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Analisis Self Regulated Learning Mata Pelajaran IPA pada Siswa Madrasah Tsanawiyah Fadilah, Rizka Elan; Fadri, Firda; Nurisya, Khofifatu
Jurnal Basicedu Vol. 7 No. 5 (2023)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/basicedu.v7i5.5185

Self-Regulated Learning penting untuk dimiliki siswa sebagai upaya dalam menjaga motivasi belajar. Penelitian ini bertujuan untuk menganalisis kemampuan Self-Regulated Learning pada pembelajaran IPA siswa Madrasah Tsanawiyah kelas IX. Metode pengumpulan data menggunakan kuesioner yang berisi tiga aspek Self-Regulated Learning, yaitu aspek perencanaan, pelaksanaan, dan evaluasi. Analisis data dalam penelitian ini menggunakan pendekatan statistik deskriptif. Hasil analisis diperoleh persentase sebesar 60,61% untuk aspek perencanaan belajar, 69,7% untuk aspek pelaksanaan, dan 63,64% untuk aspek evaluasi. Berdasarkan hasil analisis data, secara keseluruhan, kemampuan self-regulated learning siswa pada pembelajaran IPA di salah satu Madrasah Tsanawiyah kelas IX di Kabupaten Kediri masuk dalam kategori sedang.

Implementation of the Fuzzy Time Series Singh Method for Forecasting Non-Oil and Gas Export Values in Indonesia Borahima, Maharani Safira B.; Sain, Hartayuni; Setiawan, Iman; Fadri, Firda
BERKALA SAINSTEK Vol 12 No 3 (2024)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v12i3.52663

Export activities drive a country's economic growth by increasing revenue and strengthening trade relations between countries. In Indonesia, non-oil and gas products are the primary contributors of export performance. In 2022, non-oil and gas exports values reached 275.96 million USD, marking an increase of 25.80% compared to the previous year's export value. This growth in export value was influenced by various external factors, leading to substantial changes. The government requires further analysis to forecast future trends in non-oil and gas export values due to the uncertain and fluctuating patterns. The Singh Fuzzy Time Series method, an advancement of FST, utilizes fuzzy sets to forecast volatile economic data, yielding more accurate predictions. This research used the Singh FST method and achieved a low MAPE value of 1.31%, indicating a high level of accuracy. Forecasts for Indonesia's non-oil and gas export value over the next three months are projected to reach USD 22,263.02 million in January 2023, followed by USD 22,217.21 million in February 2023, and USD 22,243.68 million in March 2023. These export value forecasts can aid the government in policy-making related to exports and sustain the stability of the country’s economic growth rate.

Analisis Self Regulated Learning Mata Pelajaran IPA pada Siswa Madrasah Tsanawiyah Fadilah, Rizka Elan; Fadri, Firda; Nurisya, Khofifatu
Jurnal Basicedu Vol. 7 No. 5 (2023)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/basicedu.v7i5.5185

Self-Regulated Learning penting untuk dimiliki siswa sebagai upaya dalam menjaga motivasi belajar. Penelitian ini bertujuan untuk menganalisis kemampuan Self-Regulated Learning pada pembelajaran IPA siswa Madrasah Tsanawiyah kelas IX. Metode pengumpulan data menggunakan kuesioner yang berisi tiga aspek Self-Regulated Learning, yaitu aspek perencanaan, pelaksanaan, dan evaluasi. Analisis data dalam penelitian ini menggunakan pendekatan statistik deskriptif. Hasil analisis diperoleh persentase sebesar 60,61% untuk aspek perencanaan belajar, 69,7% untuk aspek pelaksanaan, dan 63,64% untuk aspek evaluasi. Berdasarkan hasil analisis data, secara keseluruhan, kemampuan self-regulated learning siswa pada pembelajaran IPA di salah satu Madrasah Tsanawiyah kelas IX di Kabupaten Kediri masuk dalam kategori sedang.

Negative Binomial Regression Modeling to Analyze the Determinants of Infant Mortality in West Java Province Fadri, Firda; Firmansyah, Ari; Erlanda, Victor Alesyus
BERKALA SAINSTEK Vol. 13 No. 1 (2025)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v13i1.53686

The Infant Mortality Rate (IMR) is an important indicator in assessing the quality of public health and the success of health programs in a region. Proper handling of factors that determine IMR is essential to reduce this number. The data used were 27 districts/cities in West Java in 2022 with predictor variables including the number of health workers, percentage of poor population, percentage of iron tablet consumption, percentage of clean and healthy living behavior, percentage of exclusive breastfeeding, and percentage of low birth weight babies. The results of the analysis with Poisson Regression showed overdispersion so that IMR modeling was carried out using Negative Binomial Regression. The AIC value for the Negative Binomial Regression model was 305.630 and the BIC value was 315.997. The deviance ratio and Pearson's Chi-square approached one, indicating effective handling of overdispersion. The only significant variable affecting IMR was the percentage of clean and healthy living behavior. This shows the importance of increasing clean and healthy living behavior as the main strategy for reducing IMR in West Java Province.

