Judul: Water Quality Status of Kemayungan-Linduk Coastal Waters, Banten  Bay Based on Aquatic Physical and Chemical Parameter
Abstrak: Wilayah pesisir Kemayungan-Linduk terletak di Teluk Banten (Provinsi Banten), merupakan kawasan pemukinan yang memiliki potensi tinggi di berbagai sektor termasuk budidaya perikanan dan perikanan tangkap. Berbagai aktivitas antropogenik dan perubahan alami faktor lingkungan memperngaruhi masuknya bahan pencemar ke dalam perairan sehingga berdampak pada perubahan kondisi kualitas air dan biota di sekitar wilayah perairan tersebut. Penelitian ini bertujuan untuk menganalisis status kualitas perairan pesisir di wilayah Kemayungan-Linduk melalui dua metode, yaitu perhitungan Indeks Pencemaran (IP) dan Indeks CCME (Canadian Council of Ministers of the Environment) dalam rangka pengembangan perikanan. Pengamatan kualitas air dilakukan secara in-situ dan ex-situ dengan pengambilan sampel air pada bulan Februari hingga April 2021 di enam stasiun. Parameter kualitas air yang diamati adalah suhu, kecerahan, kekeruhan, TSS, pH, salinitas, oksigen terlarut, nitrit, nitrat, dan total fosfat. Hasil penelitian secara spasial dan temporal pada enam stasiun pengamatan memiliki nilai bervariasi yang berkisar antara 0,93 hingga 6,45 yang mengindikasikan bahwa status perairan air tergolong dalam kondisi baik hingga tercemar sedang, sedangkan nilai CCME berkisar antara 33,53 hingga 63,90 yang mengindikasikan mutu perairan tergolong kurang baik hingga buruk., The Kemayungan-Linduk coastal area is located in Banten Bay (Banten Province), which is a residential area that has high potential in various sectors including aquaculture and capture fisheries. Various anthropogenic activities and natural changes in environmental factors influence the entry of pollutant materials into the waters, resulting in changes in water quality conditions and biota around the waters. This research aims to determine water quality of coastal waters in the Kemayungan-Linduk region using two methods, namely calculating the Pollution Index (IP) and the CCME Index (Canadian Council of Ministers of the Environment) in the context of fisheries development. Water quality observations were carried out in-situ and ex-situ by taking water samples from February to April 2021 at six stations. The water quality parameters observed were temperature, transparency, turbidity, TSS, pH, salinity, dissolved oxygen, nitrite, nitrate and total phosphate. The results of spatial and temporal research at six observation stations have varying values ranging from 0,93 to 6,45 which indicates that the status of the water is classified as being in a good condition to moderately polluted condition, while the CCME value ranges from 33,53 to 63,90 which indicates Water quality is classified as marginal to poor.
Keyword: Kualitas air, Indeks CCME, Indeks Pencemaran

Judul: Water Pollution Index in Banten Bay Waters Based on Several Water Physical Parameters
Abstrak: Aktivitas antropogenik di sekitar kawasan perairan Teluk Banten dapat membawa limbah tercemar ke dalam perairan. Sehingga dapat memengaruhi kualitas perairan. Penelitian status mutu suatu perairan penting dilakukan untuk menduga kondisi pencemaran suatu perairan. Penelitian ini bertujuan menentukan tingkat pencemaran perairan Teluk Banten melalui pendekatan beberapa parameter fisika perairan. Data yang digunakan ialah data primer dan sekunder tahun 2019- 2022 dari enam lokasi penelitian. Parameter yang dianalisis antara lain suhu, kecerahan, kekeruhan, dan padatan tersuspensi (TSS). Hasil analisis yang diperoleh dibandingkan dengan baku mutu berdasarkan PP No 22 Tahun 2021. Berdasarkan hasil pengamatan suhu (25.1-39ᵒC), kecerahan (0.05-10 m), kekeruhan (0.24-380 NTU), TSS (0.4-742 mg/l), menunjukan bahwa sebagian parameter masih tergolong dibawah baku mutu beberapa parameter kurang sesuai dengan baku mutu perairan (antara lain suhu, kecerahan, kekeruhan, TSS). Berdasarkan hasil perhitungan indeks pencemaran perairan Teluk Banten memiliki indeks pencemaran kategori tercemar ringan (1.315-4.490)., Anthropogenic activities around the waters of Banten Bay can bring polluted waste into the waters. So that it can affect water quality. Research on the quality status of a waters is important to do to predict the pollution conditions of a waters. This study aims to determine the pollution level of Banten Bay waters through the approach of several water physics parameters. The data used are primary and secondary data for 2019-2022 from six research locations. Parameters analyzed included temperature, brightness, turbidity, and suspended solids (TSS). The results of the analysis obtained were compared with the quality standards based on PP No. 22 of 2021. Based on the results of observations of temperature (25.1-39ᵒC), brightness (0.05-10 m), turbidity (0.24-380 NTU), TSS (0.4-742 mg/l) , shows that some of the parameters are still below the quality standards, some parameters are not in accordance with the water quality standards (among others temperature, brightness, turbidity, TSS). Based on the results of the calculation of the water pollution index, Banten Bay has a pollution index of the lightly polluted category (1.315-4.490).
Keyword: baku mutu, indeks pencemaran, kualitas air, parameter fisika, teluk banten

Judul: Penggunaan Trie dalam Membangun Frequent ltemset Menggunakan Algoritmc FPGrowth
Abstrak: FP-growth algorithm search for frequent itemset using the FP-tree data structure which is a prefix tree structure based on pattern growth algorithm. Prefix tree in FP-Growth uses the trie structure in the relationship pattern among the items in the database. Trie is a tree structure that uses strings as key, where each descendant represents a character or prefix. To find frequent itemset, FP-growth algorithm build prefix tree recursively. However, recursive function may need longer time than the Iterative (Manolopoulos 2003). Goal of this research is to create alternatives iteratively to the development of frequent iternset using trie on the FP-Growth. Research was conducted on several stages for the development of the frequent itcmset using a trie, stage through pruning and without pruning. In the last stages of testing is done using the input parameters, enter the difference in support, the number of transactions, and the items involved. Testing stage compare the execution time in the computing process through the stage with pruning and without pruning. Thing that affects the execution time is the number of items involved in development process of the frequent itemset. Frequent itemset development with the pruning process will he faster than without pruning, hecause at the time to build a combination of frequent itemset, infrequent items are not in the development process.
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