Judul: Application of Binary Particle Swarm Optimization Support Vector Machine for Cyberbullying Comments Classification on Instagram
Abstrak: Kebebasan berpendapat pada media sosial adakalanya tidak sesuai dengan etika berkomunikasi sehingga mengarah kepada tindakan cyberbullying. Instagram merupakan media sosial yang paling umum digunakan dalam melakukan cyberbullying. Tindakan cyberbullying perlu diminimalisasi karena menimbulkan banyak dampak negatif. Salah satu cara yang dapat dilakukan yaitu dengan mengidentifikasi komentar cyberbullying agar komentar tersebut dapat dihapus secara otomatis. Metode yang digunakan pada penelitian ini yaitu klasifikasi teks menggunakan algoritme Support Vector Machine (SVM) dengan penerapan seleksi fitur menggunakan metode optimasi Binary Particle Swarm Optimization (BPSO). Penelitian ini bertujuan untuk menyusun model klasifikasi komentar cyberbullying serta membandingkan performa kinerja model klasifikasi dengan dan tanpa penerapan seleksi fitur. Hasil penelitian menunjukkan bahwa pemodelan dengan SVM menghasilkan performa klasifikasi yang cukup akurat lebih dari 72% untuk semua performa klasifikasi pada setiap nilai C. Penggunaan BPSO untuk seleksi fitur dapat meningkatkan performa klasifikasi dengan meningkatnya nilai akurasi dan spesifisitas. Akan tetapi untuk kasus pada penelitian ini dipilih model tanpa seleksi fitur pada C = 0,1 karena memiliki nilai sensitivitas paling besar dengan akurasi dan spesifisitas yang cukup baik sehingga dapat mendeteksi komentar cyberbullying secara lebih akurat., Freedom of speech on social media is sometimes inappropriate with the ethics of communicating and has led to cyberbullying. Instagram is the most commonly used social media in cyberbullying. Cyberbullying needs to be minimized because it has many adverse effects. One way that can be done is by identifying cyberbullying comments so those comments can be deleted automatically. The method used in this study is text classification using Support Vector Machine (SVM) algorithm with the application of Binary Particle Swarm Optimization (BPSO) optimization method as features selection. The study aims to build a cyberbullying comments classification model and compare the classification model performance with and without the application of features selection. The experimental results showed that modeling with SVM produces a reasonably accurate classification performance over 72% for all classification performance on each C. The application of BPSO for features selection can improve classification performance by increasing accuracy and specificity. However, the model without features selection on C = 0,1 is chosen in this study case because it has the highest sensitivity with good accuracy and specificity that can detect cyberbullying comments more accurately.
Keyword: binary particle swarm optimization, cyberbullying, feature selection, support vector machine, text mining

Judul: Optimisasi Parameter Support Vector Machine Menggunakan Algoritme Particle Swarm Optimization
Abstrak: Support vector machine (SVM) is a popular classification method which is known to have a robust generalization capability. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. Kernel trick uses various kinds of kernel functions, such as: linear kernel, polynomial, radial basis function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. In this research, we propose a combination of the SVM algorithm and particle swarm optimization (PSO) to search for the kernel parameters with the best accuracy. We use data from UCI repository of machine learning database: Diabetes, Image Letter Recognition and Yeast. The results indicate that the combination of SVM and PSO is effective in improving the accuracy of classification. PSO has been shown to be effective in systematically finding optimal kernel parameters for SVM, as a replacement for randomly choosing kernel parameters. Best accuracy for each data has been improved from previous research: 78.26% for Diabetes, 97.07% for Letter and 58.21% for Yeast. However, for bigger data sizes, this method may become impractical due to the high time requirement.
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Judul: Pengusahaan kelapa sawit ( Elaeis guineensis Jacq. ) di kebun Aek Nabara Selatan PT Hapinis dan NV Oriental, Medan Sumatera Utara
Abstrak: Kelapa sawit (Elaeis guineensis Jacq.) merupakan ta- naman asli Afrika. Pada tahun 1848, empat bibit tanaman kelapa savit (lebih kurang berumur 1 tahun) untuk pertama kalinya dibawa ke Indonesia dan ditanam di Kebun Raya Bo- gor. Dua diterima dari koleksi kebun Hertus Botanicus Amsterdam dan dua lagi dari Bourbon (atau Mauritius). Ke- empat tanaman itu dapat hidup survive dan masing-masing tanaman menghasilkan banyak biji, dengan tinggi tanaman 21 m. Tanaman tersebut juga menunjukkan tipe yang sangat seragam (uniform). Dari kebun Raya Bogor inilah biji ke- lapa sawit disebarluaskan dan ditanam pada perkebunan- perkebunan di Indonesia (Sutedjo, 1963; BPP Medan, 1975; Hartley, 1977). Perkebunan kelapa sawit mulai berkembang pada tahun 1911 yang seluruhnya terdapat di Sumatra dengan luas areal 170 000 ha (BPP Medan, 1975). Tanaman kelapa sawit merupa- kan salah satu tanaman utama perkebunan di Indonesia, yang mempunyai masa depan yang cukup cerah di samping karet, teh, coklat, tebu dan kelapa...
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