Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further researchEkosistem mangrove merupakan kawasan yang sangat potensial, umumnya berada di kawasan ekoton (kombinasi kawasan intertidal dan supratidal), dimana terdapat interaksi antara perairan (laut, air payau, dan sungai) dengan kawasan daratan. Indonesia khususnya wilayah Banten dan Jawa Barat memiliki kawasan mangrove yang sangat luas dan saat ini terancam alih fungsi lahan. Apalagi pemetaan sebaran hutan bakau menggunakan platform Google Earth Engine berbasis Cloud Computing kurang dipublikasikan. Oleh karena itu, penelitian ini dilakukan dengan memperkenalkan sebaran hutan mangrove yang melibatkan metode algoritma klasifikasi Random Forest (RF), dan mencari modifikasi indeks yang terbaik. Uji kombinasi dilakukan dengan melibatkan indeks NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, dan LSWI. Sebaran mangrove terdapat di tiga provinsi (Jawa Barat, Banten, dan DKI Jakarta) yaitu seluas 933,54 ha (8,372%), 1.537,89 ha (18,231%), dan 8.184,82 ha (73,397%). Dari 70 pengujian kombinasi, indeks LSWI (K13, Type-A) merupakan kombinasi dengan tingkat akurasi terendah sebesar 58,45% (Overal Accuracy) dan 39,59 (Kappa statistik), dan kombinasi K23 (SAVI-MNDWI-IBI) merupakan kombinasi yang terbaik yaitu 96,48% dan 92,79. Hasil dan rekomendasi dalam penelitian ini diharapkan dapat digunakan sebagai acuan dalam menentukan kebijakan perlindungan kawasan mangrove dan referensi untuk penelitian selanjutnya

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983

Pembelajaran Kitab Mūn-Mūn Dalam Meningkatkan Pemahaman Ilmu Nahwu Dan Sharraf Bagi Santri Mubtadiin Di Pondok Pesantren Al-Muqri As-Salafi Lil Banin Prenduan Sumenep Madura

This article aims to find out the learning of the Mūn-Mūn book in increasing the understanding of Nahwu and Sharraf science for students, and to find out the results of learning the Mūn-Mūn book in increasing the understanding of Nahwu and Sharraf science for students and what are the supporting and inhibiting factors in the teaching and learning process Nahwu science at Al-Muqri As-Salafi Islamic Boarding School Lil Banin Prenduan. This research was designed using a qualitative approach with a descriptive type. Sources of data in the research consisted of primary data, namely caregivers, teachers and students. Meanwhile, secondary data is in the form of documents at the Al-Muqri As-Salafi Islamic Boarding School Lil Banin Prenduan Sumenep. Data collection techniques include interviews, observation and documentation. The data analysis technique uses a single case design with the data analysis model introduced by Miles and Hiberman, namely the data analysis process is carried out starting from the process of data collection, presentation and verification or drawing conclusions. The conclusion of the research is that learning the Mūn-Mūn book in increasing the understanding of Nahwu and Sharraf science for students includes the planning stages, in the form of an initial foundation for mubtadi students to understand which contains the Madura language. The implementation stages are in the form of an emphasis on direct practical learning methods using the yellow book. Assessment/evaluation stages are in the form of routine assessments and summative assessments at the end of the semester. The learning outcomes are that students can understand the books of Alfiah, Imriti, and the book of Naqmul Maqsud, students can understand I'rob and the position of sentences in the yellow book and Mubtadiin students can know the arrangement of Arabic sentences. While the supporting factors are enthusiasm in the teacher and students and practice them. While the inhibiting factors are elderly students due to illness or going home