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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Format : Jurnal Imiah Teknik Informatika bit-Tech Jurnal Kajian Ilmiah Nusantara Hasana Journal Gema Lingkungan Kesehatan Journal Information & Computer (JICOM) Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian kepada Masyarakat The Scientific Journal of Information Systems Fortech : Journal of Information Technology
Surahmat, Asep
Universitas Utpadaka Swastika
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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APPLICATION OF DATA MINING TECHNIQUES TO ANALYZE ATTENDANCE AND IMPROVE THE QUALITY OF CHINESE LEARNING Grace Limiko; Pupista, Orinda; Surahmat, Asep; Umbu Zogara, Lukas
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.176

Abstract

In the era of globalization, learning Chinese is increasingly important, but challenges such as low student attendance and learning quality are still significant problems. This article discusses the application of data mining techniques as a solution to analyze student attendance and improve the quality of Chinese learning. By collecting and analyzing attendance data from 200 students for one semester, through classification and visualization methods, this article identifies patterns that affect student attendance. The analysis results show that 65% of students who followed the interactive teaching method attended more than 80% of the total meetings, compared to only 40% of students who followed the traditional teaching method. In addition, it was found that 75% of students who received additional material for difficult topics experienced a 20% increase in average test scores compared to pre-intervention scores. Recommendations for improvement were made based on these findings, including adaptation of teaching methods and provision of supplementary materials. Through a case study of an educational institution that has successfully implemented this technique, this article shows that data mining can not only improve student attendance, but also significantly improve the quality of learning. This research is expected to encourage educational institutions to adopt data mining technology in an effort to improve students' learning experience.
Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru Di Twitter Darmawan, Rizqi; Indra; Surahmat, Asep
Jurnal Kajian Ilmiah Vol. 22 No. 2 (2022): May 2022
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/g0dv0y21

Abstract

The significant increase in the number of users has caused public opinion on the Ruang Guru application to be widely spread through social media, especially Twitter. From 15,000 twitter data taken with the keyword Ruang Guru, a total of 2,358 datasets were obtained through the process of handling duplicates. In this study, sentiment analysis was carried out using the Support Vector Machine (SVM) algorithm which was optimized with Particle Swarm Optimization (PSO) then tested using the 10-Fold Cross Validation method which resulted in the highest accuracy rate of 89.20%, while the Support Vector Machine algorithm (SVM) only produces the highest accuracy rate of 88.56%. There is an increase of 0.64% with Particle Swarm Optimization optimization. Sentiment analysis results are positive, with positive results as much as 1463 data or 62.04% and 895 or 37.96% negative sentiment. From the results of this study, it is expected to be a material consideration for Ruang Guru to improve the quality of the service sector found on social media, especially Twitter.
AI-Based Waste Detection for Water Quality Monitoring in the Cisadane River: A Deep Learning Approach Surahmat, Asep; Yato, Dhimas Buing Rindi Widra
Gema Lingkungan Kesehatan Vol. 23 No. 3 (2025): Gema Lingkungan Kesehatan
Publisher : Poltekkes Kemenkes Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36568/gelinkes.v23i3.270

Abstract

The rapid accumulation of waste in Indonesia's rivers, particularly the Cisadane River, seriously threatens water quality, ecosystem health, and public well-being. Traditional waste monitoring methods are inefficient and often fail to deliver timely data for effective interventions. This study addresses this gap by proposing an AI-based waste detection system for real-time water quality monitoring using deep learning techniques. A hybrid model integrating Convolutional Neural Network (CNN) and You Only Look Once version 7 (YOLO v7) was developed and tested on a dataset of 10,000 annotated images—60% organic and 40% inorganic waste—collected from the Cisadane River. The CNN model achieved a classification accuracy of 87%, a precision of 84%, a recall of 86%, and an F1-score of 85%. The YOLO v7 model demonstrated % detection accuracy of 82% with a processing speed of 20 frames per second. While mean Average Precision (mAP) was not directly calculated, the model's performance across key metrics supports its real-time applicability. This research offers a scalable and cost-effective approach for river waste monitoring and highlights the potential of AI in supporting sustainable environmental management in Indonesia.
PERANCANGAN FRAMEWORK GREEN IT UNTUK MENGURANGI DAMPAK NEGATIF DALAM ORGANISASI: Green IT, E-Waste, Energy Management, Organizations Muttaqi, Fajar; Alfaujianto, Moh.; Surahmat, Asep
FORTECH (Journal of Information Technology) Vol 9 No 1 (2025): Fortech (Journal Of Information Technology)
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/fortech.v9i1.1561

