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All Journal International Journal of Electrical and Computer Engineering INTEGER: Journal of Information Technology JCES (Journal of Character Education Society) Journal of Computer, Electronic, and Telecommunication (COMPLETE) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer The Indonesian Journal of Computer Science
Sholikhah, Evi Nafiatus
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Application of K-Nearest Neighbor (KNN) Algorithm to Predict Drinking Water Quality Brian, Thomas; Sholikhah, Evi Nafiatus; Aisyi Maulidhia, Alief Nur; Wibowo, Sekarsari
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 5 No 1 (2025): JTECS Januari 2025
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v5i1.6715

Abstract

Peningkatan kebutuhan akan air minum berkualitas menuntut pengembangan metode yang andal untuk menentukan potabilitas air. Penelitian ini bertujuan untuk menerapkan algoritma K-Nearest Neighbors (KNN) dalam memprediksi kualitas air minum berdasarkan dataset Water Quality dari Kaggle. Dataset mencakup 3.276 data dengan 9 parameter, seperti pH, kekerasan, dan kandungan karbon organik, serta satu atribut target yang menunjukkan kelayakan konsumsi. Penelitian ini akan menerapkan algoritma KNN dengan berbagai nilai (k), dan mengevaluasi kinerja model menggunakan metrik akurasi dan Jaccard Similarity. Hasil penelitian menunjukkan bahwa algoritma KNN dalam memprediksi kualitas air minum mencapai akurasi terbaik sebesar 58% pada nilai (k) = 2, hasil ini menunjukkan bahwa metode ini cukup baik meskipun perlu pengembangan lebih lanjut dengan metode lain untuk meningkatkan akurasi. Penelitian ini memberikan kontribusi pada implementasi teknologi pembelajaran mesin dalam pengelolaan sumber daya air.
DIGITALISASI SISTEM KEUANGAN DI YAYASAN PENDIDIKAN AL-ISLAH MELALUI PEMBUATAN SISTEM INFORMASI KEUANGAN BERBASIS WEBSITE Annisa, Aulia Rahma; Adhitya, Ryan Yudha; Sholikhah, Evi Nafiatus; Rahayu, Putri Nur; Andika, Yudi; Ardiana, Mirza
JCES (Journal of Character Education Society) Vol 8, No 1 (2025): Januari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jces.v8i1.29123

Abstract

Abstrak: Dalam Era Society 5.0, kemajuan teknologi digital telah memberikan dampak yang signifikan di berbagai sektor, termasuk dalam dunia pendidikan. Yayasan Pendidikan Al-Islah Surabaya, yang mencakup berbagai lembaga pendidikan mulai dari KB, TK, SD, SMP, hingga SMK, masih menggunakan metode pencatatan transaksi keuangan secara manual. Pada Tahun Ajaran 2024/2025, jumlah siswa yang terdaftar di Yayasan tersebut mencapai 1.723 siswa. Dengan jumlah siswa yang cukup besar, tantangan seperti kesalahan dalam pencatatan, ketidakefisienan, dan kurangnya transparansi dalam pengelolaan keuangan menjadi semakin nyata, sehingga transformasi digital dalam sistem keuangan sangat diperlukan. Oleh karena itu, diperlukan pengembangan sistem informasi keuangan berbasis web untuk mendigitalisasi proses pencatatan keuangan. Sistem ini diharapkan dapat meningkatkan akurasi dan efisiensi dalam pengelolaan dana pendidikan. Selain itu, sistem ini akan memastikan akuntabilitas dan transparansi yang tinggi, yang sangat diperlukan dalam pengelolaan dana yang berasal dari berbagai sumber, termasuk pemerintah, donator, dan pihak pemberi hibah lainnya.Abstract:  In the Era of Society 5.0, advancements in digital technology have had a significant impact on various sectors, including the field of education. Al-Islah Surabaya Education Foundation, which includes various educational institutions ranging from kindergarten, elementary school, junior high school, to vocational school, still uses manual methods for recording financial transactions. In the 2024/2025 Academic Year, the number of students registered at the Foundation reached 1,723 students. With a sufficiently large number of students, challenges such as recording errors, inefficiencies, and a lack of transparency in financial management become increasingly apparent, making digital transformation in the financial system essential. Therefore, the development of a web-based financial information system is necessary to digitize the financial recording process. This system is expected to improve the accuracy and efficiency in the management of educational funds. In addition, this system will ensure high accountability and transparency, which are essential in managing funds from various sources, including the government, donors, and other grant providers.
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.
A Simple Modeling of MPPT-based ANN for Photovoltaic System Sholikhah, Evi Nafiatus; Aulia Rahma Annisa; Muhammad Rizani Rusli; Mentari Putri Jati
Journal of Computer, Electronic, and Telecommunication (COMPLETE) Vol. 6 No. 1 (2025): July
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v6i1.684

