Clustering and Visualizing Surabaya Citizen Aspirations by Using Text Mining. Case Study: Media Center Surabaya

This research aims to identify and visualize the topics of citizen opinion about Surabaya City, Indonesia. Data used in this research is Surabaya citizen opinion taken from Media Center Surabaya. The topics were obtained by using clustering method. The pre-processing data, by cleaning the noise; i.e. basic operations and cleaning, stemming, and feature extraction, is primarily assigned to reach the goal. The optimum number of clusters was determined by using the K-Means clustering by calculating the Silhouette value and Calinski-Harabasz Index (CHI) of 2 until 18 clusters. The most optimum clusters were determined by considering the highest silhouette value and CHI. This research compared four options of pre-processing data. They are pre-processing with basic cleaning and operations; stemming, basic cleaning and operations; LDA, basic cleaning and operations; and stemming, LDA, and basic cleaning and operations. The result showed that pre-processing by using LDA as feature extraction performs the best result. Feature extraction improves the cluster result but stemming process seems to give no significant difference. Additionally, this proposed method offers 15 clusters as the optimum number of clusters which were most mentioned topics by Surabaya citizen. Furthermore, the clusters were visualized by using word clouds to highlight the more frequent appeared words. They are government service, trash, ID card, illegal parking area, government program and information., streetlights, street, computer training (BLC) by Surabaya government, potholes, administration letter, media center, online service for administration, education, clean water distribution, service hour by government. The result attempts the information for the Surabaya government of which sector that citizen most concerned about which mostly related to the street problems. For instances traffic jam, road construction, illegal parking area, street lights, and potholes. Moreover, the result encourages collaboration between public and government to concern and solve those problems

Flagging clickbait in Indonesian online news websites using fine-tuned transformers

Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding layer is then combined with a 100 node-hidden layer and topped with a sigmoid classifier was trained to detect clickbait headlines. With a total of 6,632 headlines as a training dataset, the classifier performed remarkably well. Evaluated with 5-fold cross-validation, it has an accuracy score of 0.914, an F1-score of 0.914, a precision score of 0.916, and a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. The usage of multilingual BERT in the Indonesian text classification task was tested and is possible to be enhanced further. Future possibilities, societal impact, and limitations of clickbait detection are discussed

Implementasi Dana Desa Sebagai Upaya Intervensi Penurunan Stunting Desa Pandan Wangi Kecamatan Diwek Kabupaten Jombang Implementation of Village Fund as Stunting Reduction Intervention in Pandan Wangi Village, Diwek District, Jombang Regency, Indonesia

Stunting adalah kondisi gagal pertumbuhan pada anak balita (pertumbuhan tubuh dan otak) akibat kekurangan gizi dalam waktu yang lama, sehingga anak menjadi lebih pendek dibandingkan dengan anak seusianya. Kekurangan gizi kronis terjadi sejak bayi dalam kandungan hingga anak berusia dua tahun. Periode 1000 hari pertama kehidupan harus mendapat perhatian khusus karena menjadi penentu tingkat pertumbuhan fisik, kecerdasan, dan produktivitas seseorang di masa depan. Penelitian ini dilakukan sebagai upaya untuk mengetahui bagaimana Implementasi Dana Desa sebagai Intervensi pendukukung penurunan Stunting di Desa Pandan Wangi Kecamatan Diwek Kabupaten Jombang. Penelitian ini merupakan penelitian deskriptif kualitatif dengan mengambil data primer melalui wawancara dari informan yang sudah dipilih berdasarkan kepakaran yang dimiliki sebanyak 5 orang responden dan juga mengambil data sekunder untuk di analisis. Hasil penelitian menghasilkan temuan bahwa Implementasi dana Desa sebagai upaya mendukung Intervensi penurunan Stunting sesuai dengan indikator capaian intervensi gizi spesifik dan intervensi gizi sensitif berhasil menurunkan jumlah balita stunting sebanyak 56 balita stunting di tahun 2021 dan berkurang sehingga bisa diturunkan menjadi 35 bayi stunting di bulan Nopember 2022