SISTEM INFORMASI PERENCANAAN PRODUKSI DAN PENJADWALAN POLA TANAM HORTIKULTURA DENGAN MODEL LINEAR PROGRAMMING DAN FUZZY TIME SERIES

Penelitian tentang perencanaan produksi telah banyak dilakukan, tesis ini menyajikan sistem informasi perencanaan produksi dan penjadwalan pola tanam yang dihadapi oleh petani hortikultura dengan mengkombinasikan dua metode. Metode fuzzy time series digunakan untuk memprediksi jumlah permintaan dan hasil dari metode fuzzy time series menjadi salah satu variabel pada perhitungan Linear Programming. Kombinasi kedua metode ini tepat mewakili dan mendukung pengambilan keputusan penentuan jadwal penanaman dalam kegiatan pertanian hortikultura dengan menggunakan variabel pendukung, data permintaan, data produksi, data jumlah tenaga kerja, data luas lahan, data keuntungan produksi, data jumlah bibit dan data lama tanam, studi kasus yang digunakan adalah tanaman jamur dengan pengambilan data di “Rumah Jamur”. Sistem informasi perencanaan produksi dan penjadwalan pola tanam ini dapat memberikan rekomendasi pola tanam dan jumlah jamur yang harus ditanam dalam satu periode oleh pemilik “Rumah Jamur”, siklus hidup jamur dalam satu periode adalah empat bulan, jumlah penanaman disesuaikan dengan jumlah permintaan yang ada yang sebelumnya telah diprediksi dengan menggunakan fuzzy time series, hasil menunjukan dari empat skenario selang tanam didapatkan nilai pada skenario pertama jarak penanaman satu bulan Rp 5.327.266,00, pada skenario kedua, jarak penanaman dua bulan Rp 6.426.950,00, nilai skenario ketiga, jarak penanaman tiga bulan dengan nilai Rp 11.200.000,00, dan jarak penanaman empat bulan dengan nilai Rp 8.742.400,00 berdasarkan hasil skenario satu, dua, tiga dan empat didapatkan nilai optimal pada skenario ke tiga Rp 11.200.000,00 dengan penanaman bibit jamur tidak semua ditanam di awal, tetapi dipecah dengan penanaman bibit berikutnya diberi jarak tiga bulan sebanyak penanaman bulan pertama 775, kedua 972, ketiga 1172, dan keempat 836. Kata Kunci : Sistem Informasi, Perencanaan Produksi, Penjadwalan Pola Tanam, Fuzzy Time Series, Linear Programming. Research on production planning has been widely performed, This thesis presents the information system production planning and planting patterns scheduling faced by horticulture farmer by combining two methods. Fuzzy time series method used to predict demand. The result of fuzzy time series method will be one of variables in Linear Programming calculation. Combination of both of these methods appropriately represent and support decision making determination of planting schedule in horticulture farming activities by using variable data demand, production, amount of farmers, size of areas, production advantage, amount of seeds and age of the plant, the case study used is mushroom plant with data collection at “Rumah Jamur”. Production planning and planting patterns scheduling information system give planting patterns recommendation and how much mushroom must be planted in one periods by the owner of “Rumah Jamur”, age of mushroom in one period is four months, planting mushroom be adjusted with demand which had previously been predicted by using fuzzy time series, the result is show for four scenario hose planting the value of profit first scenario is Rp 5.327.266,00, second scenario is Rp 6.426.950,00, third scenario is Rp 11.200.000,00, and fourth scenario is Rp 8.742.400,00, based on four scenarios the optimal profit value in third scenario Rp 11.200.000,00 with planting of mushroom divided every three months, in the first month is 775 seeds, in the second month 972 seeds, in third month 1172 seeds and the last month is 836 seeds. Keywords : Production Planning; Information System; Scheduling Planting Patterns; Fuzzy Time Series; Linear Programming

APPLICATION OF FUZZY TIME SERIES WITH FIBONACCI RETRACEMENT FOR FORECASTING STOCK PRICE PT. BANK RAKYAT INDONESIA

Stock can be defined as securities that indicate the ownership of a person or legal entity to the company issuing the shares. Good stocks for long-term investment are stocks that have good fundamentals and large market capitalization. The purpose of investing is to make a profit. In investing in stocks, investors need to know the risk management that can affect the ups and downs of a stock. Forecasting or forecasting is an analysis to predict everything related to the production, supply, demand, and use of technology in an industry or business. One of the forecasting methods is using fuzzy time series. The primary purpose of fuzzy time series is to predict time series data that can widely use on any real-time data, including capital market data. In this study, we will discuss the evolution of the time series model in overcoming fluctuations that often occur in stock prices by using a fuzzy time series that combines a stock analysis approach, namely Fibonacci retracement. The stock data used in this study is the close price of BBRI for October 2021 to March 2022. Forecasting results for 1 April 2022 are IDR 4660.49 with a Mean Absolute Percentage forecasting accuracy value of 1.034%

