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All Journal Cakrawala Pendidikan Jurnal Manajemen Pendidikan: Jurnal Ilmiah Administrasi, Manajemen dan Kepemimpinan Pendidikan Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab Lisanuna: Jurnal Ilmu Bahasa Arab dan Pembelajarannya Indonesian Journal of Islamic Educational Management JURNAL PENDIDIKAN TAMBUSAI JOURNAL OF SCIENCE AND SOCIAL RESEARCH JISIP: Jurnal Ilmu Sosial dan Pendidikan JURNAL MANAJEMEN PENDIDIKAN ISLAM AL-IDARAH Gema Wiralodra G-Tech : Jurnal Teknologi Terapan Ta'lim al-'Arabiyyah: Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban International Journal of Arabic Language Teaching IJALT Didaktika: Jurnal Pemikiran Pendidikan Jurnal Cahaya Mandalika Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) LEARNING : Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran Thawalib: Jurnal Kependidikan Islam Tarbiyatuna: Jurnal Pendidikan Ilmiah Ukazh : Journal of Arabic Studies Chalim Journal of Teaching and Learning Abdi Teknoyasa Jurnal Pendidikan Islam IRSYADUNA: Jurnal Kemahasiswaan Innovative: Journal Of Social Science Research Kharisma: Jurnal Administrasi dan Manajemen Pendidikan Journal of Social Science Alsinatuna Muhibbul Arabiyah: Jurnal Pendidikan Bahasa Arab Ngabekti: Jurnal Pengabdian kepada Masyarakat Proceeding of International Conference on Humanity Education and Society Journal of Informatics Development LEADERIA : Jurnal Manajemen Pendidikan Islam Jurnal Keislaman Al-Munawwarah
Asy’ari, Hasyim
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Religion Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Journal : Journal of Informatics Development

 1 
Using Machine Learning Techniques to Predict Financial Distress in Rural Banks in Indonesia Urrochman, Maysas Yafi; Asy’ari, Hasyim; Ro’uf, Abdur
Journal of Informatics Development Vol. 2 No. 2 (2024): April 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v2i2.1341

Abstract

LPS liquidated about 100 people's Rural Banks between 2015 and 2019, indicating that these banks are facing significant issues, particularly financial distress. This study seeks to forecast financial distress through a two-stage classification and regression approach. Researchers used financial report data from Rural Banks in Indonesia from 2015 to 2019, covering a total of 150 banks, with 50 financial ratios from bankrupt banks and 100 from those that remained operational. Data was analyzed for two consecutive years prior to any bankruptcy declarations. The classification targets are categorized into five classes: very healthy, healthy, quite healthy, unhealthy, and distressed. The study results demonstrate that the two-stage classification and regression method can effectively predict the onset of financial distress. This is validated by the classification outcomes using the Decision Tree Algorithm, which achieved an f1-score accuracy of 88%. The evaluation of timing predictions using Random Forest Regression revealed a mean absolute error of 1.2 months and a mean absolute percentage error of 3%. These predictions can assist regulators, bank management, and investors in making better-informed decisions to address financial distress risks in Rural Banks. The superior performance of the Decision Tree Algorithm over Naïve Bayes in classifying financial distress highlights the potential of machine learning techniques in providing robust tools for early warning systems, aiding stakeholders in making informed decisions to mitigate risks.
Aspect-Based Sentiment Analysis of Tumpak Sewu Waterfall Tourist Reviews Using the Naive Bayes Classifier (NBC) Method Urrochman, Maysas Yafi; Asy’ari, Hasyim; Ro’uf, Abdur
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1758

Abstract

With the increasing popularity of Tumpak Sewu Waterfall, the volume of visitor reviews on Google Maps continues to grow. These reviews contain valuable insights into tourists’ experiences; however, conducting an in-depth manual analysis is inefficient. This study aims to perform aspect-based sentiment analysis on visitor reviews of Tumpak Sewu Waterfall using the Naive Bayes Classifier (NBC) method. This approach enables the classification of sentiments positive, negative, and neutral based on specific aspects such as facilities, accessibility, and natural scenery. Review data were collected from online platforms and processed through stages of text preprocessing and feature extraction before being trained using the NBC model. The results show that the model effectively classifies review sentiments with a high level of accuracy and provides detailed insights into which aspects most influence visitor satisfaction. These findings not only demonstrate the effectiveness of the Naive Bayes Classifier in aspect-based sentiment analysis tasks but also offer data-driven strategic recommendations for tourism managers to enhance service quality and improve visitor experience in the future.
Co-Authors Aan Komariah Adhimah, Faizatul Akhbar, Khoiril Akhmad Sirojuddin, Akhmad al hidayah, Hanik Aliyazid, Moh. Wakil Amara, Ressa Ananda Damanik, Grista Anwar, Muhammad Saidun Auladina, Adinda Maula Aulia Prasetya, Gilang Azahra, Salsabila Baroroh, Fatimatul Elda Yudistari ElJack Ibrahim, Nahla Ibrahim Elok Rufaiqoh, Elok Ernawati, Siti Fajriah, Fellisa Juliana Nur Fangestu, Indra Wahyuni Firli Fannah, Siti Faridah, Ai Firda, Firda Zahraningtyas Ghina Tsuraya, Febia Hasyim Asy’ari Heni Pujiastuti Hindarsih, Tri Puji Humairoh, Widya Belqis Ibrahim ElJack Ibrahim, Nahla Isy, Lesca Kamalatul Jejen Musfah, Jejen Jemani, Abdurahman Kamila, Nur Indah Latifah, Hilmiyatul Lia Yuliana Likia, Nurdin M. Ali Haidar, M. Ali Maftuhah Maftuhah Mamluatul Hasanah Mani, Sulfa Maulidya, Siska Meilani, Intan Wulan Muhammad Labib, Muhammad Mulyana, Arip Mustofa, Maulana Nana Supriatna Nikma, Sulalatun Nova Wijaya, Irena Nur Fajariya, Ulfa Nurrochim, Nurrochim Pramono, Diah Atika Prasetya, Gilang Aulia Qomarun Rahman, Fathor Ratnaningsih, Sita Rizqi, Fathir Naufal Ar Ronim Azizah Ro’uf, Abdur Saadah, Gina Aniatus Saadah, Rifngatus Samroh, Siti Sa’idah, Syaella Shadicky, Zakiyus Silviani, Annisa Siti Nur Halizah Sutaman, Sutaman Syaiful Rizal Syuhadak, Syuhadak Urrochman, Maysas Yafi Vebrianto, Rian Wanda, Amelia Ayu Yuswanto, Anang Zahruddin Zainal, Muhammad Rizki Zamroni, Afif Zamroni, M Afif Zulfanuria, Alfin Khoiri Zulkarnaen, Adnan