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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) JURTEKSI International Journal of Basic and Applied Science Jurnal Sains Teknologi dan Sistem Informasi Prosiding Snastikom INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Journal of Computer Science and Research Jurnal Pemberdayaan Kepada Masyarakat
M. Safii
STIKOM Tunas Bangsa
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Medicine & Pharmacology Physics Public Health

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ANALYSIS OF THE BACKPROPAGATION ALGORITHM IN PREDICTING WATER VOLUME OF PDAM TIRTAULI PEMATANG SIANTAR CITY Saputra Ramadani; Anjar Wanto; M. Safii
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 2 (2024): Maret 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2893

Abstract

Abstract: Increasing living standards cause an increase in the need for drinking water. However, current water supply estimates are still not optimal, with water production sometimes being more or less than requirements. To estimate the amount of water, an appropriate method is needed. The method used in this research is the back propagation algorithm artificial neural network method. When developing forecasts, past data is necessary to produce accurate results. This research aims to develop a predictive model that can estimate the volume of water that will be used by PDAM Tirtauli in the future. It is hoped that this predictive model can help PDAMs in planning more efficient water supply management and can reduce the potential for water supply shortages in the future. This research uses water distribution data for the 2015-2022 period. Training data starts in 2015-2021, testing data starts in 2016-2022. In this research, results were obtained using the Matlab R2011a application. In this research, the 5 architectures used are architecture 6-53-1, 6-58-1, 6-61-1, 6-81-1, 6-87-1. Based on these five architectures, the best architecture was obtained, namely architecture 6-87-1 with a root mean square error test value of 0.00010031 and an accuracy of 92%. The results achieved in 2023 are the total water volume of PDAM Tirtauli Pematangsiantar of 189,610,426.                                                                                            Keywords: backpropagation; distribution; PDAM; prediction; water   Abstrak: Meningkatnya taraf hidup menyebabkan meningkatnya kebutuhan akan air minum. Namun, perkiraan pasokan air saat ini masih belum optimal, dengan produksi air kadang-kadang lebih atau kurang dari kebutuhan. Untuk memperkirakan jumlah air diperlukan suatu metode yang sesuai. Metode yang digunakan dalam penelitian ini adalah metode jaringan syaraf tiruan algoritma back propagation. Saat mengembangkan perkiraan, data masa lalu diperlukan untuk menghasilkan hasil yang akurat. Penelitian ini bertujuan untuk mengembangkan model prediktif yang dapat memperkirakan volume air yang akan digunakan oleh PDAM Tirtauli di masa mendatang. Model prediktif ini diharapkan dapat membantu PDAM dalam perencanaan pengelolaan pasokan air yang lebih efisien dan dapat mengurangi potensi kekurangan pasokan air pada masa yang akan datang. Penelitian ini menggunakan data sebaran air periode 2015-2022. Data pelatihan dimulai pada tahun 2015-2021, data pengujian dimulai pada tahun 2016-2022. Pada penelitian ini diperoleh hasil dengan menggunakan aplikasi Matlab R2011a. Pada penelitian ini 5 arsitektur yang digunakan adalah arsitektur 6-53-1, 6-58-1, 6-61-1, 6-81-1, 6-87-1. Berdasarkan kelima arsitektur tersebut diperoleh arsitektur terbaik yaitu arsitektur 6-87-1 dengan nilai uji root mean square error sebesar 0,00010031 dan mendapatkan akurasi sebesar 92%. Hasil yang dicapai pada tahun 2023 adalah total volume air PDAM Tirtauli Pematangsiantar sebesar 189.610.426. Kata Kunci: air; backpropagation; distribusi; PDAM; prediksi 
Pelatihan Membuat Grafik Interaktif Dan Presentase Data Menarik Dengan Microsoft Excel Di Smk Tritech Indonesia Elida Tuti Siregar; Ommi Alfina; Dedek Ardi Saputra; Frans Ikorasaki; M. Safii
Battuta-Jurnal Pemberdayaan Masyarakat Vol 2 No 1 (2025): Edisi Januari
Publisher : LPPM Universitas Battuta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Grafik merupakan tampilan visual untuk penyajian data memudahkan dalam pengambilan keputusan menggunakan microsoft excel representasi data yang informatif dan relevan.pelatihan membuat grafik interaktif ini bertujuan membekali siswa  menginterpretasi penyajian  dan presentase data yang menarik dan mudah difahami.siswa smk tritech indonesia dapat meningkatkan keterampilan analitis dan kemampuan berpikir kritis siswa dalam pengolaan data , serta siswa siswi mereka untuk menghadapi tantangan dunia kerja dengan keterampilan yang praktis dan aplikatif. , kemampuan menganalisis dan memvisualisasikan data menjadi keterampilan yang sangat penting, terutama di dunia pendidikan dan industry Salah satu fungsi yang sering digunakan dalam analisis data adalah COUNTIF, yang memungkinkan pengguna untuk menghitung jumlah data berdasarkan kriteria tertentu. Namun, banyak siswa di SMK Swasta Tritech Indonesia yang masih kurang memahami penggunaan Excel dalam analisis data dan pembuatan grafik interaktif. Oleh karena itu, pelatihan ini dirancang untuk meningkatkan kemampuan siswa dalam menggunakan COUNTIF dan grafik interaktif di Excel.
Hybrid Feature Selection dan Ensemble Learning untuk Klasifikasi Risiko Stunting Anak di Indonesia Ommi Alfina; Nita Syahputri; M. Safii; Muhammad Taufiq Rustam
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp107-111

