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All Journal Knowbase : International Journal of Knowledge in Database The Scientific Journal of Information Systems Jurnal Saintekom (Sains dan Teknologi Komputasi)
Ningrum, Leny
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science

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A COMPARATIVE REVIEW OF CLUSTERING AND CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYTICS Zogara, Lukas Umbu; Ningrum, Leny
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.179

Abstract

These days, there's so much data being created all the time. It’s honestly getting hard to keep up.That’s where data mining comes in. Basically, people use it to make sense of all this huge amount ofinformation, and there are two main ways to do it: clustering and classification. I found that there area bunch of algorithms for both, like K-Means, DBSCAN, and Hierarchical Clustering for clustering,and then there’s Decision Tree, Naïve Bayes, SVM, and Random Forest for classification. Each ofthese has its own strengths and weaknesses depending on the data you’re working with. The point ofthis paper was really to see how these algorithms perform and to give people an idea of which onemight work best depending on the situation. What we found is that no algorithm is perfect foreverything. So, choosing the right one really comes down to understanding the data and figuring outwhat you're trying to get out of it.
Classification of Referral Decision Recommendations in Community Health Centers Using the K-Nearest Neighbor Approach Ningrum, Leny; Salsabila, Nisrina
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.10137

Abstract

management, including determining patient referral decisions at community healthcenters. However, these decisions often still depend on the subjective assessment ofmedical personnel, resulting in an inaccurate and ineffective process of identifyingdiabetes patient management. The purpose and objective of this research anddevelopment is to identify diabetes patient management for referral decisionrecommendations at Puskesmas using the K-Nearest Neighbor (KNN) approach toobtain a more accurate and effective process and results so that Puskesmas can morequickly provide appropriate follow-up based on patient laboratory test results. Thedata used in this study was diabetes patient data at Puskesmas, using variables suchas age, systolic and diastolic blood pressure, glucose tests, and referral to hospitals asthe target class. The results of the research and classification evaluation using theConfusion Matrix in KNN modeling based on this data showed that the number ofpatients included in TP=41, TN=38, FP=1, and FN=4, with an accuracy of 94.02%,precision of 97.62%, recall of 91.11%, and F1-Score of 94.25%. These values arecategorized as very good because they are able to predict classes correctly at themodeling stage. Thus, this study is considered feasible as a support for referraldecision recommendations in identifying the treatment of diabetic patients atPuskesmas
Penerapan Metode Weighted Moving Average (WMA) untuk Prediksi Ketersediaan Barang Dagang Pada Perusahaan Farmasi Budiawan, Muhamad; Ningrum, Leny
Jurnal SAINTEKOM (Sains dan Teknologi Komputasi) Vol 1 No 3 (2025): Desember 2025
Publisher : Lembaga Penelitian, Pengembangan, dan Pengabdian Kepada Masyarakat (LP3M) – Universitas Binaniaga Indonesia (UNBIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jskom.v1i3.60

Abstract

Kebutuhan akan obat-obatan telah menjadi kebutuhan primer di kalangan penyedia obat[1]obatan. Permintaan yang tinggi dari berbagai apotek dan rumah sakit menjadikan perusahaan farmasi harus secara cermat dan konsisten menyediakan serta mengendalikan persediaan obat-obatan sesuai dengan tingginya jumlah permintaan. Permasalahan yang muncul dalam proses distribusi sediaan farmasi adalah kemampuan dalam mengelola persediaan dengan efisien. Berdasarkan dari hasil wawancara yang telah dilakukan terdapat indikasi bahwa proses prediksi ketersediaan barang yang dilakukan selama ini masih belum efektif, yang dapat berdampak negatif pada operasional dan keuangan perusahaan. Pada penelitian ini dibuat sebuah prototype system aplikasi yang dapat digunakan untuk memprediksi ketersediaan barang dagang pada perusahaan farmasi dengan menggunakan metode Weighted Moving Average (WMA). Pada prototype sistem aplikasi diterapkannya variabel dari data penjualan barang pada periode persediaan barang sebagai acuan dalam melakukan proses perhitungan prediksi ketersediaan barang. Dalam penelitian ini dilakukannya Uji kelayakan aplikasi oleh tim ahli sistem informasi dengan nilai kelayakan sebesar 100% yang berarti aplikasi ini sangat layak digunakan dan uji kelayakan pengguna sebesar 89%, sehingga aplikasi ini sangat layak digunakan dalam proses prediksi ketersediaan barang. Lalu telah dilakukan uji akurasi mengenai hasil prediksi yang dilakukan menggunakan Mean Absolute Percentage Error (MAPE) dengan hasil persentase sebesar 18% yang diketagorikan sebagai prediksi yang “Baik”.
Co-Authors Budiawan, Muhamad Lukas Umbu Zogara Salsabila, Nisrina