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All Journal Faktor Exacta International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Endin Fahrudin
UNIVERSITAS PAMULANG
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Forecasting Accuracy Analysis of Catering Raw Material Stock Using Simple Exponential Smoothing Based on Mean Absolute Percentage Error (MAPE) Dimas Eko Prasetyo; Endin Fahrudin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7039

Abstract

In the catering industry, inaccurate inventory management often leads to significant food waste or stockouts due to highly volatile raw material demand, and conventional intuition-based procurement methods are no longer sufficient to maintain operational efficiency. This research applies to the Simple Exponential Smoothing (SES) algorithm to forecast raw material requirements and evaluates its accuracy using the Mean Absolute Percentage Error (MAPE) metric. Twelve months of historical transaction data from a local catering business were analyzed, categorized into basic commodities, proteins, and vegetables, with the SES model calibrated by testing smoothing constants ( ) across the range of 0.1 to 0.9. The findings indicate that stable items such as rice achieve the highest accuracy at a low of 0.2, yielding a MAPE of 4.25% — classified as Very Good. Highly volatile items such as proteins and fresh vegetables require a high of 0.8–0.9 to remain responsive, producing MAPE values between 12.40% and 18.15%, classified as Good. These results confirm that SES offers a defensible, data-grounded decision-making structure that measurably reduces forecasting errors and improves procurement cost management in the catering sector.
Pengenalan dan Penerapan AI untuk Meningkatkan Inovasi Pembelajaran di Pendidikan Anak Usia Dini Endin Fahrudin; Ghema Nusa Persada; Petrus Sianggian Purba
Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Vol 2 No 3 (2026): Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Taman Kanak-Kanak (TK) Baitul Karim menghadapi tantangan nyata terkait keterbatasan literasi digital tenaga pendidik dan tingginya ketergantungan pada media instruksional konvensional yang membutuhkan waktu persiapan cukup lama. Kegiatan pengabdian kepada masyarakat (PKM) ini bertujuan untuk membekali para pendidik di TK Baitul Karim dengan pemahaman komprehensif serta keterampilan praktis dalam memanfaatkan teknologi Artificial Intelligence (AI) guna meningkatkan inovasi pembelajaran pada tingkat Pendidikan Anak Usia Dini (PAUD). Metode pelaksanaan program menggunakan kerangka kerja Hands-on Workshop intensif dengan pendekatan Problem-Based Learning dan Collaborative Learning yang diikuti oleh 15 orang peserta yang terdiri dari 12 guru kelas dan 3 staf administrasi. Hasil kegiatan menunjukkan adanya peningkatan kapasitas literasi digital guru yang diukur melalui pre-test dan post-test dengan kenaikan skor rata-rata mencapai 40%. Luaran nyata dari kegiatan ini berupa portofolio media pembelajaran digital seperti e-book cerita bergambar dan kartu karakter edukatif berbasis AI yang tersimpan dalam bank data sekolah. Faktor pendukung utama keberhasilan program ini adalah komitmen penuh pihak yayasan dan antusiasme tinggi dari para peserta selama pelatihan berlangsung
Geographc Artificial Intelligence GeoAI dan Natural Language Processing dalam Analisis Data Spatial Endin Fahrudin; Samso Supriyatna; Firman Darmawan
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.28518

Abstract

Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.
Pemanfaatan Kecerdasan Artifisial untuk Menciptakan Perangkat Pembelajaran yang Menarik dan Efektif di Taman Kanak-Kanak Ghema Nusa Persada; Salman Farizy; Endin Fahrudin
Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Vol 1 No 2 (2025): Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Education in early childhood is a crucial foundation for character building and creativity. However, the learning process at TK Baitul Karim remains conventional, dominated by lecture methods and manual worksheets, leading to low student engagement. The identified problems include teachers' low digital competence and a lack of interactive media. This community service aims to implement Artificial Intelligence (AI) technology to help teachers develop digital, adaptive, and fun learning tools. The methods used include situational analysis, training on AI platforms (ChatGPT, DALL·E, Canva AI), intensive mentoring, and pilot implementation in class. The results show a significant increase in teachers' digital literacy, with a 60% improvement in post-test scores. Furthermore, 100% of participants successfully created AI-based media such as automated picture books and educational videos. Implementation in the classroom resulted in increased student focus and interaction. This program demonstrates that AI can be a practical solution for creating innovative learning media in early childhood education.
Co-Authors Cendra Harmon Dimas Eko Prasetyo Firman Darmawan Nurhidayatuloh Persada, Ghema Nusa Petrus Sianggian Purba Salman Farizy Samso Supriyatna Sopiyan Apandi Subariah, Risah