The advancement of machine learning and natural language processing techniques hold essential opportunities to improve the existing software engineering activities, including the requirements engineering activity. Instead of manually reading all submitted user feedback to understand the evolving requirements of their product, developers could use the help of an automatic text classification program to reduce the required effort. Many supervised machine learning approaches have already been used in many fields of text classification and show promising results in terms of performance. This paper aims to implement NLP techniques for the basic text preprocessing, which then are followed by traditional (non-deep learning) machine learning classification algorithms, which are the Logistics Regression, Decision Tree, Multinomial Naïve Bayes, K-Nearest Neighbors, Linear SVC, and Random Forest classifier. Finally, the performance of each algorithm to classify the feedback in our dataset into several categories is evaluated using three F1 Score metrics, the macro-, micro-, and weighted-average F1 Score. Results show that generally, Logistics Regression is the most suitable classifier in most cases, followed by Linear SVC. However, the performance gap is not large, and with different configurations and requirements, other classifiers could perform equally or even better.

The problem of Indonesian language errors among students is of particular observation. This problem becomes an important concern for students majoring in journalism because one day the graduates will become journalists. A language error filtering application has been developed that can be used quickly and accurately in journalists’ work. This application, which involves statistical analysis, computational language, and artificial intelligence, is named U-Tapis. This study was aimed at finding out the feasibility and effectiveness measures of the U-Tapis model by focusing on the language of students’ journalistic works such as opinions, news items, and news articles. The study involved 30 students majoring in Journalism, a private university in Jakarta, Indonesia. It was found that the students’ error rate decreased after the use of the model. It can be concluded that, in addition to eligibility which reaches 92.31%, the U-Tapis application can help effectively increase students’ proficiency in the use of the Indonesian language.

Profesi jurnalis masih menjadi harapan bagi sebagian kalangan milenial. Namun lanskap industri media di Indonesia mengalami perubahan cepat dalam beberapa tahun terakhir akibat guncangan digital. Sebagian media konvensional bertransformasi ke digital, media yang lain berjuang dengan menerapkan strategi konvergensi media. Media yang tak mampu berkompetisi memutuskan tutup, di mana sejak 2014 keputusan penutupan media di Indonesia kerap diikuti pemutusan hubungan kerja para jurnalis. Ini berdampak pada masa depan para jurnalis hingga kesejahteraannya. Riset ini menemukan bahwa sebagian jurnalis yang terkena dampak pemutusan kerja ini masih berusia di antara 24 – 35 tahun dengan masa kerja di bidang jurnalistik di atas 1 tahun. Sebagian besar jurnalis ini tidak bergabung dalam serikat pekerja yang memiliki posisi tawar dan dapat bernegosiasi dengan perusahaan media di kala terjadi sengketa ketenagakerjaan. Di samping itu, kurun 2014 hingga 2017, bagi sebagian jurnalis, adalah masa-masa yang sulit untuk beradaptasi di tengah peralihan dari media konvensional ke media baru yang serba digital. Kata Kunci: guncangan digital, jurnalis, generasi milenial, penutupan media, pemutusan hubungan kerja

Representasi masyarakat adat kurang mendapat perhatian (voiceless) dari negara. Untuk mengatasi hal tersebut, masyarakat adat Dayak di Kalimantan Barat mengembangkan saluran komunikasi alternatif melalui program RuaiSMS yang dikelola oleh RuaiTV, sebuah stasiun TV lokal. RuaiSMS merupakan praktik jurnalisme warga berbasis SMS. Di Indonesia kajian tentang jurnalisme alternatif berbasis teknologi komunikasi bergerak (mobile communication technology), seperti telepon seluler, masih terbatas. Padahal penetrasi telepon seluler di Indonesia sangat tinggi. Makalah ini menguraikan bagaimana jurnalisme SMS diaplikasikan sebagai instrumen advokasi oleh masyarakat adat Dayak di Kalimantan Barat. Hasil riset ini menemukan, RuaiSMS telah membantu masyarakat adat dalam menuntut hak-hak politiknya di hadapan kuasa negara dan kuasa modal. Kata Kunci: jurnalisme warga, mobile communication, sms, telepon seluler