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All Journal Assets : Jurnal Ekonomi, Manajemen dan Akuntansi Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum Jurnal Info Sains : Informatika dan Sains Profitabilitas IZZI: Jurnal Ekonomi Islam
Nurul Ichsan
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Religion Computer Science & IT Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Social Sciences

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ANALISIS KINERJA KOPERASI SYARIAH BERDASARKAN PERATURAN NOMOR: 07/PER/DEP.6/IV/2016 (STUDI PADA KOPERASI SYARIAH BENTENG MIKRO INDONESIA PERIODE 2015-2019) Hania Yunsita Adzhani; Nurul Ichsan; Ady Cahyadi
Assets: Jurnal Ekonomi, Manajemen, dan Akuntansi Vol 10 No 2 (2020): Assets : Jurnal Ekonomi, Manajemen dan Akuntansi
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/assets.v10i2.18604

Abstract

Penelitian ini bertujuan menganalisis kinerja Koperasi Syariah Benteng Mikro Indonesia tahun 2015 sampai tahun 2019 dan perkembangannya selama tahun tersebut dengan analisis rasio yang berpedoman pada Peraturan Deputi Bidang Pengawasan Kementerian Koperasi dan UKM No.07/PerDep.6/IV/2016 dan analisis trend. Penelitian ini menggunakan metode kombinasi. Subjek penelitian ini adalah Koperasi Syariah Benteng Mikro Indonesia (Kopsyah BMI). Penelitian ini menggunakan data primer dan sekunder. Hasil penelitian ini menunjukkan bahwa kinerja Kopsyah BMI pada tahun 2015 dikategorikan sehat, pada tahun 2016 dikategorikan cukup sehat, tahun 2017 dikategorikan sehat, tahun 2018 dikategorikan sehat, dan tahun 2019 dikategorikan sehat . Sedangkan perkembangan kinerja Kopsyah BMI pada tahun 2015-2019 menunjukkan trend naik, trend turun, dan trend tetap.
Akad Bank Syariah Nurul Ichsan
Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum Vol 50, No 2 (2016)
Publisher : Faculty of Sharia and Law - Sunan Kalijaga State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ajish.2016.50.2.399-423

Abstract

An implementation of machine learning on loan default prediction based on customer behavior Robi Aziz Zuama; Nurul Ichsan; Achmad Baroqah Pohan; Mohammad Syamsul Azis; Mareanus Lase
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 01 (2024): Informatika dan Sains , Edition March 2024
Publisher : SEAN Institute

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Abstract

In the banking sector, loans have become a key component that steers the economy, encourages company expansion, and directly impacts the growth of a nation's economy. Banks must evaluate borrowers' ability to repay loans given the inherent risks involved in order to reduce the likelihood of default. In particular, machine learning (ML) has shown promise as a revolutionary tool for loan default prediction using advanced methodologies to examine historical data relating to customer behavior, this study investigates the application of machine learning (ML) in forecasting loan outcomes. The results show that XGBoost performs better than other machine learning algorithms, with an accuracy rate of 89%. Random forest and logistic regression come in second and third, respectively, with 88% accuracy. KNN and decision trees come next, both with somewhat lower accuracy rates (87%). By incorporating consumer behavior domain variables, this study fills in the gaps in the literature and offers a more thorough understanding of loan projections. In order to improve model performance and strengthen the predictive power of machine learning algorithms in loan scenarios, further research incorporating trials to optimize algorithm parameters is necessary as financial institutions continue to experience difficulties.
Perancangan Sistem Informasi Pengendalian Persediaan Barang Produksi pada PT. Harmonics Techindo Agung Basri, Hasan; Devi Alisa Putri; Alif Rizqi Mulyawan; Salman Alfarizi; Nurul Ichsan
PROFITABILITAS Vol 5 No 1 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i1.8752

