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: Analisis Sentimen Body Shaming pada Kolom Komentar Instagram Berbahasa Indonesia dengan Metode Naive Bayes
Abstrak: Instagram merupakan media sosial yang berfungsi untuk berbagi foto maupun video kepada sesama penggunanya. Kurangnya pengetahuan etika dalam bermedia sosial memunculkan permasalahan salah satunya adalah tindak body shaming. Informasi yang terkandung dalam suatu komentar didapatkan dengan cara melakukan analisis sentimen menggunakan metode Naïve Bayes. Data yang digunakan dalam penelitian ini sebanyak 9287 data komentar yang diambil menggunakan API Instagram dengan pembagian data 80 % data latih dan 20% data uji. Penelitian ini bertujuan untuk mengklasifikasikan komentar yang merupakan tindak body shaming atau bukan body shaming. Penerapan metode Naïve Bayes pada data komentar Instagram menghasilkan nilai akurasi sebesar 97.78%., Instagram is a social media platform that accommodates photo and videos sharing among its users. The lack of ethical knowledge in social media raises problems, one of which is comments that contain the acts of body shaming. The information contained in a comment is obtained by conducting a sentiment analysis using the Naïve Bayes method. The amount of data used in this study were 9287 comments, taken using the Instagram API with the distribution of 80% training data and 20% testing data. This study aims to classify comments that fall into the category of acts of body shaming and not body shaming. The application of the Naive Bayes method on Instagram comment data produces an accuracy value of 97.78%.
Keyword: Body Shaming, Instagram, Sentiment analysis, Naive bayes

Judul: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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