Judul: Analisis biaya produksi papan sambung di PT bumi mahaphala wilasa Parungkuda-Sukabumi
Abstrak: Penelitian ini bertujuan untuk mempelajari proses propapan sambung dalam kaitannya dengan faktor-faktor duksi yang mempengaruhi biaya produksi, menganalisis struktur biaya produksi papan sambung berdasarkan keadaan perusahaan, mengetahui tingkat Break Even Point (BEP) pada sistem produksi PT Bumi Mahaphala Wilasa, mengetahui tingkat efisiensi dan produktivitas pada masing-masing proses produksi yang dicapai oleh perusahaan. Dari hasil perhitungan bulan Maret 1991 didapatkan biaya total produksi papan sambung di PT Bumi Mahapahala Wilasa sebesar Rp 251,445,580,- atau Rp 683,045,-/M³, dengan perincian sebagai berikut : a. Biaya tetap Rp 67,408,549,- atau Rp 683,045,-/M³ b. Biaya variabel Rp 184,037,031, atau Rp 499,932,-/M³ Berdasarkan analisis Break Even Point (BEP), maka peru- sahaan akan mendapatkan keuntungan bila memproduksi papan sambung lebih dari 307.2816 M³/bulan atau 3,687.4023 M³/tahun. PT Bumi Mahaphala Wilasa telah menghasilkan papan sambung melebihi tingkat Break Even Point (BEP), yaitu sebesar 368.1242 M³/bulan atau 4,417.4904 M³/tahun. Nilai tambah yang diperoleh perusahaan dengan memproduksi papan sambung pada bulan Maret 1991 adalah sebesar ..dst
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Judul: Analisis Biaya Kayu Lapis Studi Kasus Di PT. Sari Bumi Kusuma, Kalimantan Barat
Abstrak: INTRODUCTION: PT. Sari Bunli Kusuma is one of plywood industry that was occured work decreasing impact. How big these impacts was assessed by analizing production cost, which is one of the important components in a decision making of company. The production cost was then calculated as well as Break Even Point (BEP), Return on Investment (ROI), and profitability rates. MATERIAL AND METHOD: The production cost that consists of fixed cost and variable cost was determined for each stage of production. Fixed costs include depreciation, salary, building and machine maintenance, etc. Variable costs include raw material cost, labourage cost, material cost, etc. Coupled with product prices, profitability rates, Break Even Point, Return On Investment were then calculated. RESULT: The production cost of plywood in 2008 at PT. Sari Bumi Kusuma were total fixed costs of Rp 30,24 billion per year, the total variable costs of Rp 4,16 million per m3 . Of the types of costs can be seen above the huge production costs for the year 2008 PT. Sari Bumi Kusuma was Rp 296,88 billion per year or Rp 4,63 million per m3 • Meanwhile, for 2009 (January to March) the total fixed costs of Rp 6,40 billion per year, and the total variable costs of Rp 3,68 million per m3 • Of the types of costs may also above the great unknown of production costs for, 2009 PT. Sari Burni Kusuma was Rp 67,95 billion per year or Rp 4,06 million per m3 . Rate of return on investment (ROI) companies in 2008 reached 5,97% and 4,31% for the year 2009. Break Even Point (BEP) at the company achieved production levels 36.105,89 m3/year and 7.961,78 m3/year in 2009. Since material cost was the largest component of plywood production cost, firm competitiveness could be improved by handling materials more properly.
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Judul: Aplikasi Mobile untuk Identifikasi Emosi Manusia Berbasis Teks pada Jejaring Sosial Twitter dengan Klasifikasi Decision Tree
Abstrak: Humans are social beings that need to communicate with others. Humans in communication often reveal emotions either directly or indirectly. Awareness of people’s emotions when communicating is one of the reasons of emotion recognition. Emotion recognitions divided into 3 categories, namely physiological signals, facial expressions, and voice. However the existing method has limitations in use or cost. A potential solution is affective computing, which can recognize emotion through typing patterns by collecting and analyzing user behavior and context from touchscreen, accelerometer, and GPS on the smartphone. This method was implemented as an Android app on Twitter social networking service. This research used decision tree classification method to identify 7 types of emotions i.e. happiness, surprise, anger, disgust, sadness, fear, and neutral by using 10 features. The data used in this study consisted 300 datasets. The highest accuracy in the research produced 44%.
Keyword: emotion recognition, decision tree, affective computing