Abstract

Information technology that is very important for an agency and company can be used to facilitate a job in achieving goals optimally. PT. Harmonics Techindo Agung is a company engaged in the manufacturing industry, Designing an information system for controlling production goods inventory at PT. Harmonics Techindo Agung is done because the system used is still done manually which results in data being easily lost, a long time in data processing, and frequent errors in data input. To overcome this, researchers use the Prototype method in the process of controlling production goods inventory at PT. Harmonics Techindo Agung to help software development in creating software models that provide an idea of ideas, experimenting with designs so that they can provide convenience for companies in managing data faster, more accurately and effectively.
Prediksi Cacat Software Menggunakan Class Balancer Bagging C4.5 dan Analisis Statistik SPSS dalam Konteks Akuntansi Nurul Ichsan; Haerul Fatah; Tri Wahyuni; Erni Ermawati; Indriyanti
PROFITABILITAS Vol 5 No 1 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i1.9081

Abstract

Prediksi cacat software merupakan langkah krusial dalam proses pengembangan perangkat lunak guna meminimalkan risiko kerugian akibat kegagalan sistem. Namun, tantangan utama dalam prediksi ini terletak pada ketidakseimbangan data (class imbalance) yang menyebabkan performa model prediksi menjadi tidak optimal. Penelitian sebelumnya menunjukkan bahwa algoritma C4.5 memiliki performa cukup baik, namun masih belum mampu menangani permasalahan ketidakseimbangan kelas secara efektif. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan pendekatan integrasi Class Balancer dan teknik Bagging pada algoritma C4.5 sebagai solusi prediksi yang lebih robust. Hasil penelitian menunjukkan bahwa model Class Balancer+Bagging+C4.5 memberikan peningkatan nilai AUC pada dataset PC4.arff hingga mencapai 0.834 dengan akurasi 83.35%, yang masuk dalam kategori Good Classification. Meskipun rata-rata akurasi menurun dibandingkan C4.5 original, rata-rata nilai AUC meningkat secara signifikan dari 0.599 menjadi 0.672, yang menunjukkan peningkatan kualitas klasifikasi dari Failure menjadi Poor Classification. Temuan ini menunjukkan bahwa integrasi teknik Class Balancer dan Bagging pada algoritma C4.5 mampu meningkatkan kemampuan model dalam mengenali cacat software, terutama dari sisi kestabilan prediksi terhadap data yang tidak seimbang.
EFISIENSI DAN PRODUKTIVITAS ORGANISASI PENGELOLA ZAKAT NASIONAL Nurul Ichsan; Nakania Yumena
IZZI: Jurnal Ekonomi Islam Vol. 2 No. 1 (2022): IZZI: Jurnal Ekonomi Islam
Publisher : Prodi Manajemen Bisnis Syariah

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Abstract

This study aims to analyze the level of efficiency and productivity of the National Zakat Management Organization during the periode 2015 – 2018 using Data Envelopment Analysis and Malmquist Productivity Index in the first stage. The second stage tests the factors that influence the level of efficiency using the Tobit Regression Model. The object of research is the financial statements at BAZNAS, LAZ Al Azhar, and Rumah Zakat. The DEA calculation results show that on average the three OPZ produce a100% efficiency level. However, only Rumah Zakat produced less than 100% optimal efficiency levels in 2015. In the calculation of productivity, on average the total productivity factor was driven by technological change, the change in efficiency resulted in a valeu of 1.000 so that it had no effect on the TFP. Furthermore, in the second stage of processing the Tobit Regression Model shows that fixed assets and current assets have a positive and significant effect on efficiency and operational costs have a negative and significant effect on the level of efficiency. Meanwhile, funds raised, personnel costs, and channeled have no significant effect on the level of efficiency.
Co-Authors Achmad Baroqah Pohan Ady Cahyadi Alif Rizqi Mulyawan Devi Alisa Putri Erni Ermawati Fatah, Haerul Hania Yunsita Adzhani Hasan Basri Indriyanti Mareanus Lase Mohammad Syamsul Azis Nakania Yumena Robi Aziz Zuama Salman Alfarizi Tri Wahyuni