Abstract

The adoption of technological advancements has strengthened the role of information technology (IT) in organizational operations. While IT provides various benefits, it also creates environmental challenges, such as high energy consumption and increasing electronic waste (e-waste). The International Energy Agency (IEA) reports that data centers and IT networks consume 1% of global energy, yet only 17.4% of e-waste is recycled. Therefore, a sustainable IT management approach is crucial. Green IT integrates sustainability principles into IT management to improve energy efficiency, waste reduction, and eco-friendly technology adoption. However, challenges such as unclear guidelines, infrastructure limitations, and low organizational awareness hinder its implementation. This study aims to design a Green IT framework suited to Indonesia’s context, using literature reviews, stakeholder interviews, and case studies. The findings will provide organizations with structured guidance to reduce environmental impact, improve efficiency, and enhance competitiveness in the digital era
ANALISIS CLUSTERING PERILAKU UNTUK MENENTUKAN PREFERENSI MEREK PRODUK IT PELANGGAN DI PT. XYZ Darmawan, Rizqi; Salma, Triana Dewi; Surahmat, Asep
Nusantara Hasana Journal Vol. 5 No. 2 (2025): Nusantara Hasana Journal, July 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i2.1653

Abstract

This study discusses customer segmentation strategies based on purchasing behavior and brand preferences for information technology products at PT. XYZ. The main objective of this research is to identify customer purchasing patterns and classify them into several segments with different characteristics. The historical transaction data used includes attributes such as the brand of purchased products, purchase frequency within one year, and total transaction value. After the data cleaning and normalization process, a centroid-based clustering technique was applied to identify homogeneous groups in the database. The clustering results show three main clusters, each representing different consumer behaviors in terms of brand loyalty, price sensitivity, and spending level. The analysis indicates that customers with high transaction values tend to select specific brands and make purchases more frequently. These findings provide strategic insights for the company in designing more personalized marketing approaches, improving the effectiveness of product offerings, and strengthening relationships with customers in each segment.
Model Prediksi Pembelian Properti: Menggunakan Analisis Data untuk Memahami Perilaku Konsumen Umbu Zogara, Lukas; Hasyanta, Gopas; Moh. Alfaujianto; Asep Surahmat
Journal Information & Computer Vol. 3 No. 2 (2025): Journal Information & Computer
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jicomisc.v3i2.48799

Abstract

Penelitian ini bertujuan membangun model prediksi pembelian properti dengan pendekatan analisis data dan metode klasifikasi. Data dikumpulkan dari 125 responden yang mencakup informasi demografis, preferensi pembelian, serta faktor-faktor yang memengaruhi keputusan. Tiga algoritma klasifikasi digunakan: Decision Tree, Random Forest, dan Support Vector Machine (SVM). Hasil menunjukkan bahwa model Random Forest memberikan akurasi tertinggi sebesar 87%, diikuti oleh SVM (83%) dan Decision Tree (80%). Temuan ini mengidentifikasi lokasi, harga, dan reputasi pengembang sebagai tiga faktor utama dalam pengambilan keputusan pembelian. Hasil penelitian ini memberikan wawasan penting bagi pengembang dan agen properti untuk menyusun strategi pemasaran yang lebih tepat sasaran dan sesuai kebutuhan pasar. Penelitian ini juga menekankan potensi besar penerapan machine learning dalam memahami perilaku konsumen di industri properti..
Sistem Otomatis Ringkasan Laporan Keuangan Berbasis PDF Menggunakan Metode NLP Transformer Nugraha, Fahmi; Surahmat, Asep
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.009

