Abstract. Development of a Mental Disorders Diagnostic System Using an Inference Engine Using the Dempster-Shafer Theory Algorithm. Patients with mental disorders in Indonesia show a high number. Mental disorders can be treated with special therapies by a psychologist or psychiatrist. However, the number of clinical psychologists and psychiatrists is not sufficient. So we need a system that can diagnose mental disorders and their treatment to prevent mental disorders as early as possible. Expert system development will face uncertainty due to the lack of information needed to decide. This study develops an expert system using the Dempster-Shafer theory algorithm as an inference engine to diagnose mental disorders. The system built can recognize mental disorders based on the symptoms felt by the patient. The system also includes explanations of mental disorders, causes, and treatment recommendations for patients. The accuracy test results show a comparison between the results of expert diagnostics and the system with a system accuracy level of 84%. Keywords: Dempster-Shafer theory, Disease diagnosis, Expert systems, Mental disorders, Uncertainty Abstrak. Penderita penyakit gangguan mental di Indonesia menunjukkan angka yang tinggi. Gangguan mental dapat diatasi dengan terapi-terapi khusus oleh psikolog atau psikiater. Akan tetapi jumlah psikolog klinis dan psikiater tidak mencukupi. Maka diperlukan sebuah sistem yang dapat mendiagnosa gangguan mental dan penanganannya sehingga dapat dicegah sedini mungkin. Pengembangan sistem pakar akan menghadapi ketidakpastian karena terjadi kurangnya informasi yang dibutuhkan untuk membuat sebuah keputusan. Penelitian ini mengembangkan sistem pakar menggunakan algoritma dempster-shafer theory sebagai mesin inferensi untuk mendiagnosis penyakit gangguan mental. Sistem yang dibangun dapat mengenali penyakit gangguan mental berdasarkan gejala yang dirasakan oleh pasien. Sistem juga menyertakan penjelasan mengenai penyakit gangguan mental, penyebab, dan rekomendasi pengobatan untuk pasien. Hasil pengujian akurasi menunjukkan perbandingan antara hasil diagnosa pakar dan sistem dengan tingkat akurasisistem mencapai 84%.Kata Kunci: Teori Dempster-shafer, Diagnosa penyakit, Sistem pakar, Gangguan mental, Ketidakpastian

Inventory of goods is one of several important factors in a company, whether engaged in trading or manufacturing. It is important for inventory to be controlled and supervised in the recording and calculation of inventory, this is because inventory can affect financial reporting. If the amount of inventory is too large (overstock), on the other hand, if the inventory is a little, it will have an impact on the lack of inventory (stockout). The purpose of this research is to develop an inventory control system using the moving average method and the development of an Extreme Programming (XP) system. The moving average method in inventory prediction is more effective because this method compares current and previous data. To develop the system, the Extreme Programming (XP) system development method is used. This method has advantages including: fast process, time and cost efficiency, low risk, flexibility and easy to implement. From the test results using black box testing by testing the functionality, the system shows the system has met expectations with a value reaching 100%. Meanwhile, for testing with the DeLone and McLean models which measure the success of information systems, the score reaches 81.37%.

Melalui perkembangan teknologi, berdampak pada dunia pendidikan dengan hadirnya pembelajaran yang dapat dilakukan secara online. Tidak terkecuali pada pembelajaran Bahasa Inggris, yang banyak memunculkan lembaga belajar atau kursus yang membuka kelasnya secara online melalui platform atau aplikasi yang mereka kembangkan. Dalam menentukan plaform kursus bahasa inggiris online pengguna harus mengetahui satu per satu profil dan program yang ditawarkan. Cara ini tentunya akan dibutuhkan waktu yang lama untuk menetapkan pilihan. Tujuan dari penelitian ini yaitu menerapkan pendekatan Weight Aggregated Sum Product Assessment (WASPAS) untuk menentukan platform kursus Bahasa Inggris yang mudah dan cepat. Metode WASPAS dapat digunakan untuk penetapan prioritas pada pilihan alternatif yang memiliki relevansi dengan bobot yang diterapkan. Berdasarkan studi kasus yang diselesaikan dengan pendekatan WASPAS mendapatkan hasil alternatif terbaik yaitu English Academy (A5) dengan nilai 0,7629. Sistem pendukung keputusan yang dibangun telah mendapatkan nilai yang valid, hal ini karena hasilnya tidak berbeda dengan perhitungan manual. Untuk pengujian melalui usability testing memperoleh nilai rata-rata sebesar 86% dan masuk pada kategori baik.Through technological developments, it has had an impact on the world of education with the presence of learning that can be done online. Learning English is no exception, as many learning institutions or courses open their classes online through the platforms or applications they develop. In determining the online English course platform, the user must know one by one the profiles and programmes offered. This method, of course, will take a long time to make a choice. The purpose of this study is to apply the Weight Aggregated Sum Product Assessment (WASPAS) approach to determine an easy and fast English course platform. The WASPAS method can be used for setting priorities for alternative choices that have relevance to the weights applied. Based on case studies that were completed using the WASPAS approach, the best alternative result was English Academy (A5) with a score of 0.7629. The decision support system built has obtained a valid value because the results are no different from manual calculations. For testing through usability testing, it obtains an average value of 86% and is included in the good category.