Judul: Klasifikasi Pemukiman Kumuh di Wilayah DKI Jakarta Menggunakan Decision Tree
Abstrak: Jakarta as the capital of Indonesia has a high level of urbanization, causing a high rate of population density. High population growth in a small area tends to create a slum in the area. So far, the Statistics Central Bureau (BPS) has conducted a survey of the slum areas, but the method used is still quite subjective. To reduce the problem of subjectivity, this study aims to apply Decision Tree in classifying the slum areas in Jakarta and determine the accuracy of the classification process. The data used in this study consisted of 10 parameters, namely, population density, building layout, construction of houses, residential ventilation, building density, state of roads, drainage/sewerage, water consumption, human waste disposal, and waste management. There were 320 sample data collected using stratified random sampling. The data were divided into 2 groups: training data and testing data. Training data were used to construct a Decision Tree, while testing data were used to test the tree. Testing process used 10-fold cross validation resulting in an accuracy of 69.7%, 68.8%, and 67.4% for confidence factors of 0.1, 0.2, and 0.3, respectively.
Keyword: Classification, Decision Tree, Slum Area, Stratified Random Sampling

Judul: Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta
Abstrak: DKI Jakarta merupakan pusat pemerintahan serta pusat ekonomi dan bisnis di Indonesia sehingga kegiatan pembangunan di Jakarta terus berlanjut setiap tahun. Oleh karenanya, pengawasan terhadap penggunaan lahan wajib ditingkatkan agar sesuai dengan Rencana Tata Ruang DKI Jakarta. Usaha tersebut perlu ditunjang dengan tersedianya data yang kontinu mengenai kondisi tutupan lahan di Jakarta. Proses pengumpulan data tersebut menjadi lebih mudah berkat perkembangan teknologi pengindraan jauh. Teknologi pengindraan jauh dapat dimanfaatkan untuk menganalisis luas penggunaan lahan dengan menggunakan analisis klasifikasi. Penelitian sebelumnya menunjukkan bahwa jenis metode klasifikasi dan banyaknya data training akan berpengaruh terhadap tingkat akurasi. Penelitian ini bertujuan untuk mengkaji tingkat akurasi dari metode Quadratic Discriminant Analysis (QDA) dan Support Vector Machine (SVM). Selain itu, penelitian ini juga mengkaji banyaknya data training yang digunakan dalam klasifikasi tutupan lahan di DKI Jakarta tahun 2017. Hasil penelitian menunjukkan bahwa pada kedua metode, tingkat keragaman dari overall accuracy, sensitivity, dan specificity semakin kecil seiring dengan semakin banyak data training yang digunakan. Berdasarkan nilai overall accuracy dan specificity, metode QDA dan SVM memiliki kinerja yang relatif sama. Namun, metode SVM lebih baik daripada QDA berdasarkan nilai sensitivity-nya.
Keyword: klasifikasi, diskriminan, jakarta, support vector machine, tutupan lahan

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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