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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengabdian kepada Masyarakat Nusantara Best : Journal of Applied Electrical, Science and Technology JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) SENTRI: Jurnal Riset Ilmiah Jurnal Cakrawala Maritim ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Brian, Thomas
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Journal : INTEGER: Journal of Information Technology

 1 
Implementasi Perbandingan Algoritma k-Means dan DB-Scan Pada Beban Listrik Rumah Tangga Aisyi Maulidhia, Alief Nur; Widyastuti, Indri Ika; Sukarno, Friska Intan; Tsany, Rahmat Basya Sharys; Brian, Thomas
INTEGER: Journal of Information Technology Vol 10, No 1 (2025): April
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7479

Abstract

Konsumsi daya listrik rumah tangga merupakan aspek penting dalam manajemen energi, terutama dalam upaya meningkatkan efisiensi penggunaan listrik. Clustering merupakan salah satu metode yang dapat digunakan untuk mengelompokkan pola konsumsi daya listrik berdasarkan karakteristik tertentu. Penelitian ini bertujuan untuk membandingkan performa algoritma K-Means dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) dalam mengelompokkan beban listrik rumah tangga. Dataset yang digunakan berasal dari Household Electric Power Consumption yang tersedia di Kaggle, yang mencatat konsumsi daya listrik rumah tangga dalam satuan menit selama beberapa tahun. Data yang telah diproses melalui tahapan pembersihan, normalisasi, dan reduksi dimensi kemudian diklasterisasi menggunakan K-Means dan DBSCAN. Evaluasi performa dilakukan berdasarkan Silhouette Score dan Davies-Bouldin Index untuk mengukur kualitas klaster yang dihasilkan oleh kedua algoritma. Hasil penelitian menunjukkan bahwa K-Means lebih unggul dalam mengelompokkan data dengan pola konsumsi yang jelas dan terdistribusi secara linier, sementara DBSCAN lebih efektif dalam mendeteksi klaster dengan kepadatan yang bervariasi serta mengidentifikasi data pencilan (outliers). Dengan demikian, pemilihan algoritma klasterisasi sangat bergantung pada karakteristik data dan tujuan analisis yang ingin dicapai.
Perbandingan Model Decision Tree, Support Vector Machine dan K-Nearest Neighbors untuk Memprediksi Kualitas Air Minum Brian, Thomas; Maulidhia, Alief Nur Aisyi; Sholikhah, Evi Nafiatus; Wibowo, Sekarsari
INTEGER: Journal of Information Technology Vol 10, No 1 (2025): April
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7523

Abstract

The need for drinking water is increasing so that appropriate method support is needed to determine water potability. In this study, machine learning models will be implemented including Decision Tree, Support Vector Machine, and K-Nearest Neighbors to determine the best model in classifying drinking water quality from the Kaggle Water Quality dataset. The dataset consists of 3,276 data with 9 parameters consisting of ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes and Turbidity, and one Potability attribute as a target that indicates the feasibility of consumption. This study will apply several machine learning models consisting of Decision Tree, Support Vector Machine, and K-Nearest Neighbors. Based on the results of the trial using 20% and 30% testing data, the results are close to the same for the confusion matrix model evaluation metrics (Accuracy, F1 Score, Precision and Recall). So it can be concluded that the Decision Tree classification model gets the best Accuracy value among other classification models of 70.50% on 20% testing data and 70.98% on 30% testing data. However, the one chosen as the final classification model is Support Vector Machine because it has the highest value by meeting three requirements with F1 Score, Precision and Recall values of 82.40% each) from the four requirements tested.
Co-Authors Abda Abda Aisyi Maulidhia, Alief Nur Al Hazman, Muhammad Alwi Sina Khaqiqi Annisa, Aulia Ardiana, Mirza Atmiasri Augustino, Immanuel Freddy Bone, Iskia Ipan Dua’ Fais Hamzah Gastriani, Ovi Prina Jami’in, Mohammad Abu Maulidhia, Alief Nur Aisyi Nisazarifa, Adristi Nizar Zulfika, Dicki Nur Rahayu, Putri Parman Pratama , Moh. Andris Pujiputra, Anggarjuna Puncak Puncak Pujiputra, Anggarjuna Putri Nur Rahayu Rahayu, Putri Nur Rahayuningsih, Siti Sanwidi, Ardhi Sholikah, Evi Nafiatus Sholikhah, Evi Nafiatus Sukarno, Friska Intan Tsany, Rahmat Basya Shahrys Tsany, Rahmat Basya Sharys Wibowo, Sekarsari Widyastuti, Indri Ika Zulfika, Dicki Nizar