau sistem yang membantu pengguna memecahkan masalah mereka melalui satu atau beberapa titik kontak. Kemudian, memungkinkan pengguna untuk memecahkan masalah, melacak masalah mereka, dan mendapatkan bantuan mengenai produk atau layanan. Universitas Klabat, dalam melaksanakan proses administrasi, pekerjaan di kantor, tidak lepas dari penggunaan IT, sehingga jika ada masalah terhadap perangkat IT pasti memerlukan bantuan dari departement IT. Namun ada beberapa masalah, diantaranya adalah departemen yang melaporkan gangguan dan kerusakan sering tidak tahu berapa lama masalah mereka akan ditangani karena proses antrian pekerjaan tidak terlihat jelas sehingga terkesan agak lambat. Tujuan dari penelitian ini adalah untuk mengembangkan sistem IT Helpdesk untuk pelaporan gangguan dan monitoring pekerjaan perbaikan. Metode yang digunakan dalam penelitian ini adalah Sistem Development Lifecycle. Hasil dari penelitian ini adalah sebuah sistem yang dapat digunakan oleh departemen IT dalam membantu memonitor tugas dan pekerjaan yang masuk.

Penggemar dan komunitas hobi ikan hias predator di Manado makin berkembang dan orang yang ingin membelinya pun semakin bertambah. Berdasarkan pengamatan peneliti dibeberapa toko yang menjual ikan hias, pengenalan tentang ikan hias predator masih sangat kurang bahkan ditempat penjualan masih ada yang belum memiliki katalog tentang jenis ikan hias predator. Berdasarkan latar belakang tersebut, peneliti membuat aplikasi pengenalan ikan hias predator dengan menggunakan teknologi augmented reality berbasia android dimana pengguna dapat memanfaatkan smartphone mereka untuk melihat jenis dan info tentang ikan hias yang akan mereka beli dengan jelas dan atraktif. Metode rekayasa perangkat lunak yang digunakan adalah metode spiral karena metode ini menawarkan pendekatan yang lebih baik untuk membuat suatu program dengan cepat dan bertahap. Hasil dari penelitian ini adalah aplikasi android yang dapat menunjukan 9 jenis ikan hias predator dalam bentuk 3D yang dapat dilihat dari 4 sisi dan 6 jenis ikan lainnya dalam bentuk gambar serta informasi tertulis tentang ikan yang akan dipelihara. Kata kunci : Augemented Reality, 3D, Android

Data centers are known as a set of servers. Most corporate data and storage are located, operated, and managed on this place. Data center is a backbone of variety internet application and service, growing demand for that has drastically increased the power consumption. The energy use is a central issue in the Data Center. According to the issue, the researchers intend to analyze and design the Green Data Center at Universitas Klabat. Simulation and direct observation using clustered and virtualization are the methods used in this research to reduce power consumption. VM layer metrics on power consumption formula was adopted and calculated from the simulations. Power consumption is calculated by dividing the idle power of the physical host by the number of running VMs. The net physical host power consumption is calculated and multiplied by the CPU utilization of the VM as seen from the physical host. The result is the energy can be utilized and decrease for 286.66 % after virtualization.