Abstract

Kompleksitas dan volume laporan keuangan perusahaan yang terus meningkat menjadi tantangan bagi analis dan pemangku kepentingan dalam menginterpretasikan informasi secara cepat dan akurat. Analisis manual cenderung memakan waktu lama dan rentan terhadap kesalahan. Penelitian ini mengusulkan sistem otomatis untuk melakukan peringkasan laporan keuangan berbasis PDF dengan menggunakan metode Natural Language Processing (NLP) berbasis Transformer. Sistem dikembangkan menggunakan Python serta memanfaatkan PyPDF2/pdfplumber untuk ekstraksi teks, NLTK untuk prapemrosesan, dan model BART/T5 dari Hugging Face Transformers untuk menghasilkan ringkasan. Evaluasi dilakukan pada laporan tahunan perusahaan multinasional dengan panjang 50–200 halaman. Hasil pengujian menunjukkan sistem mampu mereduksi teks hingga 10–15% dari panjang asli, dengan nilai rata-rata ROUGE-1 = 0,72; ROUGE-2 = 0,62; dan ROUGE-L = 0,70. Ringkasan yang dihasilkan mempertahankan informasi penting seperti tren pendapatan, laba bersih, beban operasional, dan arus kas. Pendekatan ini dapat mempercepat analisis keuangan, mengurangi beban kognitif analis, serta menghasilkan ringkasan yang konsisten. Ke depan, penelitian dapat dikembangkan dengan fine-tuning model pada korpus keuangan serta integrasi analisis sentimen untuk memperkaya interpretasi manajerial.
The Impact of Knowledge Management Systems in Enhancing the Competitiveness of Retail Companies Muttaqi, Fajar; Zogara, Lukas Umbu; Alfaujianto, Moh.; Surahmat, Asep
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.227

Abstract

This study investigates the role of Knowledge Management System (KMS) implementation inenhancing the competitiveness of retail companies, with a specific focus on Lotte Mart Indonesia.Using a qualitative exploratory case study approach, the research collected data through in-depthinterviews, field observations, and company document analysis. The findings demonstrate that KMSaccelerates the flow of information, reduces duplication, and improves operational efficiency, therebyenabling better coordination among departments. Furthermore, KMS facilitates knowledge sharingand collaboration, which supports the development of service innovations and responsive marketingstrategies. Employees reported that the system allows faster access to documents, real-time inventorychecking, and more structured workflows. Beyond operational benefits, KMS contributes tostrengthening customer satisfaction through improved responsiveness and accurate informationdelivery. Additionally, KMS supports the company’s digital transformation by integrating internalsystems such as ERP, CRM, and e-commerce platforms. Overall, KMS functions not only as aknowledge repository but as a strategic enabler of sustainable competitive advantage in the retailsector.
Implementation and Analysis of Multiple Interface Policies through System Feature Visibility on Fortigate FG-60F Alfaujianto, Moh; Muttaqi, Fajar; Surahmat, Asep; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.229

Abstract

Fortigate FG-60F is one of the popular firewall appliances utilized by small and medium-scalenetworks in managing security. However, some of the needed features such as multiple interfacepolicies are not displayed by default on the user interface. This study explores the functionality andeffectiveness of enabling system-feature visibility for easier management of inter-interface policies.Employing an experimental approach, the Fortigate FG-60F device was configured to activate thehidden feature, and subsequently, a set of policy rule scenarios with multiple interfaces wereestablished and tested. The results indicate that supporting system-feature visibility enhancessignificantly the administrator's ability to implement more specific traffic policies that arecommensurate with network topology requirements. Moreover, performance analysis showed nonegative impact on device performance after the implementation of multi-interface policy. Thefindings are expected to serve as a valuable reference for network administrators in optimizingFortigate FG-60F security capabilities by leveraging advanced, previously hidden features
Co-Authors Alfaujianto, Moh Alfaujianto, Moh. Darmawan, Rizqi Dhimas Buing Rindi Widra Yato Didi Kurnaedi Erna Oktora Fahmi Nugraha, Fahmi Fajar Muttaqi Fajar Muttaqi Grace Limiko Hasyanta, Gopas Indra Indra Indra Lukas Umbu Zogara Manorang Sihotang Moh Alfaujianto Moh. Alfaujianto Muttaqi, Fajar Nurul Badriah Pupista, Orinda Salma, Triana Dewi Sutrisno, Mirza Yato, Dhimas Buing Rindi Widra