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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL PENDIDIKAN TAMBUSAI
Muh. Sunan
IPB University
Computer Science & IT Control & Systems Engineering Social Sciences

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Market Segmentation of Smartphones in Tokopedia Using Fuzzy C-Means Clustering Reyuli Andespa; Muh. Sunan; Maisa Salsabila; Anwar Fitrianto; Kevin Alifviansyah
Jurnal Pendidikan Tambusai Vol. 9 No. 3 (2025): Desember
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Penelitian ini bertujuan untuk menganalisis segmentasi pasar smartphone pada platform e-commerce Tokopedia dengan menggunakan algoritma Fuzzy C-Means (FCM). Dataset diperoleh melalui proses web scraping terhadap 428 produk smartphone, yang mencakup variabel harga, rating, volume penjualan, nama toko, dan lokasi penjual. Tahapan analisis meliputi data cleaning, eksplorasi deskriptif, penentuan jumlah klaster optimal menggunakan metode Elbow, serta penerapan algoritma FCM untuk membentuk segmen pasar yang homogen. Hasil penelitian mengidentifikasi tiga klaster optimal, yaitu: Budget, Mid-range, dan Premium. Segmen Budget terdiri dari 337 produk dengan rata-rata harga Rp1.623.426, rating rata-rata 4,81, dan volume penjualan rata-rata 301 unit. Segmen Mid-range mencakup 20 produk dengan rata-rata harga Rp3.462.007, rating 2,25, dan penjualan 22 unit. Sementara itu, segmen Premium berisi 102 produk dengan rata-rata harga Rp6.434.597, rating 4,92, dan penjualan 201 unit. Temuan ini menunjukkan bahwa konsumen Tokopedia cenderung lebih menyukai smartphone yang terjangkau namun tetap berkualitas, sementara segmen Mid-range menghadapi tantangan dalam hal positioning dan daya saing pasar.
IndoBERT Optimization for Sentiment Analysis on DeepSeek App Reviews Muh. Sunan; Unique Desyrre A. Resiloy; Desy Endriani; Cici Suhaeni; Bagus Sartono; Gerry Alfa Dito
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.107507

Abstract

In the digital era, sentiment analysis is important to evaluate public opinion, especially in the context of Play Store apps with Indonesian-language reviews. This research aims to improve the performance of the IndoBERT model in sentiment analysis of DeepSeek app reviews by using data augmentation and hyperparameter tuning techniques. Data augmentation is done through the back-translation technique, while the hyperparameters tested include the number of epochs, learning rate, and batch size. Experimental results show that the combination of data augmentation with epoch 10, learning rate 2e-5, and batch size 16 produces the highest accuracy of 93.95% and F1-score of 0.94, with better stability than the model without augmentation. The model without augmentation showed fluctuations in performance, indicating overfitting in some configurations. These findings confirm the importance of applying augmentation techniques and hyperparameter tuning in improving the accuracy and stability of sentiment analysis models, and contribute to the development of NLP models for Indonesian and other resource-constrained languages.
Co-Authors Bagus Sartono Cici Suhaeni Desy Endriani Fitrianto, Anwar Gerry Alfa Dito Kevin Alifviansyah Maisa Salsabila Reyuli Andespa Unique Desyrre A. Resiloy