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All Journal Jurnal Sistem Informasi dan Sains Teknologi Jurnal Sisfotek Global Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Information Technology
Mas'ud Hermansyah
Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance Eka Yuniar; Sherin Ramadhania; Pascawati Savitri Universitasari; Mas'ud Hermansyah; Akas Bagus Setiawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2256

Abstract

Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. The increasing number of sellers on the platform intensifies competition and requires data-driven approaches to understand store performance patterns. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach that integrates clustering, spatial analysis, and feature importance evaluation. The dataset consists of 655 Shopee stores collected on February 18, 2026, including attributes such as number of products, chat response rate, follower count, store rating, store tenure, promotional activity, and seller address. K-Means clustering is applied to segment store performance, while spatial analysis examines the geographic distribution of clusters across Indonesian provinces. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features affecting store competitiveness. The clustering results reveal three distinct store performance groups representing low, medium, and high activity levels. Spatial analysis indicates that provinces with stronger digital ecosystems, particularly West Java and Jakarta, contain a higher concentration of active stores. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. The findings contribute to the development of hybrid data mining frameworks for marketplace analysis and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
OTOMATISASI PENJADWALAN DAN PENGINGAT TUGAS BERBASIS GOOGLE FORM, APPS SCRIPT, DAN GOOGLE CALENDAR UNTUK EFISIENSI MANAJEMEN PROYEK Akas Bagus Setiawan; Mas'ud Hermansyah
Jurnal Sistem Informasi dan Sains Teknologi Vol 7, No 2 (2025): Jurnal Sistem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v7i2.2425

Abstract

Manajemen tugas yang efektif menjadi tantangan utama dalam organisasi modern, terutama dalam mengelola tenggat waktu dan kolaborasi antar anggota tim. Penelitian ini mengembangkan sistem otomatisasi penjadwalan dan pengingat tugas berbasis Google Form, Google Apps Script, dan Google Calendar. Sistem ini memungkinkan data tugas yang diinput melalui Google Form secara otomatis dijadwalkan ke Google Calendar, lengkap dengan pengingat sebelum deadline. Metode penelitian meliputi perancangan form digital, pemrograman Apps Script untuk integrasi data, serta pengujian sistem pada lingkungan kerja nyata. Hasil implementasi menunjukkan peningkatan kepatuhan terhadap deadline dan efisiensi monitoring tugas. Integrasi ini juga mengurangi beban administratif dan potensi human error dalam penjadwalan manual. Sistem yang diusulkan dapat diadopsi oleh berbagai organisasi untuk meningkatkan efektivitas manajemen tugas dan kolaborasi tim.
BERT Sentimen: Fine-Tuning Multibahasa untuk Ulasan Bahasa Indonesia Khen Dedes; Fatimatuzzahra; Mas'ud Hermansyah; Akas Bagus Setiawan; Reza Putra Pradana; Annisa Fitri Maghfiroh Harvyanti
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.585

Abstract

Penelitian ini mengevaluasi pengaruh teknik augmentasi dan fine‑tuning terhadap kinerja model BERT multibahasa pada tugas klasifikasi sentimen ulasan film berbahasa Indonesia. Dataset awal terdiri dari 1.200 ulasan; 80% digunakan untuk pelatihan dan validasi (n = 960) dan 20% untuk pengujian (n = 240). Data pelatihan diperluas melalui augmentasi menjadi 2.880 sampel sintetis untuk keperluan fine‑tuning. Model kemudian di‑fine‑tune pada korpus yang diperluas dan dievaluasi menggunakan metrik akurasi, precision, recall, dan F1. Pada set pengujian diperoleh akurasi 82,5%, precision untuk kelas positif 76,0%, recall 95,0%, dan F1‑score 84,44%. Matriks kebingungan menunjukkan TP = 114, FN = 6, FP = 36, dan TN = 84, yang mengindikasikan sensitivitas tinggi terhadap ulasan positif namun terdapat proporsi false positive yang relatif besar. Temuan ini mengindikasikan bahwa augmentasi meningkatkan kemampuan model dalam menangkap sinyal positif (tingginya recall), namun memerlukan penyesuaian lebih lanjut untuk mengurangi kesalahan prediksi positif (meningkatkan precision). Secara keseluruhan, hasil penelitian menyediakan bukti bahwa BERT multibahasa mampu menangani tugas sentimen berbahasa Indonesia dengan performa memadai apabila didukung strategi augmentasi dan prosedur validasi yang tepat.
Extended LRFM-Based K-Means Clustering for Gym Member Segmentation and Engagement Analysis Mas'ud Hermansyah; Eka Yuniar; Muhammad Bahanan; Fatimatuzzahra Fatimatuzzahra; Khen Dedes
JURNAL SISFOTEK GLOBAL Vol 16, No 2 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.17024

Abstract

The increasing need for data-driven member management in the fitness center industry makes member segmentation important for identifying differences in activity, engagement, and financial contribution and supporting more targeted retention strategies. This study aims to group members of GYM JGM (Jaya Makmur Gym), Jember, based on Length, Recency, Frequency, and Monetary (LRFM) characteristics. The dataset used consists of 150 member data. The research process includes data preprocessing, LRFM feature selection, standardization using Z-Score, and the application of the K-Means algorithm. Determining the optimal number of clusters is done by comparing the Elbow Method, Davies-Bouldin Index (DBI), and Silhouette Score on candidate clusters k = 2 to k = 9. The evaluation results show that three clusters are the optimal configuration because they produce the lowest DBI value of 0.825 and the highest Silhouette Score of 0.430, and show a clear change point in the Elbow Method. The segmentation results produce three groups of members, namely Shorter-Term Moderate-Activity Members, High-Value Active Members, and Long-Term Low-Engagement Members. The segmentation shows that the K-Means algorithm based on LRFM features is able to identify differences in member characteristics and provide an analytical basis for developing retention strategies, increasing engagement, and reactivating GYM JGM members.
Co-Authors Annisa Fitri Maghfiroh Harvyanti Annisa Fitri Maghfiroh Harvyanti Eka Yuniar Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Khen Dedes Muhammad Bahanan Pascawati Savitri Universitasari Reza Putra Pradana Setiawan, Akas Bagus Sherin Ramadhania