Pada penelitian ini bertujuan untuk melakukan komparasi terhadap metode Naïve Bayes dan Random Forest dalam klasifikasi data pasien penyakit liver. Adapun data pengujian yang digunakan yaitu Indian Liver Patient Dataset (ILPD) yang diperoleh dari UCI Machine Learning Repository. Dataset tersebut memiliki 583 record data, 10 kriteria, dan 1 variable kelas serta dengan jumlah kelas sebanyak 2 kelas atribut, serta data set tersebut berjenis multivariate. Terdapat beberapa tahapan preprocessing yang dilakukan, antara lain normalisasi data yang diujikan, selanjutnya dilakukan analisis klasifikasi menggunakan metode naïvebayes dan random forest. Berdasarkan hasil pengujian yang dilakukan dalam memperoleh nilai akurasi perhitungan klasifikasi menggunakan Confusion Matrix, maka metode Random Forest memperoleh hasil yang terbaik yaitu dengan peroleh akurasi sebesar 70.60 % bila dibandingkan dengan Naïve Bayes yang hanya memperoleh akurasi sebesar 55.80 %. Sehingga Random Forest memiliki performa kinerja yang lebih unggul dalam perolehan akurasi yang dihasilkan dalam klasifikasi penyakit liver.

Stocks are one of the many investments that are favored by all groups because they promise high returns. But in addition to promising high returns, stocks can also provide a high risk of loss, which makes ordinary people afraid to start investing in the stock market. To prevent losses in buying stocks is to choose stocks with good fundamentals. To support this, we need an analysis that can help make decisions in choosing the best stocks in the technology sector. The saw method analysis will be used in this study, where the saw method is able to select alternatives based on predetermined categories. This study will rank the best stocks based on company fundamentals, namely EPS, PER, PBV, ROE, DER and Dividend Yield. The results of this study are EDGE stocks as the best stocks in the technology sector with the highest value of 0.88. The purpose of this research is to help investors choose stocks before investing in technology companies.

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Contract employees are employees who work in contact with a certain time agreement. However, there are times when contact employees with good performance will change their status to permanent employees. To determine whether an employee is a permanent employee, an evaluation of whether the employee's performance is worthy of being appointed as a permanent employee is required. However, to carry out this evaluation, a variable is needed to make an assessment. In the performance evaluation it is not easy to determine the value of each variable. To assist an HRD in determining the appointment of a contact employee to become a permanent employee, a decision support system is needed to facilitate HRD work. The decision support system is made using the Tsukamoto fuzzy logic method because the Tsukamoto fuzzy has a tolerance for value data. The result of the research is that the employee can be appointed as a permanent employee with a value of 93.4. The purpose of this decision support system is to determine whether or not contract employees are eligible to become permanent employees based on alternative disciplines, ways of working and behavior.