Judul: Algoritma Pohon Keputusan Pembelajar Mesin (Decision Tree of Machine Learning) dalam Mendeteksi Hutan Mangrove Menggunakan Citra SPOT 7
Abstrak: Tulisan ini menerangkan tentang pembangunan algoritma klasifikasi non-parametrik berbasis pohon keputusan dari pembelajar mesin dalam mengidentifikasi tutupan pada ekosistem mangrove menggunakan data penginderaan jauh dan data geospasial. Tujuan dari penelitian ini adalah untuk menemukan peubah (atribut) serta parameter algoritma pohon keputusan dalam mendeteksi dan mengidentifikasi tutupan hutan mangrove berbasis citra resolusi tinggi SPOT 7. Kajian ini menguji peubah-peubah geo-sosio-biofisik dan peubah spektral yang diturunkan dari citra SPOT 7. Penelitian ini menemukan bahwa peubah paling berpengaruh adalah elevasi dan NDVI. Penelitian ini juga menemukan parameter dari algoritma pohon keputusan terbaik adalah gain ratio tanpa kombinasi pemangkasan (pruning) dan pra-pangkas (prepruning), kedalaman pohon 31, alternatif prepruning 30, sampling data split dengan otomatis dan ukuran daun 61. Kombinasi tersebut menghasilkan overall accuracy (OA) sebesar 93.7% dan kappa accuracy (KA) 93.2%. Penelitian ini menyimpulkan bahwa peubah geo-sosio-biofisik lebih handal perannya dalam mengidentifikasi kelas-kelas tutupan lahan yang dibangun dibandingkan peubah indeks vegetasi., This paper describes the development of a decision tree machine learning, a non-parametric classification algorithm for identifying mangrove forest using remote sensing and geospatial data. The study objective is to find the decision tree algorithm's variables (attributes) and parameters in detecting mangrove forest cover based on SPOT 7 high-resolution imagery. This study examined geo-socio-biophysicalvariables and spectral variables derived from SPOT 7 imagery. This study also found that the best decision tree algorithm parameters were the gain ratio without a combination of pruning and pre-pruning, tree depth of 31, alternative prepruning 30, split data sampling with automatic, and leaf size of 41. The algorithm provided an overall accuracy (OA) of 93.7% and a Kappa accuracy (KA) of 93.2%. This study concludes that the geo-socio-biophysicalvariable is more reliable than vegetation index variables in identifying built-up land cover classes.
Keyword: indeks vegetasi, geo-sosio-biofisik, mesin pembelajar, pohon keputusan, decision tree, machine learning, geo-socio-biophysics, vegetation index

Judul: Study on the Decision Tree of Machine Learning  Algorithm for Cocoa Agroforestry and Monoculture Cocoa Detection Using SPOT-7 Imagery in Two Village Typologies
Abstrak: Tulisan ini membahas pembangunan algoritma klasifikasi pembelajaran mesin pohon keputusan untuk deteksi agroforestri kakao dan tutupan lahan lainnya. Tujuan penelitian ini adalah membangun algoritma decision tree terbaik dan perbedaannya dalam pengklasifikasian agroforestri kakao dan tutupan lahan lainnya di dua tipologi desa yang berbeda menggunakan atribut bio-sosio geofisik dan spektral yang diturunkan dari citra SPOT-7. Hasil penelitian menemukan bahwa atribut yang paling berpengaruh pada tipologi I adalah NDVI sehingga menjadi root node dengan kappa accuracy (KA) 91,30%, sedangkan pada tipologi III adalah GARI dengan KA 92,00%. Atribut lain yang paling berpengaruh pada tipologi I adalah ARVI, elevasi, dan jarak dari pemukiman, sedangkan pada tipologi III, NRGI, elevasi, dan jarak dari pemukiman. Algoritma terbaik pada tipologi I adalah gini index dengan parameter ukuran daun 21, menggunakan prunning, tanpa preprunning, alternative prunning 40, dan dengan automatic sampling, sedangkan algoritma terbaik pada tipologi III adalah information gain dengan parameter ukuran daun 11, tanpa prunning dan preprunning, alternative prunning nol, dan dengan stratified sampling. Hasil pengujian atribut secara terpisah menyimpulkan bahwa atribut bio-sosio geofisik lebih andal dan menghasilkan akurasi yang lebih tinggi dibandingkan atribut spektral. Namun demikian, apabila kedua atribut digabungkan akan menghasilkan hasil yang lebih akurat, This paper discusses the development of a decision tree of machine learning classification algorithm for detecting agroforestry cocoa and other land cover. The research objective is to build the best decision tree algorithm and its differences for classifying cocoa agroforestry and other land cover in two village typologies using geo-socio biophysical and spectral attributes derived from SPOT-7 Imagery. This research found that the most impact attribute in typology I was NDVI, with a KA of 91,4%, while in typology III, it was GARI, with a KA of 92,0%. Other attributes having the most impact in Typology I are ARVI, elevation, and distance from settlements, while in typology III, NRGI, elevation, and distance from settlements. The best algorithm in typology I is the gini index with parameter leaf size 21, using pruning, without pre-pruning, alternative pruning 40, and with automatic sampling, while the best algorithm in typology III is information gain with parameter leaf size 11, without pruning and pre-pruning, alternative pruning nol, and with stratified sampling. The results of the separate attribute tests concluded that the bio socio-geophysical attributes are more reliable and provide higher accuracy than the spectral attributes. However, if the two attributes are combined, it will produce more accurate results.
Keyword: pohon keputusan, indeks vegetasi, bio-sosio geofisik, tipologi, decision tree, vegetation index, bio-socio geophysical, typology

Judul: Evaluation of Influenza Antibody Titers in Serum and Egg Yolk of Layers Vaccinated with Influenza Vaccines.
Abstrak: Influenza belongs to the Orthomyxoviridae family which causes upper and lower respiratory tract infection. The aim of this study was to evaluate influenza-A specific antibody titers from serum and egg yolk of hens vaccinated with quadrivalent influenza vaccines. Three Hy-line Brown layers were vaccinated with quadrivalent influenza type A and B inactivated vaccines. Vaccination was carried out three times at a dose of 0.25 ml/chicken via the intramuscular route. The first vaccination was carried out at 24 weeks of age, then boosters were performed at 28 and 32 weeks of age. Serum and egg yolk were collected every week for 20 weeks since the first vaccination. Influenza A H3N2 and H1N1 antibody titers were determined using hemagglutination-inhibition (HI) test and agar-gel-precipitation (AGP) test. Influenza-A H3N2 and H1N1 specific antibodies were detected in both serum and egg yolk from the first to twenty weeks after the initial vaccination. Highest HI geometric mean titer (GMT) of H1N1 antibody reached 26.67 HIU at nine weeks post first vaccination (p.f.v), while H3N2 antibody reached 26 HIU at six weeks p.f.v. Our result showed that influenza antibodies can be produced in layers using quadrivalent vaccines and layer chickens are able to produce equivalent antibody levels for each influenza subtype.
Keyword: antibody, egg yolk, influenza, layer chickens, serum