Abstract

Stunting is a chronic nutritional problem that remains a major public health issue in Indonesia. This study aims to develop a classification model for stunting risk in children using a combination of hybrid feature selection and ensemble learning methods. The dataset used is derived from socio-economic and health data obtained from the Central Statistics Agency and open datasets. The research method includes data preprocessing, feature selection, model development using Random Forest and Gradient Boosting combined with a Voting Classifier, and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the proposed model achieves high performance with accuracy reaching 98% and ROC-AUC close to 1. The hybrid feature selection successfully improves model efficiency by selecting relevant features. This study demonstrates that the integration of feature selection and ensemble learning can produce an accurate and interpretable model for early detection of stunting risk.
Implementasi Smart Attendance System Berbasis RFID dan Face Recognition untuk Meningkatkan Efisiensi Presensi Siswa Pada SMK PAB 8 Sampali Medan Ommi Alfina; Nita Syahputri; Habib Nurlutman Hasibuan; M. Safii; Muhammad Taufiq Rustam
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp42-49

Abstract

The development of digital transformation in the field of education encourages schools to adopt technology that can improve the effectiveness of academic administration, particularly in the student attendance system. SMK PAB 8 Sampali Medan still faces several problems in the conventional attendance process such as delays in recording, potential manipulation of attendance, and suboptimal monitoring of student attendance data. This community service activity aims to implement a Smart Attendance System based on RFID and Face Recognition to improve the efficiency, accuracy, and security of the student attendance process. The implementation of the activity is carried out through stages of observing the school's needs, system design, installation of RFID devices and face recognition cameras, training teachers and school operators in using the system, and assistance in implementation. The results of the activity show that the implemented system is capable of accelerating the student attendance process in real-time, minimizing recording errors, improving student attendance discipline, and facilitating the school in monitoring and digitally summarizing attendance data. In addition, this activity also improves technology literacy for teachers and administrative staff in supporting the digital transformation of education within the school environment.
Penerapan Internet of Things (IoT) Dasar dalam Sistem Monitoring Lingkungan Sekolah Serta Smart Classroom di SMK PAB 8 Sampali Medan Ommi Alfina; Nita Syahputri; Ananda Pratama; Muhammad Taufiq Rustam; M. Safii; Jamaluddin Jamaluddin
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 2 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol5No2.pp259-265

