Judul: Kemampuan zeolit mempertahankan daya hantar listrik pada media tumbuh tanaman
Abstrak: Penelitian ini bertujuan untuk mengetahui kemampuan zeolit mempertahankan daya hantar listrik yang rendah dan untuk mendapatkan komposisi terbaik dari campuran zeolit dan gambut sebagai media tumbuh tanaman. Penelitian ini dilakukan di rumah kaca dan laboratorium Jurusan Tanah IPB mulai awal Maret sampai September 1995. Perlakuan yang dicobakan meliputi jenis media yaitu zeolit (Z) dan pasir (S); komposisi media atau perban- dingan volume zeolit/pasir dengan gambut yaitu 1:0 (P1), 2:1 (P2), 1:1 (P3); dan dosis pupuk N yaitu 0.2 g NL-1 (N1), 0.5 g NL1 (N2) dan 1.0 g N L-1 (N3). Nilai daya hantar listrik media zeolit dan pasir masing-masing 1.09 mS/cm dan 1.07 mS/cm, secara statistik tidak berbeda nyata. Media komposisi P1 nyata menurunkan daya hantar lis- trik media. Media zeolit mampu meningkatkan KTK, NO3-N, NH4-N, P205, Ca2+, Mg2+, K+, Na+, total basa-basa, daya mengikat air dan nenurunkan bobot isi media. Pada media komposisi P3 nilai KTK, kandungan P205, dan daya mengikat air cenderung lebih tinggi sedangkan bobot isi cenderung menurun. Media zeolit nyata meningkatkan tinggi dan jumlah daun pada 5 dan 9 MST, jumlah, bobot rerata dan total buah untuk setiap tanaman. Media komposisi P3 nyata meningkatkan tinggi, jumlah daun pada 5 dan 9 MST, jumlah, bobot rerata dan total buah. Dosis pupuk N3 nyata meningkatkan tinggi dan jumlah daun pada 9 MST, jumlah, bobot rerata dan total buah, namun menghambat pertumbuhan awal sampai kira-kira umur tanaman 6-7 MST. Komposisi media terbaik yang dapat disarankan berdasarkan hasil penelitian ialah campuran zeolit dan gambut dengan perbandingan 1:1. Kompos ditambahkan sebanyak 10% volume media.
Keyword: 

Judul: The Effect of Zeoponic  and Commercial Media Use for Plantation and Forestry Plant Seedlings
Abstrak: Saat ini banyak media tumbuh tanaman (MTT) yang memiliki permasalahan dalam menumbuhkan bibit. Keterbatasan MTT yang sering dijumpai adalah rendahnya daya dukung daya hantar listrik (DHL) akibat kapasitas tukar kation (KTK) rendah. Hal ini dikarenakan penggunaan material MTT yang memiliki KTK yang rendah sehingga penambahan pupuk menyebabkan lonjakan DHL. Zeolit memiliki KTK yang tinggi sehingga berpotensi digunakan sebagai bahan baku MTT. Untuk mendapatkan media tumbuh tanaman dengan sifat-sifat kimia dan fisika yang sesuai untuk perkembangan bibit tanaman, zeolit perlu ditambah dengan kompos dan cocopeat serta pupuk makro dan mikro. Media campuran tersebut dinamakan “zeoponik”. Zeoponik diharapkan memiliki KTK tinggi dan daya sangga terhadap DHL tinggi. Penambahan kompos dan cocopeat diharapkan menambah ketersediaan unsur mikro dan daya serap media terhadap air. Penelitian ini bertujuan mempelajari sifat-sifat kimia dan pengaruh penggunaan media tumbuh tanaman zeoponik terhadap pertumbuhan bibit tanaman mahoni, gaharu, dan durian serta membandingkan dengan media tumbuh tanaman komersial. Penelitian ini dilaksanakan pada bulan Oktober 2021 hingga bulan Maret 2022 di rumah kaca Departemen Ilmu Tanah dan Sumberdaya Lahan, Fakultas Pertanian, IPB University. Percobaan dilakukan dengan model rancangan acak lengkap (RAL) dengan 1 perlakuan yaitu jenis media tumbuh tanaman yang terdiri dari zeoponik dan 4 media komersial yaitu media A, media B, media C, dan media D. Percobaan menggunakan 3 jenis tanaman masing-masing terdapat 3 kali ulangan sehingga terdapat 45 satuan percobaan. Bibit tanaman mahoni yang berusia 2 bulan, bibit tanaman gaharu berusia 5 bulan, dan bibit tanaman durian yang berusia 1 bulan dipindahkan ke dalam media sebanyak 2 liter dalam polybag ukuran 25x25 cm. Parameter yang diamati yaitu tinggi tanaman, jumlah daun, lebar daun, panjang daun, dan diameter batang. Analisis media tumbuh tanaman yaitu pH, DHL, N-NO3, N-NH4, KTK. Berdasarkan hasil penelitian, media tumbuh tanaman zeoponik memiliki pH netral, N tersedia tinggi, dan KTK tinggi. Tingginya KTK zeoponik dapat mempertahankan DHL tetap rendah dibandingkan dengan media lainnya. Pertumbuhan bibit tanaman mahoni dan bibit tanaman gaharu paling baik ditanam menggunakan media zeoponik. Perlakuan dengan zeoponik menghasilkan tanaman dengan pertumbuhan yang paling baik. Pertumbuhan bibit tanaman durian yang ditanam menggunakan media A lebih baik dibandingan media zeoponik., Currently, many plant growth media (PGM) have problems in growing seeds. There is limitation of plant growth media that is commonly found is the low buffering capacity of the electrical conductivity (EC) due to low cation exchange capacity (CEC). This is due to the use of PGM material which has a low CEC so that the addition of fertilizer causes a surge in EC. Zeolite has a high CEC so that it is potentially used as material for PGM. In order to obtain media for plant growth with chemical and physical properties suitable for the development of plant seedlings, zeolite needs to be added with compost and cocopeat as well as macro and micro fertilizers. Theis mixed media is called "zeoponic". Zeoponic is expected to have a high CEC and a high buffer against EC. The addition of compost and cocopeat is expected to increase the availability of micro elements and the water capacity of media. This study aims to investigate the effect of zeoponic plant growth
Keyword: cation exchange capacity, electrical conductivity, plant growth media, zeolite

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation