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All Journal Journal of Information Systems Engineering and Business Intelligence JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Tekno Kompak Indonesian Journal of Data and Science Jurnal Abdi Masyarakat Indonesia
Brina Miftahurrohmah
Program Studi Sistem Informasi, Universitas Internasional Semen Indonesia, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Public Health

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Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk Brina Miftahurrohmah; Catur Wulandari; Yogantara Setya Dharmawan
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.11-21

Abstract

Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).
DINAMIKA UMKM DI GRESIK - JAWA TIMUR PADA PERKEMBANGAN ERA DIGITAL DENGAN PENDEKATAN SISTEM DINAMIK Putri Amelia; Brina Miftahurrohmah
Jurnal Tekno Kompak Vol 14, No 1 (2020): Februari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v14i1.532

Abstract

Perkembangan UMKM ini juga harus bisa mengimbangi globalisasi pasar yang menuntut peningkatan daya saing dan strategi bisnis yang saat ini tengah beralih ke era digital. Dari survey yang dilakukan BPS (2016) 67% UMKM mengalami kendala. Kendala yang sering dihadapi salah satunya adalah pemasaran (31%). Berdasarkan hasil yang ada, tidak semua UMKM yang ada melakukan implementasi digital didalam membantu kegiatan marketing. Oleh karena itu penelitian kali ini akan dibahas dinamika perubahan permintaan khususnya UMKM makanan perkembangan era digital dengan menggunakan metode pendekatan sistem dinamik. Melalui penggambaran model simuasi akan digambarkan faktor eksternal pelanggan dan faktor internal penjual. Penggambaran model akan dapat diketahui perubahan permintaan sebelum dilakukan implementasi marketing didalam proses bisnis dan saat dilakukan implementasi.
Penguatan Citra Telur Asap Khas Desa Bambang Melalui Penyuluhan Citra Merek (Brand Image) dan Desain Kemasan Niswatun Faria; Brina Miftahurrohmah; Izzati Winda Murti
Jurnal Abdi Masyarakat Indonesia Vol 2 No 5 (2022): JAMSI - September 2022
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.443

Abstract

Sebagian masyarakat desa Bambang Kecamatan Turi Kabupaten Lamongan merupakan pelaku UMKM dengan memproduksi telur asin dan asap kemudian menjual produk tersebut di lingkungan desa sekitar. Pelaku UMKM di Desa tersebut berharap dapat meningkatkan penjualan produk dan memperluas pasar. Salah satu kunci penting dalam dunia usaha adalah membangun citra merek (brand image) produk agar produk mampu bersaing dan bertahan di tengah persaingan bisnis yang semakin panas. Permasalahan yang dihadapi oleh pelaku UMKM di Desa Bambang adalah kurangnya pengetahuan terkait pentingnya merek sebagai identitas, desain kemasan produk dan pemasaran secara daring. Tujuan dari dilaksanakannya kegiatan ini adalah untuk meningkatkan potensi telur asin dan asap sebagai oleh-oleh khas desa Bambang melalui penyuluhan dan pendampingan tentang pentingnya citra merek dan bagaimana cara memasarkan produk secara daring. Berdasarkan hasil kuisioner yang disebarkan setelah kegiatan, didapatkan bahwa masyarakat semakin memahami merek dan bagaimana cara mendapatkan merek serta perlunya kemasan yang baik untuk dapat menarik minat pelanggan.
DEVELOPMENT OF INFORMATION SYSTEM FOR EMPLOYEE PERFORMANCE ASSESSMENT AT HASNUR CENTRE USING 360° ASSESSMENT Arif Muhammad Iqbal; Angelin Cahyaning; Shofiana Primi Rusdiana; Brina Miftahurrohmah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.5180

Abstract

Hasnur Centre is the CSR institution of Hasnur Group dedicated to the development of human resources in South Kalimantan. The performance assessment for Hasnur Centre employees currently relies on a conventional and unidirectional brief fill-in-the-blank method, reflecting the viewpoint of superior, indicating a need for an adjustment in the employed method. Furthermore, the employee evaluation process at Hasnur Centre still relies on a simple Google Form. Therefore, there is a need for the development of information system integration that can automate employee performance assessment at Hasnur Centre. The system is developed gradually according to the needs of the HR Admin, utilizing the Spiral development method. The tools include Use Case Diagrams, PHP as the programming language, CodeIgniter as the system development framework, and MySQL. This research has resulted in the Employee Performance Assessment Information System for Hasnur Centre employees, introducing a novelty by integrating the 360° Assessment method based on predetermined perspectives and sub-perspectives using a Likert Scale combined with a brief qualitative input method in which evaluators provide written feedback on the assessed employees. The combination of these two methods results in a more measurable, objective, and unbiased performance evaluation, making it a reliable tool for the Executive Director of Hasnur Centre in making decisions related to employee performance
Weakly Supervised Sentiment Analysis of Gold Price Discussions Using Conventional Machine Learning and IndoBERT M Rizki Hardika; Brina Miftahurrohmah
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.445

Abstract

Gold price movements are closely monitored by investors because gold functions as both a safe-haven asset and a hedging instrument. With the rapid growth of social media, public discussions provide valuable insights into market sentiment. This study proposes a weakly supervised sentiment classification framework for analyzing Indonesian public sentiment toward gold prices on Platform X. A total of 7.283 Indonesian-language tweets were collected using the keyword “harga emas” during the 2023–2025 period. After preprocessing, 4.429 tweets were retained for sentiment labeling and model development. Sentiment labels were automatically generated using a domain-specific lexicon and subsequently validated through manual annotation to assess label reliability before model training. Four classification models, namely Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and IndoBERT, were evaluated. Manual validation produced a Cohen's Kappa coefficient of 0.8718, indicating almost perfect agreement between annotators, while the lexicon-based labels achieved an accuracy of 70.62% against the manually annotated gold-standard dataset. Among the evaluated models, IndoBERT achieved the best performance with an accuracy of 98.31% and a Macro F1-score of 98.16%, outperforming SVM, Naïve Bayes, and KNN. However, evaluation using the manually annotated dataset yielded an accuracy of 69.49%, indicating that downstream classification performance remains dependent on the quality of weakly supervised labels. These findings demonstrate that weak supervision is an efficient strategy for large-scale sentiment annotation while emphasizing the importance of label validation for reliable sentiment classification in Indonesian financial social media
Co-Authors Angelin Cahyaning Arif Muhammad Iqbal Catur Wulandari Dharmawan, Yogantara Setya Faria, Niswatun Izzati Winda Murti M Rizki Hardika Putri Amelia Shofiana Primi Rusdiana