Energy is one of the basic needs for human being. One of the most vital energy sources is electricity. Electricity is a type of energy that sustains survival of human being, more particularly in industrial sector. Efficiency in industrial sector refers to a state where electricity is used to as little as possible to produce the same amount of product. The case study was conducted in marine commodity sector, anchovy and jellyfish supplier. The supplier was classified as SME that installed 33,000 VA electric powers (B2). The data were in the form of energy consumption intensity (ECI) and specific energy consumption (SEC) to determine the energy efficiency level. The objective of the study was to classify the efficiency level of electricity consumption using Sugeno Fuzzy method. The findings of the study were 1) the average ECI between January, 2016 and April, 2017 was 1,949 kWh/m2; it was classified as efficient; 2) the average SEC at the same period was 126,108 kWh/ton; it was classified as excessive. Sugeno Fuzzy logic was implemented to determine efficiency level of electricity in this company. Based on the average ECI and SEC, the electricity consumption of the company was categorized as excessive with FIS Sugeno output of 0.803.

Road is one of the transportation infrastructure which is very important for vehicle in riding activity. The vehicle is growing every year, road infastructure should be getting attention for comfortable and safety in riding. However, there are still many apprehensive road condition in the form of damaged roads, especially potholes. One of the problem in repairing road is road damage detection process which is done manually by the human, by this way the process needs a longer time. This research develops road damage detection system by using image processing and road damage mapping which are saved in database. This system uses webcam for capturing the road in real time, webcam is located in font of vehicle and GPS module is used for marking road damage location which is controlled by arduino uno microcontroller.

The development of technology, information and communication provides a new alternative to predict cow weight through Image Processing. This study utilizes Image Processing in visualizing the measurement of Chest Circumference and cow body length automatically. The cow weight estimation are very dependent on cow image segmentation result. Image segmentation method used in this study is local adaptive thresholding combined with the Connected Component Labeling (CCL) method. The implementation of the Chest Circumference and Body Length endpoints in the foreground is converted into centimeters (cm) to ensure cow weight estimation can be calculated using the Lambourne formula. In this study, the accuracy of RMSE was obtained from the cow weight data taken at 150, 170 and 190 cm distance. The accuracy is 20.35, 30.77 and 23.33 respectively. This research can be contribution to development of local cattle farms in Indonesia.

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Keberhasilan proses klasifikasi daun tembakau sangat bergantung pada ekstrasi fitur daun tembakau.  Beberapa tahapan pengolahan citra digital dapat meningkatkan kemampuan dalam mengidentifikasi tembakau kualitas terbaik secara otomatis melalui ekstrasi fitur tekstur daun. Penelitian ini bertujuan untuk mengaplikasikan sistem ekstrasi fitur tekstur daun menggunakan metode Discrete Cosine Transform. Hasil Klasifikasi menjadi tolak ukur akurasi keberhasilan sistem dalam mengesktrasi fitur tekstur terbaik. Klasifikasi daun tembakau menuntut pengetahuan yang luas dan terminologi yang kompleks, bahkan grader profesional memerlukan waktu yang signifikan di bidang ini untuk penguasaan subjek. Hal ini disebabkan karena daun tembakau biasanya dianggap memiliki karakteristik yang berguna untuk identifikasi kualitas tembakau dimana ekstrasi fitur yang tepat melalui citra daun dapat dianggap sebagai masalah penelitian yang berperan penting untuk klasifikasi. Penelitian yang diusulkan bertujuan untuk menemukan model ekstrasi yang sesuai untuk mendapatkan fitur warna melalui konversi ruang warna YcbCr dan tekstur daun tembakau yang diperoleh dari transformasi ruang frekwensi Discrete Cosine Transform. Sedangkan untuk tahap klasifikasi menggunakan metode maximum likelihood. Hasil uji coba menunjukkan akurasi keberhasilan dalam pengklasifikasin daun tembakau sebesar 90 % melalui ekstrasi 12 fitur.