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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Abdimas Sang Buana Bandung Conference Series: Statistics STATISTIKA
Fauziah Roshafara
Universitas Islam Bandung, Indonesia
Humanities Decision Sciences, Operations Research & Management Education Mathematics Social Sciences Other

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Penerapan K-Means Cluster untuk Pembentukan Portofolio Model Black-Litterman Fitri Amanah; Fauziah Roshafara; Puri Indah Lestari; Salwa Salsabila; Renita Maharani
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34165

Abstract

A portfolio in finance is a collection of investment assets that aims to reduce risk by spreading investment across various assets. In building a portfolio, cluster analysis is used to select assets. K-Means cluster is often used because it is considered efficient for handling large data. In addition, the Black-Litterman Model is used because it can combine investor knowledge into asset allocation efficiently, so that the portfolio becomes more diverse, stable and adaptive to economic conditions, and reflects the investment manager's views. The research results show that k-means cluster analysis can be applied in forming the Black-Litterman model portfolio. Two clusters were obtained, namely cluster I consisting of ADRO, AKRA, BRMS, MIKA, TLKM, UNVR shares, and cluster II consisting of INDF, INKP, SMGR, UNTR. The two clusters were then formed into portfolios I and II. The calculation of expected return and portfolio risk shows that portfolio II produces profits (expected return portfolio) that are greater than portfolio I, namely 0.04445 or IDR 4.445.344,00, and the risk level of portfolio II is also smaller than portfolio I, namely 0.02104 or IDR 2.104.400,00
PELATIHAN PENGGUNAAN GOOGLE FORM DALAM PENGUMPULAN DATA STUNTING DI DESA DAYEUHKOLOT KABUPATEN SUBANG Marliana, Reny Rian; Roshafara, Fauziah; Suliadi, Suliadi; Faladiba, Muthia Nadhira
Jurnal Abdimas Sang Buana Vol 5 No 2 (2024): Jurnal Abdimas Sang Buana - November
Publisher : LPPM Universitas Sangga Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/abdimasusb.v5i2.3686

Abstract

Stunting is a significant threat to the development of future generations' quality in Indonesia, as it can hinder children's growth, reduce learning abilities, and increase the risk of chronic diseases. Therefore, stunting prevention must be carried out through monitoring, starting from the village as the frontline. Dayeuhkolot Village in Subang Regency is one of the villages in West Java Province with a relatively low stunting rate. This success is closely linked to the monitoring efforts carried out by the Human Development Cadres (KPM) in the village. One of the primary responsibilities of KPM is to report data on monitoring the prevention and reduction of stunting. However, the data reporting process still relies on conventional methods, such as paper-based records, which create difficulties in archiving, data recapitulation, and report access. To address these challenges, the utilization of information technology, in accordance with Law No. 23 of 2006, is essential. Google Forms, a user-friendly information technology tool, offers an effective solution. By using Google Forms, stunting data can be collected in real-time and accessed online by relevant stakeholders. Therefore, this community service project was conducted using a science and technology diffusion method, aiming to provide outreach and training on the use of Google Forms for collecting monitoring data on stunting prevention and reduction in Dayeuhkolot Village. The outcomes of this activity show an improvement in the participants' ability to utilize information technology, and the use of Google Forms has proven to overcome the challenges faced by conventional data collection methods.
Time Series Clustering and Mean–Variance Portfolio Modeling on IDX Sharia Growth Stocks Fitri Amanah; Fauziah Roshafara; Nafa Nurhanifah; Novianda Dwi Tanti Ramdani
Statistika Vol. 26 No. 1 (2026): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v26i1.9723

Abstract

Abstract. A stock portfolio represents an investment strategy aimed at maximizing returns while minimizing risk. Stock data, as a form of time series data, relies on historical information to evaluate performance and identify price movement patterns. Consequently, stock selection is a critical component in asset diversification. The novelty of this study is to apply time series clustering methods to stock data and utilize the resulting clusters to construct a mean-variance portfolio. The data used are the weekly closing prices of 10 Sharia-Growth Index stocks for the period December 2022 to November 2024. Using Dynamic Time Warping (DTW) distance with average linkage, two clusters were identified: cluster 1 (MPMX, TLKM) and cluster 2 (MAPI, HEAL, ISAT, AKRA, BMTR, SIDO, KLBF, PWON), with a Silhouette coefficient of 0.9471 indicating strong clustering performance. To construct the mean-variance portfolio, three stocks with positive expected returns were selected: HEAL, ISAT, and MAPI, which are members of Cluster 2. The optimal weights obtained using Lagrange optimization are HEAL (0.49%), ISAT (99.51%), and MAPI (0%). The Lagrange method allocates 0% to MAPI because its risk level (variance of 0.00322) is the highest among the three candidate stocks, thereby not helping to minimize the overall portfolio risk. Therefore, the optimal portfolio formed by combining HEAL and ISAT would have provided an estimated return of 0.56% and a risk of 4.1%.
Klasifikasi Sentimen Publik Terhadap Banjir di Sumatera pada Teks Berita dan Media Sosial X Menggunakan IndoBERT Marini Salmonia Kisa; Fauziah Roshafara
Bandung Conference Series: Statistics 89-98
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.24414

