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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Komputasi dan Teknologi Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH TESLA: Jurnal Teknik Elektro Jurnal Informasi dan Teknologi Jurnal Teknik Elektro dan Komputasi (ELKOM) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Energi Elektrik Jurnal Sistim Informasi dan Teknologi Internet of Things and Artificial Intelligence Journal Journal Geuthee of Engineering and Energy Jurnal Malikussaleh Mengabdi Jurnal Pengabdian Masyarakat Ilmu Komputer
Fidyatun Nisa
Universitas Malikussaleh
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Journal : Journal Geuthee of Engineering and Energy

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Decision Support System for Providing Business Capital In Pidie District Using the Web-Based TOPSIS Method Nurfebruary, Nanda Sitti; Nisa, Fidyatun; Fuadi, Fuadi; Multazam, Teuku; Nasution, Fakhruddin Ahmad
Journal Geuthee of Engineering and Energy Vol 2, No 1 (2023): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v2i1.21

Abstract

Providing business capital assistance is a poverty alleviation program for people who have medium and small businesses. The Office of Industry and Trade (DISPERINDAGKOP) of Pidie Regency has one of the work programs, namely providing material and equipment assistance to small and medium industries in Pidie Regency. In order to be more efficient and effective in the process of determining whether or not someone is eligible to receive venture capital assistance, a Decision Support System (SPK) was created for the eligibility of recipients of venture capital assistance in Pidie District using the Technique For Others Preference by Similarity to Ideal Solution (TOPSIS) method. The criteria used as an assessment in determining the eligibility of recipients of business capital assistance are underprivileged conditions, economic conditions, conditions of family dependents, housing conditions, condition of type of business. These criteria are obtained based on the table of the amount of venture capital assistance that has been determined by the Pidie District Government. This system is expected to assist DISPERINDAGKOP in providing business capital assistance to the community according to the resulting criteria. Also to overcome the possibility of data duplication and errors in determining the recipient of venture capital.
Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) Gunandra Siregar, Putri; Sahputra, Ilham; Nisa, Fidyatun
Journal Geuthee of Engineering and Energy Vol 4, No 1 (2025): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v4i1.56

Abstract

Forecasting electricity consumption is one of the solutions that can be implemented by the ULP Meulaboh Kota to ensure the availability of sufficient electricity supply. With the continuous increase in electricity demand, the ULP faces challenges in predicting and managing electricity consumption. Uncertainty in consumption patterns can lead to imbalances between supply and demand, potentially causing various issues such as power outages, high operational costs, and customer dissatisfaction. Therefore, accurate forecasting is essential to support effective decision-making and planning. This study aims to forecast electricity consumption across five different sectors: residential, social, business, industrial, and public, using the ARIMA (Autoregressive Integrated Moving Average) method. The forecasting process involves data collection, stationarity testing using the Augmented Dickey-Fuller (ADF) test, and differencing when necessary to achieve stationarity. The ARIMA model is identified through ACF and PACF plot analysis, estimated, and tested before being used for forecasting. The results indicate that the ARIMA method provides highly accurate forecasts for all sectors, as reflected by the low Mean Absolute Percentage Error (MAPE) values. The residential sector has a MAPE of 4.3957%, the social sector 4.3757%, the business sector 3.1125%, the industrial sector 7.9937%, and the public sector 4.3646%. Overall, the forecasting error produced by the ARIMA model remains below 8%, with an average MAPE of 4.8483% across all sectors.
Co-Authors Achriadi Sukiman, T Sukma Afrizal, Ari Akhyar, Daniel Alfatir, Faiq Angga Pratama Arnawan Hasibuan Asran Asran Asri Asri Aulia, Muhammad Haiqal Azizi, Jamali Badriana, Badriana Cut Agusniar Dahlan Abdullah Darma, Ade Dedi Fariadi Dian Putri, Yohana Fakhruddin Ahmad Nasution Fatwa, Islami Fauzi, Sri Wahyuni Fuadi Fuadi Gunandra Siregar, Putri Habib Muharry Yusdartono Kahfi, Nurul Ashabul Karima, Annisa Lidya Rosnita Muhammad Azhari Muhammad Ikhwani Munirul Ula Mutammimul Ula Nanda Sitti Nurfebruary Nasution, Abdon Nawari Nur Elisyah Nurfebruary, Nanda Sitti Nurul, Nurul Fazrika Rachman, Aulia Rahayu, Nanda Amelia Rahman, Ashri Nurhajizah Rosdiana Rosdiana Sahputra, Ilham Salahuddin Salahuddin Saptari, Mochamad Ari Siti Maimunah Suci Ramadona Syukriah Syukriah, Syukriah Taufiq Teuku Multazam Yurni, Irma