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Kinerja Produksi Ikan Nila Salin dengan Sistem Budidaya Bioflok pada Kolam Terpal di Daerah Istimewa Yogyakarta Muktitama, Asih Makarti; Hendra, Diduk Kristina; Kusuma, Yoga Feri; Lazuardi, Bimastya; Kuswandi, Agasthya; Nugrahawati, Anis; Taufik, Imam; Kurniawan, Arga
Jurnal Salamata Vol 7, No 1 (2025): Juni 2025
Publisher : Politeknik Kelautan dan Perikanan Bone

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/salamata.v7i1.15424

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja pertumbuhan ikan nila salin yang dibudidaya dengan sistem bioflok. Karena sistem bioflok dinilai dapat menghemat penggunaan pakan, meningkatkan produktivitas budidaya, menghemat penggunaan air, dan dapat dilakukan dengan menggunakan kolam terpal atau kolam bulat. Ikan nila salin yang digunakan memiliki bobot rata-rata 10,6±0,48 gram. Penelitian ini dilakukan selama 90 hari. Penelitian ini dilakukan dengan 2 perlakuan, yaitu perlakuan A (pemeliharaan nila salin dengan sistem bioflok) dan perlakuan B (pemeliharaan nila salin tanpa sistem bioflok). Masing masing perlakuan memiliki 7 ulangan. Data yang diperoleh selama penelitian selanjutnya diuji statistika mengunakan SPSS versi 16.1 sedangkan uji T-Test digunakan untuk mengetahui apakah terdapat pengaruh antara perlakuan yang diberikan. Berdasarkan uji statisika diketahui bahwa laju pertumbuhan bobot rata-rata ikan antara perlakuan A dan B adalah tidak berbeda nyata. Sedangkan untuk rasio konversi pakan dan tingkat kelangsungan hidup memiliki hasil yang bebeda nyata, dimana perlakuan A lebih baik dari pada perlakuan B dengan nilai rasio konversi pakan perlakuan A sebesar 1,06 dan tingkat kelangsungan hidup 98%. Berdasarkan hasil penelitian ini maka budidaya nila salin sebaiknya dilakukan dengan sistem bioflok agar dapat menghemat pengunaan pakan dan tingkat kelangsungan hidup yang lebih baik.This study aims to analyze the growth performance of saline tilapia fish cultivated with the biofloc system. Because the biofloc system is considered to be able to save feed usage, increase cultivation productivity, save water usage, and can be done using tarpaulin ponds/circular ponds. The saline tilapia fish used had an average weight of 10.6 ± 0.48 grams. This study was conducted for 90 days. This study was conducted with 2 treatments, namely treatment A (maintenance of saline tilapia fish with a biofloc system) and treatment B (maintenance of saline tilapia fish without a biofloc system). Each treatment has 7 replications. The data obtained during the study were then tested statistically using SPSS version 16.1, while the T-Test was used to determine whether there was an effect between the treatments. This indicates that the growth performance of saline tilapia fish in treatment A and treatment B was not significantly different. Meanwhile, for the feed conversion ratio and survival rate, the results were significantly different, where treatment A was better than treatment B with a feed conversion ratio value of treatment A of 1.06 and a survival rate of 98%. Based on the results of this study, saline tilapia cultivation should be carried out with a biofloc system to save feed usage and achieve a better survival rate.
Klasifikasi Kemiskinan Kabupaten dan Kota di Indonesia Berdasarkan Indikator Sosial Ekonomi Menggunakan Algoritma C4.5 Anugrah Suseno, Lorencia; Basami Hutapea, Tio; Kurniawan, Arga
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 2 (2025): JBIDAI Desember 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i2.80

Abstract

Poverty remains major social issue in Indonesia, so an analysis of its causal factors is necessary to assist the government in making informed decisions. This study aims to classify poverty levels using the C4.5 algorithm by utilizing a dataset containing various socio-economic indicators such as education level, unemployment, and per capita expenditure. The research stages began with data collection and cleaning, transformation, splitting the dataset into training and testing data, and building the classification model. The results show that the C4.5 algorithm is capable of classifying poverty levels effectively and producing clear decision tree patterns. Based on the generated model, the per capita expenditure variable was identified as the dominant factor most influencing poverty status in a region. This model is expected to serve as a basis for formulating more targeted policies to reduce poverty levels in Indonesia.