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All Journal Jumat Keagamaan: Jurnal Pengabdian Masyarakat Indonesian Journal of Statistics and Its Applications
Iqbal Hanif, Iqbal
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Religion Computer Science & IT Education Mathematics Social Sciences Other

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Implementasi Moderasi Beragama melalui Lomba Mading di Lokasi KKM MTs Al-Ma'arif 01 Singosari Rizky, Alfi Nur Nadiva Soetam; Ramadani, Saviestya Dyan; Hanif, Iqbal; Amanah, Fina Sabila; Adawiyah, Nafisatul; Haq, Arinal; Faridah, Siti
Jumat Keagamaan: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2024): Agustus
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasagama.v5i2.4573

Abstract

Di era masa kini, penting bagi generasi muda untuk memahami dan mengimplementasikan moderasi beragama dalam kehidupan sehari-hari. Hal ini diperlukan mengingat dalam era globalisasi ini, keberagaman menjadi semakin nyata dan relevan dalam konteks kehidupan masyarakat. Keberagaman agama merupakan salah satu aspek yang krusial dalam dinamika sosial yang mempengaruhi cara individu memandang dunia, bertindak, dan berinteraksi satu sama lain. Implementasi moderasi beragama dapat diwujudkan salah satunya melalui pengabdian masyarakat yang dilakukan di MTs Almaarif 01 Singosari. Pengabdian masyarakat yang dilakukan oleh mahasiswa KKM UIN Maulana Malik Ibrahim Malang ini mengimplementasikan moderasi beragama melalui lomba mading. Penelitian ini menggunakan metode penelitian berupa pendekatan kualitatif deskriptif. Data dikumpulkan melalui observasi dan wawancara. Hasil yang didapat adalah adanya dampak positif bagi para siswa, karena dapat memiliki pemahaman yang komprehensif serta dapat mengimplementasikannya dalam karya mading. Meskipun demikian, masih ada beberapa kendala seperti kurangnya minat sebagian siswa terhadap tema moderasi beragama. Dari hasil tersebut, dibutuhkan adanya pemaksimalan dalam mempromosikan tema moderasi beragama untuk menarik minat generasi muda.
PENDUGAAN PARAMETER FUNGSI COBB-DOUGLAS GALAT ADITIF DENGAN ALGORITME GENETIKA Hanif, Iqbal; Soleh, Agus M; Alamudi, Aam
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.54

Abstract

Cobb-Douglas function with additive errors is a function which can be used to analyse the relationship between production output and production factors. The method commonly used to estimate the parameter of that function is Nonlinear Least Square (NLS) and a common algorithm for this method is Gauss Newton iteration (NLS-GN). However, NLS-GN method has less-optimum results when analysing multicolinearity data. A possibly better method for this analysis is Genetic Algorithm (NLS-GA). The purpose of this study is to analyse the use of Genetic Algorithm to estimate parameters of Cobb-Douglas function with additive errors. The results show that NLS-GA method could not produce a better parameter estimator than NLS-GN method does but it produced a better parameter estimator in analysing multicolinearity data. NLS-GA method is capable of producing a better model with predictive ability than NLS-GN method does with real data. Keywords: cobb-douglas function, genetic algorithm, nonlinear least square
Ensemble Learning For Television Program Rating Prediction Hanif, Iqbal; Septiani, Regita Fachri
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p377-395

Abstract

Rating is one of the most frequently used metrics in the television industry to evaluate television programs or channels. This research is an attempt to develop a prediction model of television program ratings using rating data gathered from UseeTV (interned-based television service from Telkom Indonesia). The machine learning methods (Random Forest and Extreme Gradient Boosting) were tried out utilizing a set of rating data from 20 television programs collected from January 2018 to August 2019 (train dataset) and evaluated using September 2019 rating data (test dataset). Research results show that Random Forest gives a better result than Extreme Gradient Boosting based on evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). On the training dataset, prediction using Random Forest produced lower RMSE and MAE scores than Extreme Gradient Boosting in all programs, while on the testing dataset, Random Forest produced lower RMSE and MAE scores in 16 programs compared with Extreme Gradient Boosting. According to MAPE score, Random Forest produced more good quality prediction (4 programs in the training dataset, 16 programs in the testing dataset) than Extreme Gradient Boosting method (1 program in the training dataset, 12 programs in the testing dataset) both in training and testing dataset.
Co-Authors Aam Alamudi Adawiyah, Nafisatul Agus Mohamad Soleh Amanah, Fina Sabila Haq, Arinal Ramadani, Saviestya Dyan Rizky, Alfi Nur Nadiva Soetam Septiani, Regita Fachri Siti Faridah