The Information and Documentation Management Officer (PPID) application was built as an application to meet the needs of information management and services by Public Bodies for the implementation of Law No. 14 of 2008 about Public Information Disclosure (UU KIP). This application assists public bodies in documenting and serving requests for information to the public. With the launch of the PPIPD application on the Google Play Store, it raises many opinions or responses from application users through the review feature. The reviews are quite many and unstructured and contain opinions from users about their satisfaction with the application. The feedback obtained from users is not only positive but also negative. Users often make complaints about applications that have been used or provide suggestions for features in the application. User reviews are of great interest to the owners of the application to decide the future. Sentiment analysis is an activity used to analyze a person's opinion or opinion on a topic. The Support Vector Machine (SVM) method is a text mining method that includes a classification method and the term Frequency-Inverse Document Frequency (TF-IDF) is a character weighting method. SVM and TF-IDF can be used to analyze sentiment based on user reviews of PPID apps on the Google Play Store. The purpose of this study was to classify user reviews of PPID apps on the Google Play Store using sentiment analysis that has been collected and filtered. The reviews totaled 700 data with labels of 85 positive and 615 negative. And the results of the analysis using SVM produced an average k-fold of 88%, precision of 94%, recall of 100%, f-measure of 97%, and accuracy of 97%. Keywords: PPID; Sentiment Analysis, Support Vector Machine, TF-IDF

Pelanggaran lalu lintas tidak dapat diabaikan sebab sebagian besar kasus terjadinya kecelakaan diakibatkan oleh faktor pengendara yang enggan mematuhi tata tertib lalu lintas. Di wilayah Kabupaten Majalengka masih terdapat banyak kecelakaan dan pelanggaran tata tertib lalu lintas, salah satu cara untuk meminimalisir terjadinya pelanggaran lalu lintas tersebut yaitu dengan menerapkan sistem ETLE (Electronic Traffic Law Enforcement). ETLE merupakan sistem tilang secara otomatis menggunakan kamera pemantau yang ditempatkan di lampu merah rawan terjadinya pelanggaran. Di Kabupaten Majalengka sistem ini masih dalam tahap uji coba yang diterapkan di lampu merah Abok, untuk menentukan lampu merah yang akan diiplementasikan berikutnya diperlukan suatu sistem yang dapat menentukan prioritas lokasi berdasarkan tingkat kepentingannya. Sistem penentuan yang digunakan pada penelitian ini yaitu sistem pendukung keputusan. Metode yang digunakan dalam penelitian ini adalah metode Oreste. Metode ini mengadopsi Besson Rank, Besson Rank merupakan pendekatan untuk membuat skala prioritas berdasarkan ratarata. Penelitian ini bertujuan untuk membuat sistem penentuan penerapan lokasi ETLE otomatis berbasis web dengan menerapkan metode Oreste. Implementasi metode Oreste dalam penentuan lokasi pemasangan ETLE mampu memberikan hasil akhir secara tepat dan akurat serta sistem ini berhasil menerapkan proses perangkingan berdasarkan pendekatan rata-rata dari faktor-faktor penentu lokasi ETLE terhadap setiap lokasi yang akan diterapkan ETLE.

The amount of News displayed on online news portals. Often does not indicate the topic being discussed, but the News can be read and analyzed. You can find the main issues and trends in the News being discussed. It would be best if you had a quick and efficient way to find trending topics in the News. One of the methods that can be used to solve this problem is topic modeling. Theme modeling is necessary to allow users to easily and quickly understand modern themes' development. One of the algorithms in topic modeling is the Latent Dirichlet Allocation (LDA). This research stage begins with data collection, preprocessing, n-gram formation, dictionary representation, weighting, topic model validation, topic model formation, and topic modeling results.            Based on the results of the topic evaluation, the. The best value of topic modeling using coherence was related to the number of passes. The number of topics produced 20 keys, five cases with a 0.53 coherence value. It can be said to be relatively stable based on the standard coherence value.

<p>Ruangguru is an online non-formal education application in Indonesia. There are several appealing features that encourage students to study online. The app's release on the Google Play Store will assist app developers in receiving feedback through the review feature.Users submit various topics and comments about Ruangguru in the review feature of Ruangguru, making it difficult to manually identify public sentiments and topics of conversation. Opinions submitted by users on the review feature are interesting to research further. This study aims to classify user opinions into positive and negative classes and model topics in both classes. Topic modeling aims to find out the topics that are often discussed in each class. The stages of this study include data collection, data cleaning, data transformation, and data classification with the Support Vector Machine method and the Latent Dirichlet Allocation method for topic modeling. The results of topic modeling with the LDA method in each positive and negative class can be seen from the coherence value. Namely, the higher the coherence value of a topic, the easier the topic is interpreted by humans. The testing process in this study used Confusion Matrix and ROUGE. The results of model performance testing using the Confusion Matrix are shown with accuracy, precision, recall, and f-measure values of 0.9, 0.9, 0.9, and 0.89, respectively. The results of model performance testing using ROUGE resulted in the highest recall, precision, and f-measure of 1, 0.84, and 0.91. The highest coherence value is found in the 20th topic, with a value of 0.318. Using the Support Vector Machine and Latent Dirichlet Allocation algorithms are considered adequate for sentiment analysis and topic modeling for the Ruangguru application.</p>

Misinformasi di media sosial menjadi masalah besar di era digital saat ini. Ini tidak hanya menyebarkan informasi palsu tetapi juga merusak kredibilitas berita dan sumber informasi. Membangun keterampilan literasi digital untuk menyanggah misinformasi di media sosial sangat penting bagi individu untuk membedakan antara informasi yang kredibel dan tidak kredibel. Informasi yang salah di media sosial sering kali disebarkan melalui narasi yang bermuatan emosional, tajuk utama yang sensasional, serta gambar dan video yang menyesatkan. Strategi- strategi ini dirancang untuk memanipulasi emosi dan keyakinan orang dan mungkin sulit untuk dilawan. Pemikiran kritis, literasi media, dan kewargaan digital adalah keterampilan literasi digital yang penting bagi individu untuk dikembangkan agar dapat memerangi kesalahan informasi di media sosial secara efektif. Selain itu, pentingnya memanfaatkan alat seperti situs web pengecekan fakta, evaluasi sumber, dan sumber daya pendidikan literasi media. Alat dan sumber daya ini dapat membantu individu mengidentifikasi dan menangkal misinformasi di platform media sosial. Individu dapat menerapkan keterampilan dan alat literasi digital untuk memerangi kesalahan informasi di media sosial secara efektif. Dengan membangun dasar keterampilan literasi digital yang kuat, individu dapat meningkatkan kemampuan mereka untuk mengevaluasi informasi secara online secara kritis dan membuat keputusan berdasarkan informasi. Ini, pada gilirannya, dapat menghasilkan masyarakat digital yang lebih terinformasi dan terlibat