Judul: Institutional and Transaction Cost Analysis of Sea Farming Management in Panggang Island, Administrative Regency of Kepulauan Seribu
Abstrak: Phenomenon of declining fishing production in some Indonesian waters area has raised fears of increasing scarcity of fish resources, therefore many implementative programs and efforts has been developed. One of these programs is sea farming. Sea farming is shallow waters marine resources use system based on mariculture. The main objective is to increase fish resources while sustaining fishing activity, mariculture and marine ecotourism. Sea farming is not exactly the same with mariculture. Mariculture and other economic activities based on marine resources are subsystem of the sea farming. The aim of this research are : 1) to identify the role of institutional in managing coastal resources in Panggang Island Administrative Regency of Kepulauan Seribu, 2) to analyze institutional management of sea farming, 3) to evaluate economic benefit and transaction cost of sea farming management, 4) to analyze sustainability of sea farming program. The data was analyzed by 7 methods: 1) Institutional Analysis and Development (IAD); 2) stakeholders analysis; 3) fisheries resources management conflict analysis; 4) income analysis; 5) transaction cost analysis; 6) cost effectiveness analysis; 7) program scenario evaluation analysis. Based on the identification of institutional role in management of sea farming and coastal resources, the stakeholders actors are subject (sea farming members and trader), players (government, National Parks of Kepulauan Seribu, and university-PKSPL IPB), spectator (village government), and actor (security). Economic benefit of sea farming group member is Rp 6,805,645/year. Transaction cost for institutional management is Rp 875,000/year and the effectivity of transaction is 0.13, which is considered cost effective. Evaluation of sustainable scenario showed that scenario B which is program can be continued with a significant improvement was selected. The improvement of sea farming program includes adaptif institutional development, improvement of group organization and management, in order to achieve sustainability of sea farming program.
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Judul: Correlating mariculture and household income generation: a case of panggang island initiative
Abstrak: Aquaculture has an important role in reducing poverty and ensuring food security, particularly within coastal areas. Kepulauan Seribu is a part of the capital city of Indonesia, Jakarta, but it experiences severe losses due to pollution and environmental degradation. In fact, Kepulauan Seribu not only has the lowest Human Development Index (HDI) but also the highest poverty rate, compared to other areas in Jakarta. In order to improve the local community's welfare while conserve marine ecosystem, the local government and Center for Coastal and Marine Resources Studies (CCMRS-IPB) initiated a project called sea farming in Panggang Island, Kepulauan Seribu. Sea farming is a mariculture-based project to create sustainable marine resource management. This study has two main objectives that include the following: (1) to identify which factors determine household participation in the project activities; and (2) to describe the project contribution to poverty alleviation by analyzing its impacts on household income generation as well as describing benefits and constraints of the project subjectively felt by community members particularly the participants. To address the research objectives, a survey of project participants (as the treatment group) and non-participants (as the control group) in the community was carried out. Two different sampling techniques were employed to select both groups. Stratified random sampling technique was employed to select the treatment group, while convenience sampling technique was employed to select the control group. Quantitative data on household incomeand other socioeconomic characteristics were evaluated using statistical techniques to identify the determinants of sea farming participation and the impacts of the project to household income generation. In addition, survey responses to open-ended questions were analyzed to describe benefits and constraints of the project subjectively felt by community members particularly for the participants. The determinants of sea farming participation were analyzed using probit model. The model shows that education, occupation, household size, and organization member are significant factors in influencing household decision to participate in the project. It also shows that all significant factors are reducing the probability of one person to participate in the project which is different from the initial hypothesis. This results indicates that the project is more attractive for those whom are less educated, have primary occupation not as a fisherman, have less household members, and less involvement in non-sea farming organization. It implies that the project manager and local government should socialize the objectives and positive impacts of the project effectively to attract the fishermen as the main beneficiaries. The impacts of sea farming to household income generation were analyzed using OLS regression. The model shows that sea farming participation, age, organization member, and mobile phone ownership are variables that have significant relationship to the total income. Almost all significant variables are positively related to total income except age. Sea farming participation gives the highest contribution to total income. It increased the total income by Rp14.6 million per period, ceteris paribus, at 1 percent significance level. The model indicates that the project successfully increase household income. It also shows networking is another important aspect for local community to increase their income. It implies that project manager
Keyword: sea farming, mariculture, poverty, Panggang Island

Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak