Prediksi Kuota Pemesanan Bahan Bakar Pada SPBU dengan Metode Regresi Linear Berganda

Bahan bakar minyak menjadi komoditi penting dalam menjalankan roda perekonomian suatu negara, data Badan Pengatur Hilir Minyak dan Gas(BPH MIGAS) mencatat Indonesia menghabiskan 28,25 juta kiloliter selama tahun 2019, angka ini dihimpun dari seluruh Stasiun Pengisian Bahan Bakar Umum (SPBU) yang menjadi hilir distribusi BBM kepada masyarakat, namun disisi lain SPBU sering kehabisan stok karna kurangnya pengendalian terhadap stok, dampaknya adalah antrian panjang masyarakat di SPBU, bagi SPBU yang kehabisan stok jelas akan mengurangi pemasukan karna delay tidak ada penjualan selama proses pengiriman dari hulu ke hilir, maka dibutuhkan adanya sistem yang mampu membantu memprediksi berapa kuota yang harus dipesan sehingga kondisi out of stock tidak terjadi, untuk melakukan peramalan kuota bahan bakar digunakan metode regresi linier berganda yang terdiri dari variabel independent stok sisa (X1), stok masuk (X2) dan variabel dependent stok keluar (Y). Setelah dilakukan uji asumsi klasik dapat disimpulkan bahwa variabel independent (X1 dan X2) berpengaruh positif terhadap variabel dependent (Y). Dari hasil pengujian tingkat error menggunakan metode MAPE (Mean Absolute Percent Error) diperoleh tingkat error untuk peramalan pertalite selama seminggu sebesar 11,0% dan untuk tingkat error peramalan solar sebesar 13,2%.

Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error 456.963.281 and Mean Absolute Percent Error 10,0%. Moving Averge 4 was 206.771 tons with the Mean Absolute Deviation 27.333, Mean Squared Error 752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was 210.908 tons with Mean Absolute Deviation 26.890, Mean Squared Error 723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.

Assessing Heart Rate Using Consumer Technology Association Standards

It is difficult for developers, researchers, and consumers to compare results among emerging wearable technology without using a uniform set of standards. This study evaluated the accuracy of commercially available wearable technology heart rate (HR) monitors using the Consumer Technology Association (CTA) standards. Participants (N = 23) simultaneously wore a Polar chest strap (criterion measure), Jabra Elite earbuds, Scosche Rhythm 24 armband, Apple Watch 4, and Garmin Forerunner 735 XT during sitting, activities of daily living, walking, jogging, running, and cycling, totaling 57 min of monitored activity. The Apple Watch mean bias was within ±1 bpm, and mean absolute percent error (MAPE) was <3% in all six conditions. Garmin underestimated HR in all conditions, except cycling and MAPE was >10% during sedentary, lifestyle, walk-jog, and running. The Jabra mean bias was within ±5 bpm for each condition, and MAPE exceeded 10% for walk-jog. The Scosche mean bias was within ±1 bpm and MAPE was <5% for all conditions. In conclusion, only the Apple Watch Series 4 and the Scosche Rhythm 24 displayed acceptable agreement across all conditions. By employing CTA standards, future developers, researchers, and consumers will be able to make true comparisons of accuracy among wearable devices.

Penerapan Metode Single Exponential Smoothing Pada Prediksi Penjualan Bed Sheet

Prediksi merupakan bagian dari awal suatu proses pengambilan suatu keputusan. Dalam kegiatan produksi, prediksi dilakukan untuk menentukan jumlah permintaan terhadap suatu produk dan merupakan langkah awal dari proses perencanaan dan pengendalian produksi. Permasalahan stok barang yang umum terjadi, seperti stok barang yang tidak terjual atau stok barang dengan merk tertentu menjadi kendala yang dihadapi dalam upaya untuk memenuhi kebutuhan pelanggan. Disamping itu, upaya dalam menghasilkan perencanaan dan pengendalian produksi yang baik juga merupakan salahsatu fungsi prediksi dalam kegiatan produksi. Pada penelitian ini diimplementasikan penggunaan metode Single Exponential Smoothing untuk memprediksi stok bedsheet dari berbagai merk berdasarkan data-data penjualan produk tersebut. Metode yang digunakan untuk menghitung kesalahan prediksi yang dihasilkan adalah metode Mean Absolute Percent Error(MAPE). Nilai prediksi ditentukan dari nilai alpha yang paling cocok dari perhitungan kesalahan prediksi hingga menghasilkan nilai yang paling kecil. Data yang digunakan merupakan data penjualan bed sheet periode Februari 2020 sampai dengan Mei 2020 dari 3(tiga) merk yang cukup diminati pelanggan pada tempat studi kasus. Dari hasil perhitungan yang dilakukan hasil perhitungan akurasi prediksi dari beberapa merk bed sheet tersebut mencapai 94.01%.

Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction

Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules

Pengaruh Intensitas Matahari Terhadap Karakteristik Sel Surya Jenis Polycristaline Menggunakan Regresi Linear

Renewable energy sources have both renewable and sustainable properties and the utilization of renewable energy sources is a continuously developed alternative. One of the technologies that can utilize renewable energy is polycrystalline solar cells. Solar cells are technology that converts sunlight energy into electrical energy. This technology is very potentially applied in Indonesia that has a tropical climate, but the main problem of this system is the power instability generated. The power produced relies heavily on the intensity of the sun received by the solar panels. The intensity of the solar radiation received by the solar panels can be maximised by installing solar panels, with a precise tilt angle. In research acquired the relationship between irradiation and current correlates of R = 0.7251. From the correlation value above indicates that there is a strong link and is directly proportional between irradiation and the current obtained. The acquired Model needs to be seen its accuracy, in which case it will be used Mean Absolute Percent Error So it is obtained by 26.5%. This indicates that the model is good enough.

