This research proposes MedLeaf as a new mobile application for medicinal plants identification based on leaf image. The application runs on the Android operating system. MedLeaf has two main functionalities, i.e. medicinal plants identification and document searching of medicinal plant. We used Local Binary Pattern to extract leaf texture and Probabilistic Neural Network to classify the image. In this research, we used30 species of Indonesian medicinal plants and each species consists of 48 digital leaf images. To evaluate user satisfaction of the application we used questionnaire based on heuristic evaluation. The evaluation result shows that MedLeaf is promising for medicinal plants identification. MedLeaf will help botanical garden or natural reserve park management to identify medicinal plant, discover new plant species, plant taxonomy and so on. Also, it will help individual, groups and communities to find unused and undeveloped their skill to optimize the potential of medicinal plants. As the results, MedLeaf will increase of their resources, capitals, and economic wealth.

Aplikasi Jobstreet merupakan sebuah aplikasi lowongan pekerjaan yang sudah didownload oleh lebih dari 10 juta masyarakat yang menyediakan beberapa jenis pekerjaan seperti akuntansi, sumber daya manusia, pemasaran, komunikasi, pelayanan, dan lainnya. Dengan banyaknya masyarakat yang mendownload aplikasi ini maka masyarakat pasti memberikan ulasan-ulasan mereka terhadap aplikasi ini. Di masa pandemi seperti ini, banyak orang yang mencari pekerjaan menggunakan aplikasi android dimana informasinya lebih cepat dan mudah untuk mencari lowongan pekerjaan, oleh karena itu aplikasi Jobstreet membantu masyarakat dalam mencari lowongan pekerjaan di perusahaan yang mereka inginkan. Ulasan komentar opini masyarakat ini bisa dijadikan peluang untuk menggali keterangan tentang evaluasi dan penilaian atas pelayanan aplikasi jobstreet yang telah berjalan menggunakan analisis sentimen. Tujuan dari penelitian ini adalah melakukan klasifikasi sentimen terhadap ulasan pada aplikasi Jobstreet dengan metode Naïve Bayes. Dalam penelitian ini opini akan dibagi kedalam dua golongan sebagai positif dan negatif, kemudian diklasifikasikan dengan menggunakan algoritma Naïve Bayes. Hasil pengujian yang didapat menggunakan data uji memiliki nilai akurasi sebesar 0,96; nilai precision sebesar 0,98; nilai recall sebesar 0,94.

This research will be conducted at the Solusi Hijau Main Garbage Bank in the Gunung Sindur District, Bogor Regency. The Solusi Hijau Waste Bank still uses a manual recording system that is easily lost, inefficient, and not dynamic. Therefore it is necessary to make a Web-based Waste Savings Application that can become the main activity facility for the Green Solutions Main Garbage Bank. This application will be used as an interactive interface for Customers and Unit Waste Banks as well as the general public as Prospective Customers or Prospective Management Unit Waste Banks so that they can maximize the performance of the Waste Bank in its development. The Web-based Waste Savings application is a continuation of basic research that has been done before, namely to create digital applications that are utilized by waste banks and the community so that waste banks can become a forum for public education in terms of waste handling and environmental management as well as generating community economic resources. Systems Development Life Cycle (SDLC) is implemented as an Application development method. SDLC is a system development cycle in making waste bank applications by applying the waterfall method. The results of this research in the future are directed to be developed into a standard for Waste Saving System Applications in the community.