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All Journal bit-Tech Journal of Advanced in Information and Industrial Technology (JAIIT) JoMMiT : Jurnal Multi Media dan IT
Putri, Shafira Amanda
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Analisis Segmentasi Sentra Wisata Kuliner untuk Optimalisasi Omzet UMKM di Surabaya Menggunakan Metode Agglomerative Hierarchical Clustering: Data Mining Selayanti, Nabilah; Putri, Shafira Amanda; Fahrudin, Tresna Maulana
JoMMiT Vol 8 No 2 (2024): Artikel Jurnal Volume 8 Issue 2, Desember 2024
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v8i2.1351

Abstract

Peran Usaha Mikro, Kecil dan Menengah (UMKM) menjadi salah satu peranan yang dominan dalam struktur perekonomian Indonesia. UMKM menghadapi tantangan dalam hal keragaman karakteristik dan kondisi usaha yang berbeda-beda, salah satunya adalah Sentra Wisata Kuliner (SWK). Meskipun banyak SWK terletak di lokasi strategis dengan fasilitas yang memadai, mereka belum memberikan pendapatan optimal bagi para pelaku usaha. Pada penelitian ini dilakukan analisis pengelompokan SWK Usaha Mikro, Kecil, dan Menengah (UMKM) di kota Surabaya Tujuannya adalah untuk mengelompokkan SWK berdasarkan kesamaan karakteristik seperti luas sentra, kapasitas, jumlah pelaku usaha, dan produktivitas menggunakan metode Agglomerative Hierarchical Clustering (AHC). Setelah melalui tahapan pra-pemrosesan data, penentuan metode cluster terbaik menggunakan korelasi cophenetic, dan validasi jumlah cluster optimal dengan silhouette coefficient, diperoleh hasil pengelompokan yang membagi SWK menjadi 3 cluster berdasarkan pengukuran jarak menggunakan single linkage, average linkage, complete linkage, dan ward linkage. Complete linkage memberikan performa yang baik yakni nilai cophenetic sebesar 0.8734 dan nilai silhouette coefficient sebesar 0.4864. Interpretasi cluster yang didapatkan yakni cluster 1 menunjukkan stabilitas dan aktivitas ekonomi tinggi, cluster 2 mencakup mencakup sentra yang kurang berkembang, dan cluster 3 menunjukkan karakteristik sebagai pusat-pusat usaha yang sangat besar dan berhasil. Hasil pengelompokan ini dapat digunakan sebagai dasar untuk merancang strategi dan program pengembangan UMKM secara lebih efektif dan berkelanjutan.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Damaliana, Aviolla Terza; Muhammad Nasrudin; Riyantoko, Prismahardi Aji; Nabilah Selayanti; Putri, Shafira Amanda
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
Analysis of the LQ45 Stock Portfolio Using Mean–Variance Method and Cornish–Fisher Expansion Putri, Shafira Amanda; Trimono, Trimono; Muhaimin, Amri
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3640

Abstract

Public interest in stock market investment in Indonesia has increased alongside growing awareness of financial planning and portfolio management. The LQ45 Index, consisting of stocks with high liquidity, large market capitalization, and strong fundamentals, is widely used as a benchmark for portfolio analysis. However, many portfolio studies still rely on conventional Value at Risk (VaR), which assumes normally distributed returns and may underestimate extreme losses, making it less effective in capturing tail risk. This study addresses this research gap by integrating Mean–Variance Optimization (MVO) with the Cornish–Fisher VaR approach, which incorporates skewness and kurtosis to accommodate non-normal return distributions. Daily adjusted closing price data of LQ45 stocks from January to December 2025 were obtained from Yahoo Finance, and logarithmic returns were calculated. Based on the highest Sharpe Ratios, BRPT, EXCL, and ANTM were selected as portfolio constituents. Correlation analysis shows low dependency among the selected stocks, supporting diversification, while normality tests confirm deviations from normality, justifying the use of Cornish–Fisher VaR. The optimal portfolio allocates 10.6% to BRPT, 65.5% to EXCL, and 23.9% to ANTM, producing an expected return of 65.7%, portfolio risk of 26.2%, and a Sharpe Ratio of 2.5, indicating strong risk-adjusted performance. Cornish–Fisher VaR estimates potential losses of 2.23%, 3.09%, and 5.30% at the 90%, 95%, and 99% confidence levels. These results demonstrate that combining MVO and Cornish–Fisher VaR offers a more robust framework for portfolio optimization in the Indonesian stock market.
Co-Authors Abda Abda Amri Muhaimin Damaliana, Aviolla Terza Fahrudin, Tresna Maulana Muhaimin, Amri Nabilah Selayanti Riyantoko, Prismahardi Aji Selayanti, Nabilah Trimono, Trimono