Judul: Penentuan Tingkat Kemiskinan Menggunakan Logika Fuzzy pada Koperasi Baytul-Ikhtiar Bogor
Abstrak: Poverty is a problem for every country in the world, including Indonesia. Many indicators have been used to determining the level of poverty. Baytul-Ikhtiar cooperation (BAIK) is a cooperation that offers a micro-credit to the middle lower economy level. The cooperation has a problem to determine the level poverty of a person. The use of fuzzy logic system is one way to determine the level of poverty. One of model using the fuzzy logic is Mamdani model. There are some steps being taken in Mamdani model, namely fuzzification, evaluation, aggregation, and defuzzification. Inputs used in this paper are income, the number of dependent, the condition of building, and the consumption of rice. The output of this model is the level of poverty. It is compared to the output using data from the BAIK. It shows that the conformity percentage is 93,33 percent. It can be concluded that fuzzy model can be used to determine the level of poverty in BAIK cooperation.
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Judul: Implementation of Artificial Neural Network Methods and Fuzzy Logic Methods for the Decision Support System for Giving Zakat.
Abstrak: Masalah kemiskinan menjadi salah satu permasalahan utama di Indonesia. Upaya yang dilakukan dalam menanggulangi kemiskinan antara lain dengan memaksimalkan potensi zakat di Indonesia. Penelitian ini mengambil kasus distribusi dana zakat di IZI (Inisitif Zakat Indonesia). IZI telah berperan dalam pengelolaan zakat serta donasi keagamaan lainnya sejak berdiri pada tahun 2015. Dalam operasional sehari-hari, dibutuhkan suatu standar dalam pembuatan keputusan pemberian zakat kepada mustahiq. Dalam hal ini, perlu standar pengklasifikasian kemiskinan seseorang yang bisa dilihat dari beberapa indikator, yaitu: pendapatan, jumlah tanggungan, nilai rumah, dan status kepala rumah tangga. Dalam penelitian ini penulis menggunakan model logika fuzzy Mamdani dan model jaringan syaraf tiruan untuk pengambilan keputusan pemberian zakat. Output kedua model ini kemudian dikonfirmasikan dengan keputusan yang sudah dijalankan selama ini di IZI. Penelitian ini menunjukan kesesuaian yang sangat tinggi (94%) antara output model logika fuzzy dan fakta keputusan di lapangan. Akurasi yang serupa juga ditunjukkan oleh model jaringan syaraf tiruan. Kedua model tersebut dapat diimplementasikan dengan baik dalam penelian ini menggunakan perangkat lunak Python. Sifat perangkat Python yang merupakan perangkat lunak open source memudahkan implementasi kedua model di IZI dan LAZNAS lainnya. Dari segi kemudahan implementasi, model jaringan syaraf tiruan relatif lebih mudah diterapkan di lapangan, karena tidak diperlukan penyusunan aturan-aturan implikasi yang relatif kompleks, seperti dalam model logika fuzzy., The problem of poverty is one of the main problems in Indonesia. Efforts made in overcoming poverty include maximizing the potential for zakat in Indonesia. This research takes the case of the distribution of zakat funds at IZI (Inisiatif Zakat Indonesia). IZI has played a role in managing zakat and other religious donations since its establishment in 2015. In daily operations, a standard is needed in making decisions on giving zakat to mustahiq. In this case, it is necessary to have a standard for classifying one's poverty which can be seen from several indicators, namely: income, number of dependents, house value, and the status of the head of the household. In this study, the authors used the Mamdani fuzzy logic model and the artificial neural network model for the decision making of giving zakat. The output of these two models is then confirmed with the decisions that have been made at IZI. This study shows very high compatibility (94%) between the output of the fuzzy logic model and the fact of the decisions in the field. Similar accuracy is also shown by the neural network model. Both models can be implemented properly in this research using Python software. The nature of Python software which is open source software facilitates the implementation of both models in IZI and other LAZNAS. In terms of ease of implementation, the neural network model is relatively easier to apply in the field, because it does not require the preparation of relatively complex implication rules, such as in fuzzy logic models.
Keyword: artificial neural network, fuzzy logic, IZI, Zakat

Judul: Macroalgae development as feeding stimulants for the adult sea urchin (Tripneustes gratilla Linnaeus 1758)
Abstrak: Development of artificial feed for somatic growth and gonad quality is one of the focus of research on sea urchin aquaculture activities. Palatability is important factor when developing artificial feed for aquaculture diets. This study investigated the effects of macroalgae (Sargassum polycystum, Gracilaria lichenoides, and Ulva reticulata) as feeding stimulants to artificial diets consumption and gonad profile for the adult sea urchin Tripneustes gratilla, by three feeding trials. Previously starved T. gratilla were held in replicated (3X) 50 L aquaria with recirculation water system (in the first feeding trial) and flow through water system (in the second and third feeding trial). In the first feeding trial, preference of T. gratilla for dried macroalgae was assessed in choice feeding experiment. T. gratilla displayed significant preference for the agar containing dried U. reticulata at 20% agar weight over nine other diets treatment when they were offered simultaneously. Total protein, energy and the ratio of protein to energy in the dried macroalgae had no correlation on agar consumption by T. gratilla. Three artificial diets were made by incorporating one of each of these dried macroalgae at 5% feed weight used in the second feeding trial. Although no significant difference in artificial diets containing dried macroalgae, urchins fed diets containing dried macroalgae consumed more than basal diet when they were offered as single diet. These results indicate that dried macroalgae added to the artificial diets act as feeding stimulants and increasing the acceptability of artificial sea urchin diets. The third feeding trial used dried U. reticulata as feeding stimulant to artificial diet. Data suggest that artificial diet containing 10% dried U. reticulata boosting feed consumption and significantly increasing nutrient accumulation, gonad index, egg diameter and fix gonad color.
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