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All Journal 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.
Co-Authors Amri Muhaimin Damaliana, Aviolla Terza Fahrudin, Tresna Maulana Muhammad Nasrudin Nabilah Selayanti Riyantoko, Prismahardi Aji Selayanti, Nabilah