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules. Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy model, future forecas

Sistem Perkiraan Penggunaan Listrik Rumah Tangga Menggunakan Logika Fuzzy (Studi Kasus: PLN Area Pasuruan)

Di salah satu wilayah Indonesia yakni Pasuruan, di beberapa tahun terakhir telah terjadi peningkatan pesat dalam pertumbuhan ekonomi, sehingga terjadi peningkatan yang besar terhadap kebutuhan energi listrik sampai melampaui skenario yang semula direncanakan oleh pemerintah. Sistem kelistrikan kota Pasuruan sendiri merupakan sistem kelistrikan yang kompleks dimana terdapat kesulitan dalam memperkirakan besar pemakaian listrik yang dapat mempengaruhi kesiapan dari unit pembangkit untuk menyediakan pasokan listrik kepada konsumen. Berdasarkan pertimangan tersebut, maka perlu dilakukan perkiraan penggunaan listrik jangka panjang terutama untuk sektor rumah tangga dalam melakukan perencanaan penambahan pembangkit listrik yang baru, perluasan jaringan distribusi dan kebutuhan perencanaan penjadwalan pengoperasian pembangkit energi listrik, agar daya yang dibangkitkan sesuai dengan kebutuhan beban. Pada penelitian ini digunakan metode logika fuzzy untuk melakukan perkiraan atau peramalan. Data yang digunakan sebanyak 70 data histori dari bulan Januari 2012 sampai dengan Oktoer 2017 didapatkan dari PLN Area Pasuruan. Hasil implementasi dan pengujian akurasi pada penelitian ini mendapatkan nilai parameter terbaik dengan hasil nilai MSE terendah sebesar 1,602823095 dan MAPE 3,84%. Pengujian yang dilakukan mendapatkan jumlah fuzzy set terbaik pada nilai 16, sedangkan nilai terburuk sejumlah 7 fuzzy set

Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results

Peramalan Butuhan Hidup Minimum Menggunakan Automatic Clustering dan Fuzzy Logical Relationship

Kebutuhan hidup minimum (KHM) adalah standar kebutuhan seorang pekerja atau lanjang untuk dapat hidup layak secara fisik untuk kebutuhan satu bulan. Selain itu KHM berpengaruh terhadap upah minum provinsi dan kota. Oleh karena itu diperlukan suatu peramalan KHM untuk mengetahui nilai KHM di tahun yang akan datang. Peramalan ini bermanfaat untuk perusahaan dalam merencanakan keuangan perusahaan tahun depan. Dalam melakukan peramalan KHM menggunakan metode automatic clustering dan fuzzy logical relationship. Automatic clustering digunakan untuk membentuk sub-interval dari data time series yang ada. Sedangkan fuzzy logical relationship digunakan untuk melakukan peramalan KHM berdasarkan relasi fuzzy yang telah dikelompokan. Automatic clustering dapat menghasilkan cluster-cluster yang sangat baik sehingga dalam melakukan peramalan dalam fuzzy logical relationship memberikan akurasi yang tinggi. Dalam menghitung kesalahan menggunakan mean squere error (MSE), nilai kesalahan semakin berkurang ketika diterapkan automatic clustering dalam fuzzy logical relationship. Hasil peramalan memiliki nilai koefisien korelasi yang hampir mendekati satu

A refined approach for forecasting based on neutrosophic time series

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series

The effects of graphed a stock indicator on a traditional control chart X and S: perspectives, analysis of the operability of the indicator and assignable causes to make decisions in the market

