Analisis Kelayakan Finansial pada Industri Baglog Jamur untuk Peningkatan Produksi 30.000 Unit per Bulan pada Agroindustri

The agricultural sector is the main structure of national economic development. One of the sub-sectors in agriculture that has an important role for the welfare of farmers and the regional economy as well as the national economy is the Horticulture sub-sector. This sub-sector has been supported by the agro-industry. One of the products from this agro-industry is baglog of mushrooms. This study aims to analyze the financial feasibility of increasing production capacity from 20,000 to 30,000 baglogs of mushrooms per month. It caused the average demand is 29,070 baglogs of mushrooms per month. The research object is the manufacture of mushroom baglog. Descriptive quantitative with financial feasibility analysis techniques and sensitivity analysis is used as a method in this research. The results found that the investment period of 5 years required an investment cost of IDR 244,342,677. Financial feasibility analysis with a discount rate of 9% produces an NPV value of IDR 257,886,281, an IRR of 44.594%, a Profitability index of 2.06 and a Payback Period of 1.88 years. The results of the sensitivity analysis of an increase in the cost of raw materials by 17% and a decrease in production of 9% obtained the results of NPV > 0, IRR > 9%, Profitability index > 1 and Payback period MARR

TF-IDF-Enhanced Genetic Algorithm Untuk Extractive Automatic Text Summarization

AbstrakPenelitian ini mengusulkan sebuah implementasi terkait dengan automasi peringkasan teks bertipe ekstraktif dengan menggunakan metode TF-IDF-EGA. Dimana dalam permasalahan peringkasan teks dibutuhkan suatu solusi untuk meringkas teks dengan kalimat ringkasan yang dapat merepresentasikan keseluruhan teks yang ada. Algoritma TF-IDF dikenal mampu untuk menghasilkan ringkasan teks berdasarkan skor yang didapat pada setiap kalimat dalam teks. Namun hasil dari TF-IDF terkadang didapati hasil ringkasan yang terdiri dari kalimat yang tidak deskriptif, hal ini dikarenakan dalam peringkasannya TF-IDF hanya memilih beberapa kalimat yang memiliki skor tertinggi dan biasanya kalimat dengan skor tertinggi merupakan kalimat yang berisi kata-kata penting/kata-kata ilmiah tertentu sehingga kalimatnya tidak deskriptif. Algoritma EGA mampu untuk mengatasi permasalahan tersebut dengan cara memilih kalimat ringkasan yang memiliki nilai probabilitas tertentu sebagai hasil peringkasan teks.Kata kunci: peringkasan teks, automasi ekstraktif, TF-IDF, EGA, algoritma evolusi, meta-heuristik.AbstractThis research proposed an implementation related to extractive automatic text summarization using TF-IDF-EGA method. Which in summarization problem required a solution to summarize text with a sentence summary that could represent the whole data text. TF-IDF algorithm was usually known to be used for generating summary by its sentence scores. However the result from TF-IDF tends to generate a summary that consist of non descriptive sentences, this is due its summarize that only choose sentences with maximum score and usually sentences with maximum score is consist of significant words on a form of scientific word. EGA could solve this problem by choosing summary by its sentence probability values as a the whole text summary.Keywords: text summarization, extractive automation, TF-IDF, EGA, evolutionary algorithm, meta-heuristic

TF-IDF-Enhanced Genetic Algorithm Untuk Extractive Automatic Text Summarization

Abstrak Penelitian ini mengusulkan sebuah implementasi terkait dengan automasi peringkasan teks bertipe ekstraktif dengan menggunakan metode TF-IDF-EGA. Dimana dalam permasalahan peringkasan teks dibutuhkan suatu solusi untuk meringkas teks dengan kalimat ringkasan yang dapat merepresentasikan keseluruhan teks yang ada. Algoritma TF-IDF dikenal mampu untuk menghasilkan ringkasan teks berdasarkan skor yang didapat pada setiap kalimat dalam teks. Namun hasil dari TF-IDF terkadang didapati hasil ringkasan yang terdiri dari kalimat yang tidak deskriptif, hal ini dikarenakan dalam peringkasannya TF-IDF hanya memilih beberapa kalimat yang memiliki skor tertinggi dan biasanya kalimat dengan skor tertinggi merupakan kalimat yang berisi kata-kata penting/kata-kata ilmiah tertentu sehingga kalimatnya tidak deskriptif. Algoritma EGA mampu untuk mengatasi permasalahan tersebut dengan cara memilih kalimat ringkasan yang memiliki nilai probabilitas tertentu sebagai hasil peringkasan teks. Kata kunci: peringkasan teks, automasi ekstraktif, TF-IDF, EGA, algoritma evolusi, meta-heuristik. Abstract This research proposed an implementation related to extractive automatic text summarization using TF-IDF-EGA method. Which in summarization problem required a solution to summarize text with a sentence summary that could represent the whole data text. TF-IDF algorithm was usually known to be used for generating summary by its sentence scores. However the result from TF-IDF tends to generate a summary that consist of non descriptive sentences, this is due its summarize that only choose sentences with maximum score and usually sentences with maximum score is consist of significant words on a form of scientific word. EGA could solve this problem by choosing summary by its sentence probability values as a the whole text summary. Keywords: text summarization, extractive automation, TF-IDF, EGA, evolutionary algorithm, meta-heuristic