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All Journal Journal of Information Systems Engineering and Business Intelligence JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Tekno Kompak Progresif: Jurnal Ilmiah Komputer 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 Control & Systems Engineering 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

Introduction: Gold price movements attract substantial public and investor attention because gold serves as both a safe-haven asset and a hedging instrument. This study investigates Indonesian public sentiment toward gold price discussions on Platform X using a weakly supervised sentiment-analysis framework. Method: A total of 7,283 Indonesian-language tweets containing the keyword “harga emas” were collected during 2023–2025, with 4,429 tweets retained after preprocessing. Sentiment labels were generated using a domain-specific lexicon and validated through manual annotation. Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and IndoBERT were evaluated using the same train–test partition. Results and Discussion: Manual validation achieved a Cohen’s Kappa of 0.8718, indicating almost perfect inter-annotator agreement, while the lexicon-based labels achieved 70.62% accuracy against the manually annotated reference. IndoBERT achieved the highest performance on weakly supervised labels with 98.31% accuracy and a 98.16% macro F1-score, outperforming SVM, Naïve Bayes, and KNN. However, its accuracy decreased to 69.49% when evaluated against manually annotated data, demonstrating that downstream performance remains strongly influenced by weak-label quality. Conclusion: Weak supervision provides an efficient and scalable approach for large-scale Indonesian financial sentiment annotation, while contextual models such as IndoBERT offer superior classification performance; however, reliable manual validation remains essential to mitigate label noise and improve generalizability.
Imputasi Curah Hujan ERA5 Menggunakan Random Forest dan XGBoost di Maluku Utara Balya Badar Syah; Brina Miftahurrohmah
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3734

Abstract

Climate change increases hydrometeorological disaster risks in North Maluku, yet missing BMKG rainfall data and ERA5's spatial bias hinder precise risk analysis. This study applied point-based Statistical Downscaling using Random Forest (RF) and Extreme Gradient Boosting (XGBoost), with ERA5 precipitation as the single predictor, to impute missing rainfall at four stations. XGBoost achieved lower MSE and RMSE at three stations (Sultan Babullah: MSE 1017.29, RMSE 31.89, MAE 12.31, R² 0.0351; Gamar Malamo: MSE 513.96, RMSE 22.67, MAE 9.91, R² -0.0141; Oesman Sadik: MSE 163.59, RMSE 12.79, MAE 6.27, R² 0.0369), with NRMSE-based accuracy of 83.89 – 87.56%, though RF retained slightly lower MAE. RF was selected at Emalamo (MSE 163.75, RMSE 12.79, MAE 5.62, R² -0.0517, accuracy 85.78%). The models imputed 298 missing days, though the univariate predictor underestimated rainfall above 40 mm. The resulting continuous dataset offers a scientific basis for regional disaster mitigation planning. Key Word: ERA5; Imputation; Statistical Downscaling; Random Forest; XGBoost   Abstrak Perubahan iklim meningkatkan risiko bencana hidrometeorologi di Maluku Utara, sementara kekosongan data curah hujan BMKG dan bias spasial ERA5 menghambat analisis risiko presisi. Penelitian ini menerapkan Statistical Downscaling berbasis titik menggunakan Random Forest (RF) dan Extreme Gradient Boosting (XGBoost), dengan presipitasi ERA5 sebagai prediktor tunggal, untuk mengimputasi kekosongan data curah hujan di empat stasiun. XGBoost menghasilkan MSE dan RMSE lebih rendah di tiga stasiun (Sultan Babullah: MSE 1017,29, RMSE 31,89, MAE 12,31, R² 0,0351; Gamar Malamo: MSE 513,96, RMSE 22,67, MAE 9,91, R² -0,0141; Oesman Sadik: MSE 163,59, RMSE 12,79, MAE 6,27, R² 0,0369), dengan akurasi berbasis NRMSE 83,89 – 87,56%, meski RF tetap mencatat MAE sedikit lebih rendah. RF terpilih di Emalamo (MSE 163,75, RMSE 12,79, MAE 5,62, R² -0,0517, akurasi 85,78%). Model berhasil mengimputasi 298 hari data kosong, meski prediktor univariat underestimate curah hujan di atas 40 mm. Basis data historis kontinu menjadi landasan saintifik bagi mitigasi bencana daerah.  
Co-Authors Angelin Cahyaning Arif Muhammad Iqbal Balya Badar Syah Catur Wulandari Dharmawan, Yogantara Setya Faria, Niswatun Izzati Winda Murti M Rizki Hardika Putri Amelia Shofiana Primi Rusdiana