[ES] El presente artículo estructura indicadores bursátiles de tal forma que puedan ser analizados por medio de un gráfico tradicional de control de procesos Shewhart. Un gráfico muy utilizado en la industria para controlar variables altamente correlacionadas con procesos. Estos gráficos tienen el fin de asignar causas a posibles cambios en el comportamiento normal de los datos. De igual forma, cualquier intento por obtener puntos de vista estadísticos que ayuden a tomar decisiones de inversión con bajo riesgo es bienvenido, ya que un error en la toma de decisiones en el mercado bursátil puede conllevar a una gran pérdida económica para los inversionistas. En el presente documento se muestran los resultados de lo analizado y se relacionan las recomendaciones para construir un gráfico de control Shewhart X y S con indicadores bursátiles, con el fin de definir sus prestaciones.[EN] This article structures stock market indicators for that they can be analyzed using a traditional Shewhart process control chart. A graph widely used in the industry to control variables highly correlated with processes. These graphs seek to assign causes to possible changes in the normal behavior of the data. On the other hand, any attempt to obtain statistical points of view that help to make low-risk investment decisions is welcome, since an error in decision-making in the stock market can lead to a great economic loss for investors. This document shows the results of the analysis and the recommendations to build a Shewhart X and S control chart with stock market indicators, in order to define its benefits.Neira-Rueda, J.; Carrión García, A.; Romero-Arenis, W. (2020). Los efectos de graficar un indicador bursátil en un gráfico de control tradicional X y S: perspectivas, análisis de la operatividad del indicador y causas asignables para tomar decisiones en el mercado. Revista UIS Ingenierías. 19(4):223-237. https://doi.org/10.18273/revuin.v19n4-2020019S22323719

Prediksi Produksi Biofarmaka Menggunakan Model Fuzzy Time Series dengan Pendekatan Percentage Change dan Frequency Based Partition

Masa depan biofarmasi semakin cerah. Akibat mahalnya harga obat modern, maka permintaan tanaman obat meningkat di dalam dan luar negeri. Hal ini karena biofarmaka banyak digunakan di industri lain, seperti makanan, minuman, dan kosmetik. Konsumen di seluruh dunia termasuk di Indonesia bergerak menuju produk makanan dan kesehatan yang lebih sehat dengan slogan "kembali ke alam". Dengan demikian permintaan tanaman obat sebagai bahan baku industri lainnya juga meningkat. Untuk mengatasi masalah tersebut diperlukan suatu prediksi untuk menentukan besaran kenaikan atau penurunan jumlah produksi komoditas strategis biofarmaka untuk beberapa tahun ke depan, sehingga Memungkinkan analisis pergerakan tren dari perkembangan data sebelumnya. Saat ini belum dijumpai studi peramalan deret waktu untuk memprediksi produksi biofarmaka dengan tingkat akurasi baik. Dalam eksperimen ini kami mengusulkan model peramalan fuzzy time series berdasarkan pendekatan percentage change sebagai himpunan semesta dan frequency-based partition yang dapat memberikan tingkat akurasi peramalan yang tinggi. Prediksi difokuskan pada biofarmaka untuk empat jenis rimpang yaitu Jahe, Lengkuas, Kencur, dan Kunyit yang dinilai menjadi prioritas utama pengembangan tanaman obat di Indonesia. Dalam penelitian ini menggunakan data sekunder yang diperoleh dari Badan Pusat Statistika tahun 1997-2020. Tujuan dari survei adalah untuk memprediksi dan menganalisa perkembangan produksi biofarmaka untuk empat jenis rimpang. Hasil prediksi menunjukan akurasi luar biasa dengan nilai Mean Absolute Percentage Error yang sangat kecil yakni Jahe 0,03%, Lengkuas 0,02%, Kencur 0,14%, dan Kunyit 0,03%. Dengan demikian hasil eksperimen ini dapat berkontribusi dan digunakan bagi pihak yang berkompeten untuk membantu dalam menentukan kebijakan strategis di masa depan. AbstractBiopharmaceuticals' future is brightening. Due to the exorbitant cost of modern treatment, the desire for medicinal herbs is growing. due to their widespread use in different industries such as food, beverages, and cosmetics. Consumers worldwide, especially in Indonesia, are gravitating towards healthier food and health goods. So the demand for medicinal plants as raw materials increases. To solve this issue, a forecast is required for the next few years on the increase or decline in production of strategic biopharmaca commodities. Currently, no reliable time series forecasting study exists for biopharmaca production. To achieve high predicting accuracy, we present a fuzzy time series forecasting model based on percentage change as a universal set and frequency-based partition. Ginger, Galangal, Kencur, and turmeric are predicted to be the most important rhizomes for biopharmaca research in Indonesia. Secondary statistics from the Central Statistics Agency for 1997–2020 This study's goal was to anticipate and analyze biopharmaca synthesis in four rhizomes. The prediction results are incredibly accurate, with Mean Absolute Percentage Error values of just 0.03%, 0.02%, 0.14%, and 0.03% for Ginger, Galangal, Kencur, and Turmeric, respectively. Thus, competent parties can use the outcomes of this experiment to help determine future strategic policies

Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms