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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Agriculture and Socio-Economic Journal
Ansyah, Ferdi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences

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Dampak Kredit Usaha Rakyat (KUR) terhadap pengembangan pertanian: Sebuah Tinjauan Literatur Nurdin, Fadilah; Alfira, Alfira; Ansyah, Ferdi; Gustiana, Gustiana; Susanti, Susi; Nurhalyza, Nurhalyza
Agriculture and Socio-Economic Journal Vol 2, No 1 (2025): March
Publisher : Lembaga Penelitian, Pengembangan, Pemberdayaan Potensi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61316/asej.v2i1.108

Abstract

Permasalahan permodalan masih menjadi kendala utama dalam pengembangan sektor pertanian di Indonesia. Program Kredit Usaha Rakyat (KUR) hadir sebagai solusi pembiayaan dengan skema bunga rendah yang ditujukan untuk meningkatkan produktivitas dan kesejahteraan petani. Penelitian ini bertujuan untuk mengkaji dampak KUR terhadap pengembangan pertanian melalui tinjauan literatur artikel ilmiah yang relevan. Metode yang digunakan adalah studi pustaka dengan pendekatan deskriptif kualitatif, yang memfokuskan analisis pada tiga aspek utama: pendapatan petani, produktivitas usahatani, dan kontribusi KUR terhadap pembangunan pertanian. Hasil kajian menunjukkan bahwa KUR berpotensi meningkatkan produktivitas dan pendapatan petani, serta memperkuat posisi tawar dalam sistem ekonomi pedesaan. Namun, keberhasilan program ini sangat bergantung pada faktor sosial ekonomi petani, efektivitas penggunaan dana, serta dukungan kelembagaan seperti penyuluhan dan literasi keuangan. Dengan demikian, agar KUR dapat berfungsi optimal sebagai instrumen pembangunan pertanian yang inklusif, diperlukan integrasi yang kuat antara akses pembiayaan, pendampingan teknis, dan kebijakan pendukung.
Sentiment Classification of Indonesian-Language Roblox Reviews Using IndoBERT with SMOTE Optimization Ansyah, Ferdi; Suryono, Ryan Randy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10155

Abstract

Roblox is a community-based gaming platform that is extremely popular among users of various age groups. Millions of user reviews available on the platform contain valuable information regarding user satisfaction, expectations, and criticisms of the gameplay experience. To extract insights from these reviews, a reliable natural language processing (NLP) approach tailored to the local language context is essential. This study aims to classify sentiments in Indonesian-language user reviews of Roblox into three categories: positive, negative, and neutral. The model used is IndoBERT, a transformer-based model specifically trained to understand the structure and vocabulary of the Indonesian language. One of the main challenges in this study is the imbalance in the number of data points across sentiment classes. To address this, the SMOTE (Synthetic Minority Over-sampling Technique) method is applied to strengthen the representation of minority classes. The dataset consists of thousands of reviews that have been manually labeled by annotators. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the combination of IndoBERT and SMOTE provides significant improvements compared to the baseline approach without oversampling. This research contributes to the development of automated sentiment analysis systems in the Indonesian language, which can be applied across various digital platforms. The implementation of this model can assist game developers and product analysts in efficiently understanding user opinions, thereby driving improvements in service quality and user satisfaction in the future.
Co-Authors alfira, alfira Gustiana Gustiana, Gustiana Nurdin, Fadilah Nurhalyza, Nurhalyza Ryan Randy Suryono Susi Susanti