Prediksi cacat software adalah salah satu studi pada bidang Rekayasa Perangkat Lunak yang telah diteliti oleh banyak peneliti. Tujuan dari studi ini adalah untuk mencari tahu algoritma yang dapat memberikan kinerja prediksi cacat software yang lebih baik. Salah satu penelitian yang telah dilakukan adalah melakukan prediksi cacat software dengan menggunakan algoritma berbasis pohon seperti Decision Tree, Random Forest dan Deep Forest. Deep Forest adalah algoritma klasifikasi berbasis pohon yang baru yang merupakan perbaikan dari algoritma Random Forest. Namun implementasi Deep Forest dalam penelitian terdahulu masih belum memberikan kinerja yang maksimal. Hasil pada penelitian terdahulu menunjukan bahwa kinerja algoritma Deep Forest masih ada yang lebih rendah dibandingkan algoritma berbasis pohon yang lain. Pada penelitian ini berfokus pada peningkatan kinerja algoritma berbasis pohon dengan melakukan normalisasi pada dataset dan hyperparameter tuning pada algoritma klasifikasi dengan menggunakan pencarian grid. Dataset yang digunakan adalah 3 dataset dari ReLink yaitu Apache, Safe, dan Zxing. Setiap model prediksi divalidasi dengan Stratified 10-Fold Cross Validation dan kinerja dievaluasi menggunakan AUC. Dari hasil eksperimen yang didapatkan,hasil prediksi dari pendekatan yang diusulkan lebih baik daripada metode sebelumnya.

Guru sebagai individu tenaga pendidik dan profesional khususnya guru Pegawai Negeri Sipil (PNS) diharapkan dapat peningkatan pangkat dan jabatannya. Untuk meningkatkan pangkat dan jabatan tersebut perlu memiliki syarat, yaitu salah satunya karya ilmiah. Karya ilmiah yang memenuhi syarat adalah karya ilmiah yang diterbitkan oleh penerbit dimana harus terdapat dewan redaksinya atau suatu lembaga pemerintah yang disebarluaskan kepada masyarakat. Kurangnya pengetahuan tentang tata cara publikasi karya ilmiah secara online merupakan salah satu penyebab kurangnya karya ilmiah yang dihasilkan guru. Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk memberikan pengetahuan dan keterampilan kepada guru tetnag proses pendaftaran dan submit karya ilmiah melalui Open Journal System (OJS). Pengabdian ini ditunjukan kepada tenaga pendidik, yaitu guru PNS yang memiliki pangkat fungsional dan ingin melakukan kenaikan. Metode yang digunakan untuk pelatihan dan pendampingan ini adalah penyampaian teori dan praktek langsung menggunakan OJS. Target serta luaran dari kegiatan ini adalah: (1) Terlaksananya kegiatan pelatihan pengelolaan dan penggunaan jurnal bagi guru SMPN 8 Banjarbaru (2) Meningkatkan kemampuan guru dalam memahami proses penerbitan karya ilmiah. Berdasarkan hasil evaluasi yang dilakukan setelah kegiatan ini, sekitar 80% peserta dapat melakukan proses pendaftaran dan submit karya ilmiah pada OJS yang telah disediakan.Teachers, as professional educators, are required to increase their rank and position. There are requirements to improve ranks and positions and scientific work. Published scientific papers are works published by a publisher with an editorial board or a government agency and disseminated to the public. Lack of knowledge about the procedures for publishing scientific papers online is one of the causes of teachers' lack of scientific papers. This Community Service aims to provide knowledge and skills to teachers regarding the registration process and submitting scientific papers through the Open Journal System (OJS). This service is aimed at educators, in this case, educators (teachers) who have functional ranks, most of whom currently have not received registration training from OJS publishers. The method used for this training and mentoring is the delivery of theory and direct practise using OJS. The targets and outcomes of this activity are: (1) the implementation of training in the management and use of journals for teachers at SMPN 8 Banjarbaru; (2) Improving teachers' understanding of the process of publishing scientific papers. Based on the evaluation results carried out after this activity, around 80% of participants were able to register and submit scientific papers to the Open Journal System provided.

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.

Researchers have collected Twitter data to study a wide range of topics, one of which is a natural disaster. A social network sensor was developed in existing research to filter natural disaster information from direct eyewitnesses, none eyewitnesses, and non-natural disaster information. It can be used as a tool for early warning or monitoring when natural disasters occur. The main component of the social network sensor is the text tweet classification. Similar to text classification research in general, the challenge is the feature extraction method to convert Twitter text into structured data. The strategy commonly used is vector space representation. However, it has the potential to produce high dimension data. This research focuses on the feature extraction method to resolve high dimension data issues. We propose a hybrid approach of word2vec-based and lexicon-based feature extraction to produce new features. The Experiment result shows that the proposed method has fewer features and improves classification performance with an average AUC value of 0.84, and the number of features is 150. The value is obtained by using only the word2vec-based method. In the end, this research shows that lexicon-based did not influence the improvement in the performance of social network sensor predictions in natural disasters. HIGHLIGHTS Implementation of text classification is generally only used to perform sentiment analysis, it is still rare to use it to perform text classification for use in determining direct eyewitnesses in cases of natural disasters One of the common problems in text mining research is the extracted features from the vector space representation method generate high dimension data A hybrid approach of word2vec-based and lexicon-based feature extraction experiment was conducted in order to find a method that can generate new features with low dimensions and also improve the classification performance GRAPHICAL ABSTRACT

<p><em>Intrusion Detection System</em> merupakan suatu sistem yang dikembangkan untuk memantau dan memfilter aktivitas jaringan dengan mengidentifikasi serangan. Karena jumlah data yang perlu diperiksa oleh IDS sangat besar dan banyaknya fitur-fitur asing yang dapat membuat proses analisis menjadi sulit untuk mendeteksi pola perilaku yang mencurigakan, maka IDS perlu mengurangi jumlah data yang akan diproses dengan cara mengurangi fitur yang dapat dilakukan dengan seleksi fitur. Pada penelitian ini mengkombinasikan dua metode perangkingan fitur yaitu <em>Information Gain Ratio </em>dan <em>Correlation </em>dan mengklasifikasikannya menggunakan algoritma <em>K-Nearest Neighbor</em>. Hasil perankingan dari kedua metode dibagi menjadi dua kelompok. Pada kelompok pertama dicari nilai mediannya dan untuk kelompok kedua dihapus. Lalu dilakukan klasifikasi <em>K-Nearest Neighbor</em> dengan menggunakan 10 kali validasi silang dan dilakukan pengujian dengan nilai k=5. Penerapan pemodelan yang diusulkan menghasilkan akurasi tertinggi sebesar 99.61%. Sedangkan untuk akurasi tanpa seleksi fitur menghasilkan akurasi tertinggi sebesar 99.59%.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>Intrusion Detection System is a system that was developed for monitoring and filtering activity in network with identified of attack. Because of the amount of the data that need to be checked by IDS is very large and many foreign feature that can make the analysis process difficult for detection suspicious pattern of behavior, so that IDS need for reduce amount of the data to be processed by reducing features that can be done by feature selection. In this study, combines two methods of feature ranking is Information Gain Ratio and Correlation and classify it using K-Nearest Neighbor algorithm. The result of feature ranking from the both methods divided into two groups. in the first group searched for the median value and in the second group is removed. Then do the classification of  K-Nearest Neighbor using 10 fold cross validation and do the tests with values k=5. The result of the  proposed modelling produce the highest accuracy of 99.61%. While the highest accuracy value of the not using the feature selection is 99.59%.</em></p>