UJMC (Unisda Journal of Mathematics and Computer Science)

Penentuan Premi Tahunan Dan Cadangan Premi Dengan Metode New Jersey Asuransi Endowment Status Joint Life Menggunakan Suku Bunga Stokastik

2024-01-25T08:13:55+00:00

Joint life endowment status insurance is insurance that pays out if the participant dies during the policy participant's first death or survives until the conclusion of the insurance period. The purpose of this study is to calculate the amount of joint life endowment status life insurance premium reserves using the New Jersey technique in the CIR model with stochastic interest rates. The CIR model's stochastic interest rate value based on Bank Indonesia interest rates from 2018 to 2022 is 0,075. According to the calculations, the resulting annual premium is lower since the number of individuals who survive is greater than the number of persons who die over the insurance period. Furthermore, the size of the New Jersey premium reserve is zero in the first year and becomes closer to the compensation value as the insurance period proceeds.

Aplikasi Android Mendiagnosa Hama dan Penyakit Tanaman Bawang Merah Menggunakan Fuzzy Logic

2024-01-25T08:16:53+00:00

The productivity of shallots in Indonesia from year to year is still low from maximum productivity, especially in Probolinggo district because it is caused by attacks of pests and diseases so often decreases the economic value and the quality of shallots. Lack of information for farmers, especially novice farmers on shallot plants, results in late countermeasures to prevent pest and disease attacks on shallots. For that, an application system is needed, namely an expert application system that can be implemented into machine language easily and efficiently by using Fuzzy Logic. The use of the Fuzzy Logic expert system can bridge machine language that is very precise with human language which tends to be not precise. In this study the logic used is fuzzy logic with the Sugeno method based on Android using the Java programming language by testing 16 expert data. From the results of testing this application system, researchers obtained an accuracy rate of 93.75% with details of 15 accurate data and 1 inaccurate data.

Penerapan Algoritma ID3 dan Algoritma C4.5 Untuk Klasifikasi Penerima BPNT

2024-01-25T08:13:31+00:00

Non-Cash Food Assistance (BPNT) is social food assistance in the form of non-cash from the government which is given to Beneficiary Families (KPM) every month through an electronic account mechanism which is used only to buy food at traders or e-warongs. One of the difficulties that the government sometimes faces in distributing BPNT is that the distribution process is uneven and not on target. Therefore, it is necessary to carry out further analysis using a mathematical approach, so that we can determine the feasibility of a BPNT recipient prediction problem. Through the results of the data collection analysis, it can be seen whether residents are eligible to receive BPNT or not. Based on existing problems, a classification method is used to predict the eligibility of BPNT beneficiaries using two methods, namely the ID3 algorithm and the C4.5 algorithm. The ID3 algorithm produces an accuracy value of 90%, precision of 100%, and recall of 83.33%. The C4.5 algorithm produces an accuracy value of 80%, precision of 100%, and recall of 80%. The AUC/ROC value of the ID3 algorithm is 0.500, the classification is diagnosed in the AUC/ROC curve as failure or failure in classification. The C4.5 algorithm has an AUC/ROC value of 0.800, meaning that the classification is included in good classification. In this way, it can be concluded that the C4.5 algorithm has better results compared to the ID3 algorithm

Clustering Daerah Bencana Alam di Indonesia Menggunakan Metode Fuzzy C-Means

2024-01-25T08:15:13+00:00

Bencana alam merupakan kejadian luar biasa yang disebabkan oleh faktor alam ataupun faktor dari ulah manusia yang berdampak pada lingkungan dan manusia itu sendiri. Indonesia menjadi salah satu negara yang menjadi rawan bencana alam seperti tanah longsor, banjir, banjir bandang, gempa bumi, tsunami, kekeringan, kebakaran, gunung meletus, puting beliung dan gelombang pasang laut, Sehingga menimbulkan kerusakan lingkungan, kerugian harta benda, dampak psikologis, dan bahkan menimbulkan korban jiwa. Dalam penelitian ini dapat mengcluster bencana alam antara aman, cukup aman, rawan dan sangat rawan, sehingga dalam penyaluran bantuan bisa tepat sasaran. Dalam melakukan pengelompokan disini banyak metode yang bisa digunakan, namun dalam penelitian ini peneliti menggunakan metode Fuzzy C-Means. Dari hasil penelitian tersebut ada 11 provinsi yang masuk pada cluster 1, 4 provinsi yang masuk pada cluster 2, 13 provinsi yang masuk pada cluster 3 dan 6 provinsi yang masuk pada cluster 4. Berdasarkan hasil clustering terdapat beberapa provinsi yang paling rawan bencana adalah provinsi Aceh, Sumatera Utara, Riau, Sumatera Selatan, Lampung, Jawa Barat, Jawa Tengah, Jawa Timur, Nusa Tenggara Timur, Sulawesi Tenggara, Sulawesi Selatan, Papua, dan Papua Barat.

Analisis Multiple Alignment Pada Penyebaran Epidemi Sars Cov E.G 5.1 Menggunakan Metode Neighbor - Joining

2024-03-15T00:08:07+00:00

SARS CoV-2 merupakan suatu virus yang masih menjadi topik hangat di media dan sangat menarik untuk dikaji. Apalagi SARS CoV-2 semakin bermutasi dari waktu ke waktu dan memunculkan varian jenis baru. Akhir-akhir ini dunia kembali dihebohkan dengan munculnya varian SARS CoV-2 jenis baru yang bernama varian E.G 5.1 atau biasa disebut Eris. Di Indonesia, varian E.G 5.1 pertama kali dilaporkan di Provinsi Jakarta pada 09/03/2023. Berdasarkan hal tersebut, penulis ingin mengetahui proses penyebaran Epidemi SARS CoV E.G 5.1 yang terjadi di Indonesia dengan analisis Multiple Alignment. Analisis ini memiliki beberapa tahap antara lain, melakukan analisis sistem jaringan topologi, sistem jaringan daerah mutasi dan sistem jaringan mode mutasi, sehingga diperoleh pohon filogenetik menggunakan algoritma Neighbor-Joining yang digunakan untuk menentukan awal mula penyebaran virus. Data yang digunakan adalah data 92 sekuen DNA yang diperoleh melalui GISAID. Hasil dari analisis tersebut diperoleh awal mula penyebaran SARS CoV E.G 5.1 di Indonesia yang secara singkat berawal dari Jakarta 09/03/2023, kemudian menyebar ke Bogor 20/04/23, Medan 11/05/23, Surabaya 03/07/23, Bandung 24/10/23, Riau 07/12/23, dan terakhir menyebar di Provinsi Bali (Denpasar) pada tanggal 10/12/23 dan 11/12/23.