DELAY PREDICTION IN IOT AND TACTILE INTERNET USING MACHINE LEARNING APPROACH

Предложены методы прогнозирования задержки в сетях интернета вещей и тактильного интернета при прогнозировании вперед на несколько шагов MSP (Multi-step ahead Prediction) и один шаг SSP (Single-step ahead Prediction). Использованы нелинейные авторегресионные рекуррентные нейронные сети с внешними входами NARX(NonlinearAutoregressive with Exogenous inputs) для временных рядов. Проведена оценка точности прогнозирования с помощью трех алгоритмов обучения нейронной сети (Trainlm, Traincgf, Trainrp) при использовании в качестве оценок точности прогнозирования среднеквадратичной ошибки RMSE(Root Mean Square Error) и средней абсолютной ошибки в процентах MAPE(Mean Absolute Percent Error). In this paper, we perform the delay prediction in IoT and tactile Internet communication networks using a multistep ahead prediction (MSP) and single-step ahead prediction (SSP) with Time Series NARX (Nonlinear AutoRegressive with eXogenous inputs) Recurrent Neural Networks. The prediction accuracy has been evaluated using three neural network training algorithms (Trainlm, Traincgf, Trainrp) using the RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percent Error) as predictive accuracy measure.

Pengukuran Kapasitas Container Yard Menggunakan Yard Occupancy Ratio dalam Upaya Optimalisasi Penggunaan Lapangan Penumpukan Kontainer di PT XYZ

PT. XYZ is a logistics service company engaged in freight forwarding services for ships / air ships and warehousing. In its operations, the company experienced a problem, namely the flow of containers that entered the company's container yard capacity experienced overcapacity. The purpose of this study was to deter-mine the results of the measurement of container yard capacity using the yard occupancy ratio at PT. XYZ This study uses the Yard Occupancy Ratio (YOR) method to determine the capacity of the container yard, while for forecasting using moving averages and exponential smoothing. Meanwhile, to calculate forecast error using mean squared error and mean absolute percent error. Based on the results of measurements made that the results of forecasting container flows for July 2019 to December 2019 amounted to 1,487 containers, 1,493 con-tainers, 1,614 containers, 1,377 containers, 1,532 containers and 1,495 contain-ers, respectively. Based on the results of the YOR analysis that scenario 3 is the best scenario compared to scenario 1 and scenario 2, because it produces a low-er YOR value, namely for July 2019 at 45%, August 2019 at 45%, September 2019 at 48%, October 2019 at 41%, November 2019 at 46% and December 2019 at 45%. This means that by using the YOR method there will be no overcapasity in the future because the YOR value does not exceed 100%.

Evaluasi Prediksi Curah Hujan dengan Algoritma Backpropogation di BMKG Cilacap

Backpropogation atau biasa disebut dengan backprop adalah algoritma yang mempelajari tentang bagaimana cara memperkecil atau meminimkan tingkat ke-error-an dengan dimenyesuaikannya bobot berdasarkan perbedaan output dan target sesuai dengan yang diinginkan. Penelitian ini akan membahas mengenai prediksi curah hujan bulanan di BMKG Cilacap dengan algoritma Backpropagation. Memprediksi untuk masa depan kadang belum menemukan ketepatan. Oleh sebab itu, maka peramalan harus mampu mengurangi/memperkecil tingkat kesalahannya. Hasil dari penelitian tersebut menyatakan bahwa peramalan curah hujan dengan algoritma backpropagation ini akurat dengan hasil dari Mean Square Error (MSE) adalah 0,011465, Mean Absolute Percent Error (MAPE) adalah 0.3289 pada proses pelatihan jaringan. Pada penilaian MSE dan MAPE untuk proses pengujian secara keseluruhan adalah 0,011807 dan 0,050448.

Peramalan Harga Saham Menggunakan Metode Autoregressive Dan Web Scrapping Pada Indeks Saham Lq45 Dengan Python

Bursa Saham memberikan kemungkinan investor untuk memperoleh keuntungan (capital gain) atau mengalami kerugian (capital loss) dikarenakan harga saham yang berfluktuasi. Ketidakpastian ini bisa disiasati dengan menerapkan metode peramalan untuk memprediksi harga saham di masa datang. Salah satu metode peramalan yang dapat digunakan adalah Autoregressive. Metode ini memanfaatkan data saham di masa lalu untuk mendapatkan formula prediksi di masa datang. History harga saham bisa dilihat secara realtime melalui beberapa laman penyedia data saham. Data ini bisa ditarik secara otomatis dengan menggunakan teknik Web Scrapper, sehingga hasil peramalan dapat diperoleh dengan lebih cepat, mudah, dan akurat. Tingkat akurasi peramalan diukur dengan menggunakan metode MAPE (Mean Absolute Percent Error). Metode ini dipilih karena lebih mudah dipahami oleh para pengguna awam. Hasilnya, aplikasi peramalan mampu menampilkan prediksi harga saham beserta tingkat akurasinya. Data yang diujikan pada penelitian adalah semua data saham LQ45. Tingkat akurasi rata-rata yang diperoleh adalah sebesar 94,62 %. Tingkat akurasi terbesar terdapat pada emiten BKSL dengan nilai persentase 99,92 % dan tingkat akurasi terkecil terdapat pada emiten ASRI dengan nilai persentase 90,13 %.