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All Journal Majalah Ilmiah Teknologi Elektro Tekinfo | Scientific Journal of Industrial and Information Engineering Majalah Ilmiah Teknologi Elektro
Dian Retno Sawitri
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Model dan Simulasi Alur Rantai Pasok Sampah Organik Menjadi Pakan Ternak Lele Ratih Setyaningrum; Dewi Agustini; Rudy Tjahyono; Dian Retno Sawitri; Tambah Ayu Arida
Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi Vol 8 No 2 (2020)
Publisher : Program Studi Teknik Industri Universitas Setia Budi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31001/tekinfo.v8i2.800

Abstract

The most widely generated waste is organic waste, which is 60% compared to other types of waste. So far, NRC has been able to process organic waste to be used as animal feed products for catfish or pellets. The production capacity that can be carried out by the NRC company is 150 kg to 200 kg per day, but the organic waste obtained at this time has not been able to reach the required production capacity. Based on this, it is necessary to propose a better organic waste supply chain model for NRC companies. The purpose of this study will be to map the supply chain of organic waste into catfish feed. The supply chain model made is the current supply chain and the proposed supply chain. The final output of the study will compare current conditions and proposed supply chain models. The simulation results with arena software show the acquisition of organic waste as much as 352 kg. That means organic waste obtained by the NRC company from the 4 waste banks is 352 kg. Recommended supply chain recommendations have 4 waste bank suppliers, with each bank having a minimum of 3 waste producers.
PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE) Sari Ayu Wulandari; Rudy Tjahyono; Dian Retno Sawitri
Jurnal Teknologi Elektro Vol 15 No 1 (2016): (January - June) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2016.v15i01p17

Abstract

Abstract— Pattern recoqnition methods for image of diabetic retinopaty are influenced by differences in pigmentation. To help diabetic retinopathy image recognition is required a software. This paper presents the results of research on pattern recognition image of diabetic retinopathy,This study used the image of the yellow canal with Gabor filter.Characteristics that are taken from each image is characteristic of the mean,variance, skewness and entropy, followed by feature extraction with PCA (Principle Component Analysis).At PCA feature extraction, square matrix whose number of columns equal to the number of features is enerated.There are four features used. These features are 4 PCs (Principle Component), ie, PC1, PC2, PC3 and PC4.From the combination of these features, we obtained six pairs that consist of two traits. By using a linear model of SVM will been selected the pair with the highest accuracy value. Based on the analysis, we obtained a couple PC1and PC2 models that have the highest levels of learning (100%) and the fastest recognition time, which is explicitly indicated by the smallest amount of support vector.
PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE) Sari Ayu Wulandari; Rudy Tjahyono; Dian Retno Sawitri
Jurnal Teknologi Elektro Vol 15 No 1 (2016): (January - June) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2016.v15i01p17

Abstract

Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w), dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means), variasi Varians), skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri PCA (Principle Com­­ponent Analysis). Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent), PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks. DOI: 10.24843/MITE.1501.17
Co-Authors Dewi Agustini Ratih Setyaningrum Rudy Tjahyono Sari Ayu Wulandari Tambah Ayu Arida