Tujuan dari penelitian ini adalah Menerapkan metode AHP untuk menentukan tanaman alternative pengganti dan Mendapatkan hasil validasi dan realibiliitas untuk tanaman alternative pengganti tembakau. Penelitian ini menggunakan metode AHP,1 Sistem pendukung keputusan banyak digunakan untuk kepentingan umum,contohnya pada bidang pertanian. Oleh karena itu penelitian ini akan membahas sistem pendukung keputusan yang diharapkan dapat membantu masyarakat dalam pemilihan dan mengetahui jenis tanaman pengganti tembakau. Data diolah dan diambil dari dinas pertanian Alternatif yang digunakan adalah semangka, jagung, pisang, cabe merah dan output adalah hasil peringkingan dari AHP. Berdasarkan hasil dan pembahasan, maka dapat ditarik kesimpulan yaitu dari hasil perhitungan manual dan matlab dengan menggunakan metode AHP, Dari hasil perhitungan peringkingan AHP dapat disimpulkan bahwa alternative tanaman pengganti tembakau dengan ranking pertama untuk petani 1 yaitu tanaman bawang merah.
Indonesia is an island nation consisting of 17,504 islands and has 81,290 kilometers of coast. Indonesia has 12 (twelve) law enforcement agencies at sea, the twelve institutions have carried out their duties and functions, but have not synergized, so that sea security is not only influenced by the number of institutions, but is influenced by many factors, namely: Political and legal factors, economic factors, security and security factors, socio-cultural factors, environmental factors, technological factors, so that Indonesia needs a marine security model to determine the factors that most influence Indonesian sea security using the Analytical Hierarchy Process (AHP Method). For program calculations we use web programming. Research results: [1] Defense and security = 0.40, [2] Politics and law = 0.22, [3] Technology = 0.14, [4] Environment = 0.09, [5] Economics = 0, 06, [6] Social culture = 0.06. The sub-factor criteria that most influence each factor are as follows: [1] Defense and security = Expenditures (0.28), [2] Politics and law = Division of zones of sea areas (0.37), [3] Technology = Cyber Attack (0.52), [4] Environment = Geographical condition of the disaster zone (0.44), nn [5] Economy = Indonesian economic growth (0.30), [6] Social culture = Maritime cultural awareness (0 , 44). So the factors and sub-factors that most influence on Indonesia's sea security are defense and security factors with the national defense expenditure expenditure sub-factor.
Model time series yang sangat terkenal adalah model Autoregressive Integrated Moving Average (ARIMA) yang dikembangkan oleh George E. P. Box dan Gwilym M. Jangkins. Model time series ARIMA menggunakan teknik-teknik korelasi. Identifikasi model bisa dilihat dari ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function) suatu deret waktu. Tujuan model ARIMA dalam penelitian ini adalah untuk menemukan suatu model yang akurat yang mewakili pola masa lalu dan masa depan dari suatu data time series. Pada penelitian ini, Penulis akan menganalisis penurunan algoritma suatu metode peramalan yang disebut metode peramalan ARIMA Kemudian menerapkan metode tersebut pada data riil yaitu data produksi air di PDAM Pamekasan dengan bantuan komputer dan software SPSS, yang nantinya akan diterapkan di dalam memberikan informasi dan analisis yang akurat terhadap perusahaan PDAM Pamekasan.Dari hasil pembahasan diperoleh rumus ARIMA yang berbentuk: Profit=+Y+Z, kemudian dari hasil penerapan data riil yaitu pada data produksi air di PDAM Pamekasan diperoleh model ARIMA (1 0 0) (0 0 1) sebagai model terbaik. Dengan model :
Communication between people is essential for daily life activities. However, humans are created with their own strengths and weaknesses. One of them is the difficulty of communication and interaction for people with hearing and speech impairments. Sign language is a language for people who have difficulty hearing and speaking. However, sign language is not popular in society, and people who have it will have more difficulties. This research aims to classify hand gestures of sign language into letters using a convolutional neural network (CNN). The dataset is obtained from Kaggle, with a total of 34,627 data divided by the ratio of training and testing data of 80:20. From the test results, the letters of the alphabet that can be translated are: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, S, T, U, V, W, X, Y, and Z. Furthermore, validation accuracy is obtained. In this study, a very high validation accuracy was obtained. The easiest letters to guess are V and N, while the most difficult letters to guess are n, c, j, and z. With different preprocessing, the loss value can be reduced, giving a higher accuracy of 95.4%.
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