Predicting Drought in East Nusa Tenggara: A Novel Approach Using Wavelet Fuzzy Logic and Support Vector Machines Sain, Hartayuni; Fadri, Firda
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17142

The water crisis, or what is hereinafter referred to as drought, has become a systemic and crucial problem in several regions in Indonesia. Indonesia is an agricultural country, where the presence of water is very influential so that drought can become a natural disaster if it starts to cause an area to lose its source of income due to disturbances in agriculture and the ecosystem it causes. Drought forecasting can provide support solutions in preventing the impact of drought. In this paper, we compare the performance of wavelet fuzzy logic and the support vector machine (SVM) as a supervised learning method for drought forecasting in East Nusa Tenggara. This study examines the monthly rainfall data for 1999-2015 which is the basis for calculating the drought index based on the Standardized Precipitation Index (SPI). The SPI value used is SPI-3 at a station in East Nusa Tenggara. The performance of models is compareded on R2. The results showed that R2 of wavelet fuzzy logic is smaller than one of SVMVM is better than the wavelet fuzzy logic for forecasting SPI value of drought in East Nusa Tenggara.

Community Structure of Avifauna in The Rehabilitation Zone at Wonoasri Resort, Meru Betiri National Park Siddiq, Arif Mohammad; Sulistiyowati, Hari; Ulaa, Munaa Aqidatul; Ulum, Fuad Bahrul; Fadri, Firda
Jurnal Biodjati Vol 9 No 1 (2024): May
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/biodjati.v9i1.30033

The rehabilitation zone of Meru Betiri National Park (MBNP) needs to be measured in relation to the success of ecosystem restoration. It can be used as a bioindicator, such as the bird community structure, hence their sensitivity to environmental changes. Therefore, the aims of this study are to determine the community structure of avifauna in the rehabilitation zone at Wonoasri Resort, MBNP including bird diversity, richness, dominance, feeding guilds, and their correlation with forest cover. The research was conducted in January 2023 in the Bonangan Block at three observation points, namely low vegetation cover, medium vegetation cover, and high vegetation cover. The ecological data, such as species and abundance, were collected using the point count method, while additional information related to conservation statuses were collected by web browsing on the IUCN Red List, the CITES appendix, and Indonesian government regulation (LHK No.106/2018). Data analysis used the dominance index, species diversity index (Shannon Wiener), and evaluated for statistical data using a Kruskal-Wallis (KW) test in R version 3.2.1. We recorded 38 bird species belonging to 31 genera and 21 families during this study. Among the feeding guilds, the insectivorous birds (14 species) were recorded as the higher species richness, followed by omnivorous (eight species), frugivorous (five species), granivorous (five species), carnivorous (four species), and nectarivorous (two species). The Kruskal-Wallis test shows there is a significant correlation among the feeding guilds (value of 11.644, with p-value = 0.040). Furthermore, referring to species richness, high vegetation cover areas have the highest species richness (30 species) compared to medium vegetation cover areas (27 species) and low vegetation cover areas (26 species). The dominance of avifauna species at this location falls into the low category (C = 0.07). According to the diversity index, avifauna in Bonangan Block is classified as a high category (H'=3.01).

Prediction of Rice Production in Jember Regency Using Adaptive Neuro Fuzzy Inference System (ANFIS) Riski, Abduh; Putriana, Novia Ayu; Fadri, Firda; Kamsyakawuni, Ahmad; Pradjaningsih, Agustina; Santoso, Kiswara Agung; Sari, Merysa Puspita
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2797.262-275

Jember Regency is the fourth largest rice-producing regency/city in East Java, so Jember Regency dramatically contributes to increasing the agricultural sector in East Java Province. However, the level of rice production can fluctuate, which is influenced by other factors such as rainfall. A prediction system is needed to anticipate a decrease in rice production. This research aims to predict rice production in the Jember Regency using the Adaptive Neuro Fuzzy Inference System (ANFIS), highlighting the impact of key variables like rainfall, harvested area, and land productivity. This research consists of three stages: training, testing, and prediction. The input variables used in this research are rainfall (mm), harvested area (Ha.), and land productivity (Kw/Ha.), while the output variable is rice production (tons). The membership functions used are generalized Bell and Gaussian, with several combinations of many membership functions. The best model obtained from this research is a model that uses generalized bell membership functions with three membership functions for rainfall variables and two membership functions for harvest area and land productivity variables. The epoch (iteration) used to achieve minimum error is 100 epochs. The best model achieved high accuracy, producing a MAPE value of 0.080% in training and 1.525% in testing, indicating its strong potential for reliable agricultural production forecasting. The predicted amount of rice production in Jember Regency in 2024 was 922,136.8317 tons.

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