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All Journal JPNM : Jurnal Pustaka Nusantara Multidisiplin Indonesian Journal of Education (INJOE) Jurnal Penelitian Teknologi Informasi dan Sains
Asep Saeppani
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Analisis Peminjaman Ruangan di Gedung Creative Center Sumedang Menggunakan Metode PIECES Dipa Arya Pangestu; Asep Saeppani
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 3 No. 1 (2025): JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v3i1.2998

Abstract

The utilization of information technology can provide benefits that are faster, more accurate, and more transparent. One application is in a room loan system that can avoid schedule clashes, increase user productivity, and make the administration process faster and more efficient. This research was conducted at the Sumedang Regency Tourism, Culture, Youth and Sports Office in the creative economy sector, which manages room loans at the Creative Center Sumedang Building. The current room loan system at the Creative Center Sumedang Building uses Google Form which experiences various obstacles, such as conflicting schedules and inefficient administrative processes. This study aims to evaluate and provide recommendations for a website-based system that is more structured and user-friendly to support room loan management. This research uses the PIECES (Performance, Information, Economy, Control, Efficiency, and Service) analysis method to evaluate the weaknesses of the current system and provide recommendations for website-based improvements with UI/UX design integration. Data was collected through observations and interviews with managers and users of room lending services. The results of the analysis show that a website-based system can improve the efficiency of room loan management, ensure a more organized schedule, and facilitate automatic data management. The proposed solution includes automatic room checking and a simpler interface design. With the implementation of an integrated and user-friendly system, room loan management is expected to become more effective and support optimal service.
OPTIMIZE TEXTILE BOOK RECOMMENDATION SYSTEM USING DEEP LEARNING ALGORITHMS Sitti Nur Alam; Asep Saeppani; Iwan Setiawan
Indonesian Journal of Education (INJOE) Vol. 4 No. 1 (2024): APRIL
Publisher : CV. ADIBA AISHA AMIRA

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

Abstract

The research aims to optimize the recommendation system for textile books by applying deep learning algorithms. The textile industry, rich in content and material variation, requires a system of recommendations that can accurately accommodate the diverse needs of its users. Deep learning, with its sophistication in processing large and complex data, offers solutions in improving the quality of recommendations. The study explores the use of deep learning models in interpreting user preferences and book characteristics, with the hope of producing more relevant and personal predictions. Research methods that literature conducts systematically through the collection of data from scientific sources such as journals, conferences, and related articles published in the last decade. The results show that deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) have been successfully applied in improving the accuracy of book recommendation systems, including in textile contexts. These models are able to understand and process textile information and user preferences more deeply than traditional algorithms. The research also revealed important factors that influence model performance, such as data quantity and quality, model architecture, and parameter setting. Although there are limitations associated with resource use and the need for large datasets, the use of deep learning algorithms in recommendation systems for textile books shows significant potential in improving personalization and user satisfaction.
Analisis Sentimen Publik Program Makan Bergizi Gratis Menggunakan Support Vector Machine M.Arif Firmansyah; Asep Saeppani; Irfan Fadil
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 3 No. 4 (2025): December : Jurnal Pustaka Nusantara Multidisiplin (ACCEPTED)
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v3i4.805

Abstract

Ketersediaan gizi merupakan faktor utama yang menunjang pertumbuhan anak dan kualitas sumber daya manusia. Pemerintah Indonesia memperkenalkan Program Makan Bergizi Gratis (MBG) sebagai langkah strategis untuk mengatasi masalah gizi buruk dan stunting pada anak sekolah. Penelitian ini berfokus pada analisis sentimen masyarakat terhadap program MBG dengan memanfaatkan algoritma Support Vector Machine (SVM) berbasis pengolahan Bahasa Alami (NLP). Data berasal dari 1.574 komentar di media sosial X (Twitter) yang diproses melalui tahapan pembersihan data, tokenisasi, penghapusan stopword, stemming, dan pembobotan TF-IDF. Hasil pengujian menunjukkan bahwa model SVM meraih akurasi 78%, sedangkan Logistic Regression mencapai 82%, dengan sebagian besar sentimen publik cenderung positif. Temuan ini mengindikasikan bahwa masyarakat menerima MBG dengan baik. Penelitian ini turut memperlihatkan manfaat penerapan machine learning untuk menganalisis kebijakan publik dan dapat digunakan pemerintah sebagai acuan menyusun strategi komunikasi dan meningkatkan efektivitas program gizi nasional
Co-Authors Dipa Arya Pangestu Irfan Fadil Iwan Setiawan M.Arif Firmansyah Sitti Nur Alam