Abstract. Irwansyah RM, Azzahra SIN, Darmastuti SA, Ramadhandi AR, Firdaus O, Daeni F, Safitri N, Fajri OPA, Nugroho GN, Naim DM, Setyawan AD. 2021. Crab diversity and crab potential as support ecotourism in Teleng Ria, Grindulu and Siwil Beach, Pacitan, East Java, Indonesia. Intl J Bonorowo Wetlands 11: 75-83. The mangrove area in Pacitan District, Pacitan, East Java, Indonesia is generally not polluted. Therefore, the land is suitable for growing conditions from mangrove plants and has great potential as a mangrove area with ecotourism management in Indonesia. Ecotourism activities in mangrove areas, in principle, are the use of mangrove areas while maintaining the biological/ecological functions of mangrove areas that have social and economic value for local community. The mangrove ecosystem is a habitat of various species of Crustacea, such as the crab. Crabs are live in coastal/mangrove ecosystems and one of the key species that have a very important role in maintaining the balance of the ecosystem. The study about diversity of crabs in the mangrove area is very important because it will improve the quality of mangrove and potentially support ecotourism in the mangrove ecosystem. This study aims to determine the diversity of the crab and its potential to support ecotourism if the Pacitan Beaches will be ecotourism in the future. This research was conducted in Teleng Ria Beach, Grindulu Beach, and Siwil Beach, Pacitan, East Java, Indonesia in November 2021. Plots of 10 x 10 m2 are made to record the species and the number of individual crab species. The result found five species of crab,i.e.  Austruca annulipes (H. Milne-Edwards, 1837), Coenobita perlatus (H. Milne-Edwards, 1837), Ocypode kuhlii (De Haan, 1835), Perisesarma guttatum (A. Milne-Edwards, 1869), and Scylla serrata (Forsskål, 1775). The total crab diversity index of 1.25 is included in the medium category. The morphology, activity, number of individuals and distribution of each crabs species in an ecotourism area will increase the attractiveness of tourists to visit. For example, the morphology of C. perlatus that has the red color as a strawberry sometimes has a home/the shells of the Mollusca which color and unique shape so that add appeal to be seen. Then, the crab A. annulipes that like to dance and play the violin with the claw can also be attractions drawing tourists. Hopefully, the data can be a reference for the managers of the mangrove area in developing ecotourism and conservation of mangrove forest.

Nowadays, mechanical experiments such as spring oscillations for high schools and colleges have been developed using smartphones. This study aims to compare the use of acceleration sensors and video tracker available on smartphones for free and damped spring oscillation experiments. This experiment used varying load masses. In experiments using acceleration sensors, acceleration versus time data were obtained, while experiments using a video tracker got position versus time data. The data were then calculated to obtain the spring constant value and the damping coefficient are obtained. The experimental results showed that for experiments using the acceleration sensor, the spring constant values k = (11.8 ± 0.2) N m−1. For experiments using a video tracker the spring constant values k = (12.1 ± 0.4) N m−1. Experiments using the acceleration sensor can explain the acceleration trends that change periodically whereas when we use a video tracker it explains the trends in positions that change periodically. But, based on the standard deviation, showing that experiments using acceleration sensors obtain more precise results. We hope that mechanical experiment using smartphone acceleration sensors can be applied in physics laboratories on high schools and colleges, because that is easy, inexpensive, and also the results are more precise.

Penyediaan data distribusi mangrove serta perubahannya membutuhkan waktu pemrosesan yang lama jika dilakukan dengan interpretasi citra secara konvensional, apalagi jika dilakukan pada area yang luas seperti Kabupaten Kubu Raya. Hadirnya platform yang bernama Google Earth Engine (GEE) bisa menjadi solusi permasalahan tersebut. GEE mempunyai akses data yang besar, mampu mengolah data berbasis cloud serta memiliki banyak algoritma machine learning. Oleh karena itu penelitian ini mencoba memetakan mangrove di Kabupaten Kubu Raya menggunakan machine learning yang tersedia di GEE, selain itu kami juga membahas beberapa future work terkait pemetaan mangrove di Kabupaten Kubu Raya menggunakan GEE. Machine learning yang digunakan dalam penelitian ini antara lain: CART, Random Forest, GMO Max Entropy, Voting SVM, Margin SVM. Hasil penelitian ini menunjukkan bahwa machine learning yang terbaik dalam memetakan mangrove di Kabupaten Kubu Raya adalah CART. Random Forest juga menjadi machine learning dengan akurasi tertinggi setelah CART, baik keduanya merupakan machine learning berbasis logika atau juga disebut machine learning berbasis pohon keputusan. Dari beberapa studi juga mendukung bahwa machine learning ini sangat cocok digunakan untuk pemetaan penutup lahan. Hasil pemetaan mangrove ini memiliki akurasi kappa yang baik walaupun masih terdapat misklasifikasi sehingga perlu dilakukan sentuhan manual seperti interpretasi visual. Penelitian ini masih terdapat banyak keterbatasan sehingga perlu dikembangkan penelitian dengan menggunakan input data yang lebih beragam dan pengujian hyperparamater antar machine learning.

Informasi penutup lahan merupakan data yang sangat penting dalam pengelolaan Daerah Aliran Sungai (DAS). Tantangan dalam penyediaan informasi penutup lahan di DAS Kreo adalah tutupan awan dan cangkupan areanya yang cukup luas. Hadirnya platform pengolahan data spasial berbasis cloud yaitu Google Earth Engine (GEE) bisa menjawab tantangan tersebut. Oleh karena itu penelitian ini bertujuan untuk memetakan penutup lahan di DAS Kreo menggunakan klasifikasi berbasis machine learning pada GEE. Proses pemetaan penutup lahan di DAS Kreo menggunakan citra satelit Landsat 8 dan DEM SRTM. Input data yang digunakan antara lain band 1 sampai 7 pada citra Landsat 8, transformasi NDVI dan NDBI serta nilai elevasi dari DEM SRTM. Adapun tahun yang dipilih adalah tahun 2015 dan 2020 dengan machine learning yang diujikan meliputi CART, Random forest dan Voting SVM. Hasil penelitian ini menunjukkan bahwa machine learning yang terbaik dalam memetakan penutup lahan di DAS Kreo adalah Random forest. Penelitian ini masih terdapat banyak keterbatasan terutama kelas penutup lahan yang dipetakan.

In the initial topic in basic physics in school and college, the concept of analysing a measurement error needs to be understood by students. This paper proposes the use of various sensors on smartphones for statistical error analysis which usually uses the classical method with repeated measurements. The smartphone sensors used were light, acceleration, and magnetometer sensors. The data from the sensor recording fluctuations for 10 s were analysed for the simple statistical figures and error. The experiment used three different conditions, namely placing the smartphone on a stable table, holding it with your hand, and bringing it closer to a notebook that played a 650 Hz tones. The experimental findings, the experimental conditions (environmental and blunder), and the specifications of the instrument affect the error. Students also can tell that the sensor takes repeated measurements so that it shows fluctuation data. This research contributes to offering the use of the latest technology, namely smartphones, for statistical error analysis for physics students.