Abstract

The development of Internet of Things (IoT) technology has opened up significant opportunities for improving the efficiency and effectiveness of learning systems as well as school environmental management. This community service activity aims to provide basic IoT training and implement an environmental monitoring system and smart classroom at SMK PAB 8 Sampali Medan. The implementation method includes three main stages: (1) socialization of basic IoT concepts and their application in the educational field, (2) practical training for creating simple IoT devices based on temperature, humidity, and light sensors, and (3) implementation of a school environmental monitoring system integrated with a web-based platform and a real-time dashboard. In addition, participants were also introduced to the concept of a smart classroom, where classroom conditions can be monitored and controlled automatically through the developed IoT system. The results of the activity showed an 85% increase in participants' understanding of basic IoT concepts and their ability to independently design environmental monitoring system prototypes. This program has had a positive impact on enhancing digital literacy, technical skills, and school readiness for IoT-based educational digital transformation.
Klasifikasi Pola Konsumsi Energi Rumah Tangga Menggunakan Algoritma Machine Learning untuk Mendukung Implementasi Smart City Ommi Alfina; M. Safii
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp300-306

Abstract

Population growth in urban areas drives a significant increase in household energy consumption. This condition poses a major challenge for the implementation of the smart city concept, particularly in achieving energy efficiency and sustainability. This study aims to classify household energy consumption patterns based on household power consumption data to support intelligent decision-making in urban energy management. The research method includes data preprocessing, data cleaning, and aggregation of daily energy consumption by utilizing key attributes such as Global Active Power, Voltage, Global Intensity, and three sub-metering variables. Consumption pattern categories are formed using the tertile method into three classes: Low, Medium, and High. Several machine learning algorithms are applied to build the classification model, including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting. The test results show that the Random Forest model with hyperparameter adjustments produces the best performance with an accuracy value of 0.98 and an F1-macro value of 0.98, surpassing other models. These findings indicate that the ensemble learning approach is able to capture the complexity of household energy consumption patterns more effectively than conventional linear models. The contribution of this research lies in the development of a machine learning-based predictive model to support adaptive energy consumption monitoring and control systems in smart city implementations.
Prediksi Jumlah Produksi Kelapa Sawit di Indonesia Menggunakan Algoritma Backpropagation M. Safii; Ommi Alfina
Majalah Ilmiah METHODA Vol. 14 No. 2 (2024): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol14No2.pp166-174

Abstract

Indonesia is a country that has advantages in the agricultural sector which has the largest plantation and agricultural areas in ASEAN, one of which is oil palm plantations. Indonesia is one of the largest crude palm oil (CPO) business players in the world. More and more palm oil mills and oil palm land are being converted to oil palm cultivation, because oil palm plantations are more beneficial for farmers and palm oil processors. Palm oil plantations are still trying in several ways to maintain stable market demand, one of which is by increasing palm oil production, because palm oil is the main source of other product derivatives. Palm oil production fluctuates every month, but the ups and downs are caused by many factors, namely climate, rainfall, soil fertility, selling prices, and others. Reduced production has a direct impact on the income of farmers and workers in the sector, which in turn can cause economic instability. Actions are needed to ensure the continuity of this industry, one of which is by making predictions. One prediction technique is the Backpropagation artificial neural network. The prediction model can provide very accurate estimates of palm oil production at the provincial level. By analyzing historical data, this research can identify patterns that can help predict future palm oil production. The urgency lies in the strategic role of palm oil in the Indonesian economy.
Co-Authors Abdi Rahim Damanik Afifah Wulandari Akbar Fahri Hambali Ananda Pratama Aron Saputra Sirait Bahrudi Efendi Damanik Dedek Ardi Saputra Dedi Suhendro Dessy Dwi Angraini elfrida lucyana hutahaean Elida Tuti Siregar Fitri Anggraini Frans Ikorasaki Habib Nurlutman Hasibuan Indra Gunawan Irawan Irawan Jamaluddin Lena Sapura Muhammad Rafai Muhammad Taufiq Rustam Murniati Rambe Nita Syahputri Nita Syahputri Ommi Alfina Ommi Alfina Ommi Alfina Saputra Ramadani Sundari Retno Andani Susi Septi Hardiani Syahrial Azmi Pohan Tri Febri Damayanti Wanto, Anjar