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All Journal JUITA : Jurnal Informatika Journal of Information System Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Paradigma Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Riset Informatika REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH Jurnal AbdiMas Nusa Mandiri J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Insan Digital Transformation Technology (Digitech) J-Icon : Jurnal Komputer dan Informatika Jurnal Informatika dan Bisnis Pengabdian Kepada Masyarakat Indonesia SEAN (ABDIMAS SEAN) Bianglala Informatika
Fintri Indriyani
Universitas Bina Sarana Informatika
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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Penerapan Klastering pada Data Mining dalam Menentukan Status Gizi Anak Balita dengan Menggunakan Algoritma K-Medoids Susliansyah Susliansyah; Heny Sumarno; Hendro Priyono; Linda Maulida; Fintri Indriyani
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15710

Abstract

Status gizi balita merupakan indikator penting yang mencerminkan kesehatan dan perkembangan anak. Penilaian gizi biasanya dilakukan melalui pengukuran berat badan, tinggi badan, serta perhitungan Indeks Massa Tubuh (IMT). Namun, proses klasifikasi secara manual seringkali membutuhkan waktu dan berisiko menimbulkan ketidaktepatan, sehingga diperlukan metode yang lebih efisien. Penelitian ini menggunakan pendekatan data mining dengan algoritma k-medoids untuk mengelompokkan status gizi balita. Algoritma ini bekerja dengan menentukan medoid sebagai pusat kelompok yang mewakili karakteristik balita berdasarkan tinggi, berat, dan IMT. Balita lain kemudian diklasifikasikan sesuai jarak terdekat dengan medoid tersebut. Hasil penelitian menunjukkan bahwa penerapan k-medoids mampu mengelompokkan balita ke dalam kategori normal, kurang gizi, dan obesitas secara lebih sistematis. Temuan ini diharapkan dapat membantu tenaga kesehatan dalam mengidentifikasi balita yang membutuhkan tindakan secara khusus, sehingga mendukung tumbuh kembang anak secara optimal.
Pemodelan dan Prediksi Harga Emas Menggunakan Metode ARIMA pada Data Time Series Yesni Malau; Eni Pudjiarti; Fintri Indriyani; Riswandi Ishak; Asep Sayfulloh; Wahyutama Fitri Hidayat
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.12455

Abstract

This study aims to analyze and predict gold price movements using a time series approach with the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this research are historical daily gold closing prices from 2020 to 2026 obtained from Investing.com, consisting of 1,568 data. The research stages include data collection, preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis, parameter estimation, diagnostic checking, and model accuracy evaluation. The results indicate that the data are stationary with a p-value < 0.05. Based on the identification and model selection process, the ARIMA (3,0,3) model was identified as the best model with an Akaike Information Criterion (AIC) value of 15449.326. Model evaluation results show an RMSE of 120.86, MAE of 95.02, and MAPE of 5.48%. The MAPE value below 10% indicates that the model has good accuracy in predicting gold prices. Therefore, the ARIMA model can be used as an effective approach to predict gold price movements based on historical data.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol. 3 No. 4 (2021): September 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i4.97

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Co-Authors Agam, Alfadh Saifullah Agra Syahputra Ali Haidir Anisa Rahma, Anisa Annisa Dwi Wijayanti Asep Sayfulloh Citra R, Fatmah Eni Irfiani Eni Pudjiarti Fatmah Citra R Firmansyah, Naufal Hendro Priyon Hendro Priyono Heny Sumarno, Heny I Gusti Bagus Wiksuana Indra Riyana Rahadjeng Khoirunnisa, Tasya Ramadhinta Kurniasih R Sihite Linda Maulida Maulida, Linda Muhammad Fahmi Julianto Mukhlis Fahmi Asidiqi Nining Suryani Novian Putra Utama Priyono, Hendro Purwoko, Agus Riswandi Ishak Sanjaya, Yanuar Rizki Susliansyah, Susliansyah Syaiful Anwar Tommi Alfian Armawan Sandi Wahyu, Abdullah Guntur Wahyu, Abdullah Guntur Wahyutama Fitri Hidayat Wawan Nugroho Yahdi Kusnadi, Yahdi Yesni Malau