Abstract

Abstract. Flood disasters on Sumatra Island have triggered diverse public responses through online news coverage and social media platform X. This study applies IndoBERT, a BERT-based model that uses pre-trained weights and is subsequently fine-tuned to classify public sentiment regarding the disaster. Data were collected through web scraping from three national news portals, namely Detik, CNBC Indonesia, and Kompas, as well as from social media X using the keyword “banjir Sumatera” between November 28, 2025, and January 9, 2026. After data selection, 2,687 records were obtained, consisting of 1,494 news texts and 1,193 X posts. This study aims to evaluate IndoBERT performance on two data types with different linguistic characteristics. Initial sentiment labeling was conducted using the InSet lexicon, producing three classes: negative, neutral, and positive. On news data, IndoBERT achieved an accuracy of 0.81, precision of 0.81, recall of 0.80, and F1-score of 0.80. On social media X data, it achieved an accuracy of 0.79, precision of 0.78, recall of 0.78, and F1-score of 0.78. These findings indicate that IndoBERT performs better on news texts because their formal language characteristics are more consistent with the model’s pre-training corpus. The results demonstrate that corpus compatibility influences model performance across different Indonesian textual domains. Abstrak. Bencana banjir di Pulau Sumatera memunculkan beragam respons publik melalui pemberitaan media berita online dan media sosial X. Penelitian ini menerapkan IndoBERT berbasis arsitektur BERT dengan memanfaatkan bobot pre-trained yang kemudian digunakan pada proses fine-tuning untuk mengklasifikasikan sentimen publik terkait bencana tersebut. Data dikumpulkan melalui web scraping dari tiga portal berita nasional, yaitu Detik, CNBC Indonesia, dan Kompas, serta media sosial X menggunakan kata kunci “banjir Sumatera” pada periode 28 November 2025 hingga 9 Januari 2026. Setelah proses seleksi, diperoleh 2.687 data yang terdiri atas 1.494 teks berita dan 1.193 cuitan X. Penelitian ini bertujuan mengevaluasi kinerja IndoBERT pada dua jenis data dengan karakteristik yang berbeda. Pelabelan awal dilakukan menggunakan kamus leksikon InSet dan menghasilkan tiga kelas sentimen, yaitu negatif, netral, dan positif. Hasil evaluasi menunjukkan bahwa pada data berita IndoBERT memperoleh accuracy 0,81, precision 0,81, recall 0,80, dan F1-score 0,80. Sementara itu, pada data media sosial X diperoleh accuracy 0,79, precision 0,78, recall 0,78, dan F1-score 0,78. Hasil tersebut menunjukkan bahwa IndoBERT lebih baik dalam mengklasifikasikan sentimen pada teks berita karena karakteristik bahasanya lebih formal sesuai dengan korpus pre-training IndoBERT.
Pemodelan Kemiskinan di Provinsi Jawa Barat dengan Geographically Weighted Regression (GWR) Kurnia Ardi Ferdianto; Fauziah Roshafara
Bandung Conference Series: Statistics 243-250
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25414