Abstract

This research describes a simple modeling technique for Maximum Power Point Tracking based on Artificial Neural Network (MPPT-based ANN) for photovoltaic (PV) systems. The proposed ANN model utilizes a feed-forward backpropagation architecture. The PV system was developed and tested in a simulation environment under uniform irradiation levels of 1000 W/m², 800 W/m², and 600 W/m², and rapidly varying irradiation changes. The simulation results demonstrate that the MPPT-based ANN accurately tracks the MPP, achieving stable power outputs of 98.36 W, 79 W, and 57.45 W, respectively. Although the system experiences initial transient oscillations during the tracking phase, it stabilizes within 80 milliseconds, showcasing rapid convergence and high steady-state accuracy. Under dynamic conditions, the MPPT-based ANN adapts effectively to fast-changing irradiation, restarting the algorithm to track and maintain the system at the updated MPP accurately. These results highlight the reliability, adaptability, and suitability of the MPPT-based ANN for real-time applications in dynamic environments. Nonetheless, further improvements to the ANN model are suggested to minimize transient oscillations and enhance overall performance.
Development of an LSTM-Based Power Monitoring and Prediction System for Campus Electrical Facilities Using ESP32 and PM2120 Sholikhah, Evi Nafiatus; Oktavia Rizqi Kurniawan; Dimas Pristovani Riananda; Mustika Kurnia Mayangsari; Rohmad Hadi Handayani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5030

Abstract

This study develops a data acquisition system for monitoring, detecting, and forecasting electrical energy consumption to support efficient energy management. Electrical parameters such as voltage, current, and power are measured using a PM2120 power meter via Modbus RTU RS485 and processed by an ESP32 microcontroller. The data are displayed in real-time through a Nextion Human-Machine Interface (HMI) and utilized as input for a Long Short-Term Memory (LSTM) model trained on historical consumption data. Safety features include LED indicators that activate when current reaches 80% of maximum capacity and a buzzer that signals threshold violations. Experimental results demonstrate high prediction accuracy, with RMSE values of 0.38 kW (5.32%) for phase R, 0.47 kW (7.55%) for phase S, and 0.28 kW (5.39%) for phase T. Transmission latency averages two to three seconds, while prediction computation is under 10 seconds. The system effectively reflects consumption trends, making it a reliable decision-support tool for enhancing energy efficiency in small- to medium-scale installations.
Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation Sholikhah, Evi Nafiatus; Windarko, Novie Ayub; Sumantri, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4559-4570

Abstract

A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.
Co-Authors Adhitya, Ryan Yudha Aisyi Maulidhia, Alief Nur Ardiana, Mirza Aulia Rahma Annisa Aulia Rahma Annisa Bambang Sumantri Brian, Thomas Dimas Pristovani Riananda Maulidhia, Alief Nur Aisyi Mentari Putri Jati Mustika Kurnia Mayangsari Novie Ayub Windarko Oktavia Rizqi Kurniawan Rahayu, Putri Nur Rohmad Hadi Handayani Rusli, Muhammad Rizani Wibowo, Sekarsari Yudi Andika