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All Journal SAMUDERA Infotech Journal ITEj (Information Technology Engineering Journals) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Teknologi Jurnal Sosial Humaniora Sigli Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology JUTECH : Journal Education and Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Jurnal Ilmu Komputer dan Informatika Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Information Technology (JINTECH) Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Solusi Masyarakat (JSM) INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Teewan Journal Solutions
Asrianda Asrianda
Teknik Informatika Universitas Malikussaleh
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Journal : itej information technology engineering journals

 1 
Classification of Family Hope Program Assistance Recipients Using the C4.5 Algorithm with Z-Score Normalization (Case Study in Atu Lintang District) Wahyuni, Siti; Asrianda, Asrianda; Retno, Sujacka
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.207

Abstract

One of the challenges in distributing social assistance is determining recipients who are truly eligible objectively and efficiently. This study develops a classification system for Family Hope Program (PKH) recipients by utilizing the C4.5 algorithm combined with Z-Score normalization to group citizen data into Eligible or Ineligible categories. The data used came from 551 residents of Atu Lintang District and included attributes such as house status, wall type, toilet facilities, occupation, and income. The research stages started from data preprocessing, attribute normalization, training the model, to evaluating its performance through metric such as accuracy, precision, recall, and F1-score. The evaluation results showed that the model achieved an accuracy of 94%, precision 0.96, recall 0.90, and F1-score 0.93 for the Eligible category. Based on the confusion matrix, the model was able to correctly classify 47 Eligible residents and 57 Ineligible residents. Analysis of the attributes showed that occupation was the most influential feature in the classification process. These results prove that the application of the C4.5 algorithm can be applied effectively to build a decision support system in the distribution of social assistance, and provide accurate and easy-to-understand results. This study also opens up opportunities for improving model performance by adding more data and testing with alternative algorithms going forward.
Forecasting of Palm Oil CPO Production Results at PTPN III Batang Toru Plantation Using The Autoregressive Integrated Moving Average Method Utari, Sylva Putri; Asrianda, Asrianda; Retno, Sujacka
ITEJ (Information Technology Engineering Journals) Vol 10 No 2 (2025): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i2.254

Abstract

The increasing demand for palm oil as a raw material for food and energy industries has driven the need for accurate forecasting methods to optimize palm oil production management. This study aims to forecast Crude Palm Oil (CPO) production at PTPN III Batang Toru Plantation using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly time series data from January 2020 to January 2024, including Fresh Fruit Bunches (FFB), loose fruit, and CPO yields, were analyzed to build the forecasting model. The Augmented Dickey-Fuller (ADF) test confirmed that the data is stationary without differencing. Based on the ACF, PACF, and white noise tests, the ARIMA(1,0,1) model was identified as the best fit. The forecasting results indicated a potential increase in CPO production from January 2025 to December 2026. However, alternative models like CPOF showed poor accuracy, with a high MAPE of 442.12%, suggesting the need for further model refinement. Despite limitations, the ARIMA method remains effective for short-term forecasting and supports data-driven decision-making in the plantation sector.
Co-Authors Aidilof, Hafizh Al Kautsar Aidilof, Hafizh Al-Kausar Aidilof, Hafizh Al-Kautsar Alvesaldy, Alvin Amalia, Iklasni Amalia, Wildi Ar Razi Ardi Wirya Indarto Armaya, Devira Yuda Asmaul Husna Azmi, Win Damayanti, Ita Delianda, Anggun Dewi, Tursina Diqti, Fadillah Fauziah Erwanda, Ade Putra Eva Darnila Fadlisyah Fajriana, Fajriana Faresya, Natasya Fyanda, Dwi Auji Gunarwan, Gunarwan Herman Fithra Herman Mawengkang Ibnu Khaldun Jange, Beno Jannah, Rauzatul K. M. Nasution , Mahyuddin Kamaruddin Hasan Kautsar, Al Khaidar, Al Kurniawati Lidya Rosnita Mardhatillah, Mardhatillah Maryana Maulana, OK Muhammad Majid Muhammad Azhari Muhammad Dhuha, Teuku Nabil Muhammad Fikry Muhammad Fikry Munadila, Aura Muqarrabin, Khalis Al Mutammimul Ula Nasrul ZA Nasrul ZA, Nasrul Panggabean, Pitra Rahmadani Poltak Sihombing Pratiwi, Dinda Putri, Sri Raihan Rachman, Aulia Rachmat Triandi Tjahjanto Rahayu, Lolia Lusiana Rahmat Hidayat Rahmat Hidayat Rahmat Triandi Rahmatillah, Siska Yuna Raihansyah, Khananda Rauzatul Jannah Razi, Ar Rini Meiyanti Risawandi, Risawandi Rozzi Kesuma Dinata Saputri, Rifa Andriani Sembiring, Vivi Dista Br Siti Hajar Siti Wahyuni Sujacka Retno Utari, Sylva Putri Wahdana, Aldi Wibowo, Patmono Yesy Afrillia ZA , Nasrul Zahratul Fitri, Zahratul Zulfadl, Zulfadl Zulfadli Zulfadli Zulfadli Zulfadli Zulfadli