Abstract

Abstract. Poverty in West Java Province in March 2024 was recorded at 7.46 percent, or 3.89 million people, with patterns that vary across regions due to spatial effects. This condition indicates that classical regression, which assumes constant parameters across all regions, is less appropriate to use, making it necessary to apply Geographically Weighted Regression (GWR), a method capable of accommodating local variations in relationships between regions. This study aims to model the number of poor population across 27 regencies/cities in West Java Province in 2024 using GWR, with independent variables including the Gini ratio, GRDP growth rate, per capita expenditure, the Community Literacy Development Index (IPLM), minimum wage, and the Labor Force Participation Rate (TPAK). The spatial weighting used an adaptive bisquare kernel function, with the optimum bandwidth determined through the Cross Validation method, and data processing was carried out using R Studio. The model fit test results show an F-value (2.3704) greater than the F-table value (2.235) with a p-value of 0.03764, indicating that GWR is more appropriate than OLS. The GWR model produced different equations for each region, with an AICc value of 33.7708, and significant variables that varied across regions, grouped into six clusters of regencies/cities. Abstrak. Kemiskinan di Provinsi Jawa Barat pada Maret 2024 tercatat sebesar 7,46 persen atau 3,89 juta jiwa, dengan pola yang bervariasi antarwilayah akibat adanya pengaruh spasial. Kondisi ini menunjukkan bahwa regresi klasik yang mengasumsikan parameter konstan di seluruh wilayah kurang tepat digunakan, sehingga diperlukan metode Geographically Weighted Regression (GWR) yang mampu mengakomodasi variasi hubungan antarwilayah secara lokal. Penelitian ini bertujuan memodelkan jumlah penduduk miskin di 27 kabupaten/kota Provinsi Jawa Barat tahun 2024 menggunakan GWR, dengan variabel independen meliputi rasio gini, laju pertumbuhan PDRB, pengeluaran per kapita, IPLM, upah minimum, dan TPAK. Pembobot spasial menggunakan kernel adaptive bisquare dengan bandwidth optimum ditentukan melalui metode Cross Validation, dan pengolahan data dilakukan menggunakan R Studio. Hasil uji kesesuaian model menunjukkan F-hitung (2,3704) > F-tabel (2,235) dengan p-value 0,03764, sehingga GWR lebih sesuai digunakan dibandingkan OLS. Model GWR menghasilkan persamaan berbeda untuk tiap wilayah dengan nilai AICc sebesar 33,7708, serta variabel signifikan yang bervariasi antarwilayah, dikelompokkan menjadi enam kelompok kabupaten/kota.
Peramalan Harga Emas Menggunakan Geometric Brownian Motion Rifa Fadhila; Fauziah Roshafara
Bandung Conference Series: Statistics 291-300
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25726

Abstract

Abstract. Gold is one of the most popular investment instruments because its price fluctuates due to various economic factors. Therefore, a method capable of modeling stochastic price movements is needed. Geometric Brownian Motion (GBM) has been widely used to model financial asset prices because it incorporates both deterministic and stochastic components. This study aims to determine the accuracy of the GBM model in forecasting gold prices based on the Mean Absolute Percentage Error (MAPE) and to forecast gold prices for the next five periods. Daily gold price data from 1 January 2024 to 31 December 2025, obtained from Investing.com, were used in this study. The data were divided into 415 training observations and 104 testing observations. Model parameters were calculated based on return values, and Monte Carlo simulations were performed using 100, 500, and 1000 iterations. Model accuracy was evaluated using MAPE, and the model with the smallest MAPE value was selected for forecasting. The results showed that the Monte Carlo simulation with 500 iterations produced the best model with a MAPE value of 7.8838%. Based on this model, gold prices were projected to increase from 4,321.53 USD/oz in the first period to 4,344.99 USD/oz in the fifth period. These findings indicate that the GBM model provides good forecasting accuracy and is a suitable alternative for short-term gold price forecasting. Abstrak. Emas merupakan salah satu instrumen investasi yang nilainya berfluktuasi akibat berbagai faktor ekonomi, sehingga diperlukan metode yang mampu memodelkan pergerakan harga emas secara stokastik. Penelitian ini bertujuan untuk mengetahui tingkat akurasi model Geometric Brownian Motion (GBM) dalam melakukan peramalan harga emas berdasarkan nilai Mean Absolute Percentage Error (MAPE) serta memperoleh hasil peramalan harga emas untuk lima periode ke depan. Data yang digunakan berupa harga emas harian periode 1 Januari 2024–31 Desember 2025 yang diperoleh dari Investing.com. Data dibagi menjadi 415 data training dan 104 data testing. Parameter model dihitung berdasarkan nilai return, kemudian dilakukan simulasi Monte Carlo sebanyak 100, 500, dan 1000 iterasi. Akurasi model dievaluasi menggunakan MAPE, kemudian model terbaik digunakan untuk melakukan peramalan. Hasil penelitian menunjukkan bahwa simulasi Monte Carlo dengan 500 iterasi menghasilkan model terbaik dengan nilai MAPE sebesar 7,8838%. Berdasarkan model tersebut, harga emas diproyeksikan mengalami tren meningkat selama lima periode ke depan, yaitu dari 4.321,53 USD/oz pada periode pertama menjadi 4.344,99 USD/oz pada periode kelima. Hasil penelitian menunjukkan bahwa model GBM mampu memberikan tingkat akurasi yang baik untuk peramalan harga emas jangka pendek.
Co-Authors Faladiba, Muthia Nadhira Fitri Amanah Fitri Amanah Kurnia Ardi Ferdianto Marini Salmonia Kisa Nafa Nurhanifah Novianda Dwi Tanti Ramdani Puri Indah Lestari Renita Maharani Reny Rian Marliana Rifa Fadhila Salwa Salsabila Suliadi Suliadi