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Pengaruh Pemberian Probiotik Dengan Dosis Berbeda Pada Pakan Daun Kaliandra (Calliandra calothyrsus) Terhadap Pertumbuhan Benih Ikan Nila (Oreochromis niloticus) Siswati, Siswati; Jurniati, Jurniati; Marran, Riska; Muchlis, Andi Mi'rajusysyakur; Idrus, Andi
Juvenil Vol 6, No 2: Mei (2025)
Publisher : Department of Marine and Fisheries, Trunojoyo University of Madura, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/juvenil.v6i2.29462

Abstract

ABSTRAKIkan nila (Oreochromis niloticus) merupakan jenis ikan air tawar yang memiliki nilai ekonomis tinggi. Budidaya ikan nila dipengaruhi oleh kualitas dan kuantitas dari pakan yang diberikan, salah satu alternatif yang dapat dilakukan untuk mendapatkan sumber protein pada pakan yaitu daun Kaliandra (Calliandra calothyrsus). Tujuan penelitian untuk mengetahui pengaruh pemberian probiotik dengan dosis berbeda pada pakan daun Kaliandra terhadap pertumbuhan benih ikan nila. Metode penelitian RAL (Rancangan acak lengkap) dengan 4 perlakuaan dan 3 ulangan. Perlakuan A (Probiotik 0 mL/100 g pakan); B (probiotik 5 mL /100 g pakan); C (probiotik 10 mL /100 g pakan; D (probiotik 15 mL/ 100 g pakan), Analisis data dilakukan dengan menggunakan metode ANOVA Hasil pengamatan menunjukkan bahwa semua perlakuan tidak berpengaruh nyata terhadap laju pertumbuhan mutlak benih ikan nila tetapi secara deskriptif rerata laju pertumbuhan mutlak benih ikan nila tertinggi terdapat pada perlakuan B (62,6 g) dan terendah pada perlakuan A (45,6 g). Kelangsungan hidup ikan nila menunjukkan bahwa semua perlakuan tidak berpengaruh nyata, nilai tertinggi pada perlakuan B dan C = 100%, dan terendah pada perlakuan A dan D = 90. Kesimpulan dari penelitian ini adalah pemberian probiotik 5 mL/100 g pakan menunjukkan potensi paling optimal secara deskriptif terhadap pertumbuhan benih ikan nila, dengan tingkat kelangsungan hidup yang tetap tinggi. Penemuan ini memberikan dasar penting bagi pengembangan pakan fermentasi berbahan nabati lokal yang lebih efisien dan ramah lingkungan dalam budidaya ikan nila.Kata Kunci: Probiotik, Kaliandra, Ikan Nila, PertumbuhanABSTRACTTilapia (Oreochromis niloticus) is a type of freshwater fish that has high economic value. Tilapia cultivation is influenced by the quality and quantity of the feed given, one alternative that can be done to get a source of protein in the feed is kaliandra leaves (Calliandra calothyrsus). The purpose of the study was to determine the effect of probiotics with different doses in Kaliandra leaf feed on tilapia seed growth. Research method RAL (complete randomised design) with 4 treatments and 3 replicates. Treatment A (Probiotic 0 mL/100 g feed); B (probiotic 5 mL /100 g feed); C (probiotic 10 mL /100 g feed; D (probiotic 15 mL / 100 g feed), Data analysis was performed using ANOVA method. The results showed that all treatments had no significant effect on the absolute growth rate of tilapia fry but descriptively the average absolute growth rate of tilapia fry was highest in treatment B (62.6 g) and lowest in treatment A (45.6 g). The survival rate of tilapia fish showed that all treatments had no significant effect, the highest value in treatments B and C = 100%, and the lowest in treatments A and D = 90 %. The conclusion of this study is that the provision of probiotics 5 mL/100 g of feed shows the most optimal potential descriptively on the growth of tilapia fish fry, with a high survival rate.This finding provides an important basis for developing more efficient and environmentally friendly fermented feed using local plant-based ingredients in Nile tilapia aquaculture.Keywords: Probiotics, Calliandra, Tilapia, growth
Klasifikasi Status Penerima Bantuan Program Keluarga Harapan di Provinsi NTB Menggunakan Metode Regresi Probit Zulhan Widya Baskara; Harsyiah, Lisa; Widya Baskara, Zulhan; Eka Putri, Dina; Jurniati, Jurniati
Mandalika Mathematics and Educations Journal Vol 7 No 3 (2025): Edisi September
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i3.9776

Abstract

The Indonesian government's effort to accelerate the achievement of comprehensive social welfare involves adopting strategic policies in the form of distributing social assistance to economically vulnerable communities. One concrete example of this policy is the Family Hope Program (Program Keluarga Harapan/PKH). However, its implementation in the field still faces challenges, particularly in the form of unequal distribution, which has the potential to hinder the program’s effectiveness. To address this issue, a rigorous verification system is required to ensure that prospective beneficiaries truly meet the official criteria set by the government. Therefore, classifying households eligible for PKH is a crucial step. The probit regression approach is employed as a method to analyze and determine the household eligibility status. This method yields an accuracy rate of 76.25%, which is considered valid and reliable based on the Press’s Q statistic
Peramalan Produksi Kedelai di Provinsi Nusa Tenggara Barat menggunakan Model Grey-Markov (1,1) Nurmaulia, Ananda Rizantia; Harsyiah, Lisa; Purnamasari, Nur Asmita; Jurniati, Jurniati
Semeton Mathematics Journal Vol 2 No 2 (2025): Oktober
Publisher : Program Studi Matematika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/semeton.v2i2.314

Abstract

The continuously increasing soybean imports are caused by the imblance in domestic soybean produstion. This indicates that nationalsoybean self-sufficiensy has not yet been achieved, as many soybean farmlands have now been converted to other commodities. This condition occurs in West Nusa Tenggara, Which is one of Indonesia’s nationalsoybean production centers. To understand future soybean production conditions, forecasting future soybean production in West Nusa Tenggara province using the grey_markov (1, 1) model. This model only requires minimal data for forecasting, which aligns with the limites research data available. The data used in this study is soybean production data from West Nusa Tenggara province. The research results show that in 2022, soybean production in West Nusa Tenggara province will decline, with the prediction demonstrating good accuracy as indicated by a MAPE value of 15.75%.
Peramalan Jumlah Kasus Demam Berdarah Dengue di Pulau Lombok Menggunakan Model Space Time Autoregressive (STAR) Haryati, Haryati; Bahri, Syamsul; Purnamasari, Nur Asmita; Jurniati, Jurniati
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8170

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease with potential to cause outbreaks. It progresses and often proves fatal, with a high mortality rate frequently attributed to delayed treatment. According to data from the West Nusa Tenggara (NTB) Provincial Health Office, the incidence of DHF in the region has shown a consistent upward trend year over year, necessitating increased vigilance and preventative measures. This study aims to develop an accurate forecasting model to predict the number of DHF cases. The resulting model is intended to serve as tool for the community and policymakers to anticipate the spread of the disease, particularly on Lombok Island. The analytical method employed is the Space-Time Autoregressive (STAR) model, a time-series technique that incorporates interdependencies across both location (space) and time. The data analyzed consists of monthly DHF case counts on Lombok Island from January 2018 to December 2-22. The research results indicate that the best-perfoming model is STAR (3, 1). The forecasting accuracy of this optimal model, measured by the Mean Absolute Scaled Error (MASE), for Central Lombok and North Lombok Regencies was 0.87 and 0.59, respectively. These MASE values, being less than 1, indicate that the forecasting performance of the STAR model is superior to that of a simple naïve baseline model.