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Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : -     EISSN : 25973584     DOI : -
Core Subject : Science,
Computer Science & IT
Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) merupakan ajang pertemuan ilmiah, sarana diskusi dan publikasi hasil penelitian maupun penerapan teknologi terkini dari para praktisi, peneliti, akademisi dan umum di bidang sistem informasi dan teknologi dalam artian luas.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 471 Documents
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Analisis Sentimen dan Topik Komentar YouTube tentang MBKM di Indonesia: Inferensi BERTopic Debby Amanda Rosaline Pannuw; Muhammad Amirul Haq
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study analyzes sentiment and conversational themes in 45 comments from 7 YouTube videos on Indonesia’s MBKM program. Sentiment classification is performed using an Indonesian pretrained model. Topic modeling is conducted with BERTopic, which leverages Transformer embeddings (paraphrase-multilingual-MiniLM-L12-v2), UMAP for dimensionality reduction, HDBSCAN for clustering, and c-TF-IDF for keyword extraction. The results indicate that most comments are positive (?93.3%), with relatively small neutral and negative portions. The very short and often formulaic nature of the comments limits lexical diversity, making topic differentiation challenging. The study’s contributions include a reproducible analytic protocol, c-TF-IDF–based topic reports, and methodological notes for modeling topics in very short texts. Future work should expand the corpus across channels and time periods and incorporate manual validation
Perancangan Rating Tool untuk Evaluasi Green IT di Perguruan Tinggi Cecep Muhamad Sidik Ramdani; I Made Oka Widyantara
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Sustainability issues in information technology are gaining increasing attention as energy consumption, carbon emissions, and environmental impacts from the use of software and digital infrastructure increase. This research aims to develop a Green IT evaluation framework based on a rating tool that is integrated with the principles of the Green Software Foundation (GSF) and COBIT 2019 as an IT governance framework. This approach combines the basic principles of sustainable software evaluation, such as carbon efficiency, energy efficiency, hardware efficiency, networking efficiency, and holistic lifecycle evaluation, with relevant COBIT 2019 objectives, such as resource optimization, managing enterprise architecture, managing quality, and monitoring performance. The results of this integration form an Environmentally Sustainable Computing (ESC) framework that covers four main dimensions: design, deployment, monitoring & refactoring, and governance & policy. Each dimension has assessment indicators with a maturity level scale of 0–5, so it can be used as an evaluation tool for the level of IT sustainability in higher education. This research is expected to be able to provide conceptual and practical contributions in the development of a Green IT rating tool that is applicable in the academic environment.
Arsitektur Ensemble Convolutional Neural Network untuk Klasifikasi Multi Kelas Penyakit Daun Kopi Ade Irma Purnamasari; Dadang Sudrajat; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Coffee leaf disease remains one of the most significant threats to global coffee production, particularly Coffee Leaf Rust (CLR) caused by Hemileia vastatrix. Early and accurate disease detection is essential for maintaining yield stability and ensuring sustainable coffee farming. This study proposes an Ensemble Convolutional Neural Network (CNN) architecture that combines MobileNetV2 and ResNet50 to enhance robustness and generalization in multi-class classification of coffee leaf diseases. The dataset consists of 1,664 images categorized into four classes: miner, nodisease, phoma, and rust, collected from both public repositories and real-field observations. Image preprocessing includes resizing, normalization, and augmentation to increase diversity and reduce overfitting. The ensemble model is trained using the Adam optimizer with a learning rate of 0.0001 and evaluated through accuracy, precision, recall, and F1-score metrics. Results demonstrate that the ensemble CNN outperforms single CNN architectures, achieving an accuracy of 95.6%, precision of 94.4%, and F1-score of 94.2%, even under challenging illumination and noise conditions. Compared to individual models, performance improvement ranges from 2%–4%. The model also maintains higher stability when tested under low-light and noisy images, confirming its robustness in real-world scenarios. This study concludes that ensemble CNN offers a reliable and efficient framework for real-time coffee leaf disease detection and can serve as a foundation for developing intelligent agricultural systems using edge computing.
Systematic Literature Review: Kecerdasan Buatan untuk Penilaian Kualitas Telur secara Non-Destruktif Andi Nur Rachman; I Made Oka Widyantara
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Systematic Literature Review (SLR) is a structured research approach used to map scientific developments, identify research gaps, and provide evidence-based knowledge synthesis. This study aims to systematically review the literature on the application of machine vision, artificial intelligence (AI), and deep learning in egg quality detection, with a particular focus on duck eggs as the research object. Egg quality assessment is crucial in the poultry industry, both to determine suitability for consumption and to ensure successful hatching. However, manual inspection methods are still widely applied, which often result in inaccuracies and inconsistencies. Using the PRISMA methodology, a total of 120 articles published between 2015–2024 were initially identified, of which 45 were selected as relevant studies after screening and eligibility checks. The review results indicate a significant increase in detection accuracy, shifting from conventional image-processing techniques to advanced algorithms such as CNN, ResNet-50, and YOLOv8, achieving accuracies above 94%. Major challenges remain, including the lack of publicly available datasets, risks of overfitting, and limited real-world implementation. This study concludes that future research directions should focus on the integration of lightweight IoT-based systems, standardized duck egg datasets, and hybrid methods (image–spectroscopy) to improve accuracy, robustness, and practical adoption of egg quality detection systems.
Penerapan Clustering Kinerja Pengelolaan Sampah Daerah Indonesia dengan Algoritma K- Means Marisa; Belinda Eka Sarah Dewi; Satria, Satria; Panca Indah Lestari
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Waste management is a major problem in Indonesia that requires a comprehensive assessment. This study utilizes data from 2024 obtained from the National Waste Management Information System (SIPSN) managed by the Ministry of Environment and Forestry (KLHK), with performance indicators including the level of waste management and waste handling. The technique applied is K-Means Clustering with the use of the Elbow Method to determine the number of the most efficient clusters. The findings indicate that the most efficient clusters consist of three categories: cluster 0 (low-performance areas), cluster 1 (medium-performance areas), and cluster 2 (high-performance areas). Areas that show high performance are characterized by a high proportion of managed and handled waste that is almost 100%. Based on the analysis results, Bogor Regency is included in the group with the best performance in waste management, so it can be used as a reference for other regions in implementing successful and sustainable waste management strategies.
Implementasi Sistem Informasi Data Pelanggan Dengan Metode Soft System Methodology (SSM) Suratman, Suratman; Sofa Sofiana
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The rapid development of information technology has had a significant impact on various aspects of life, including the business world. One aspect affected is customer data management. Customer data is a valuable asset that can be utilized to improve services, optimize business processes, and support more informed decision-making. However, many laundry businesses, including Melia Laundry & DryCleaning BSD, still manage customer data manually using bookkeeping or spreadsheets. To address this issue, a web-based customer data management information system is needed that can help Melia Laundry & DryCleaning BSD manage customer data better. In developing this system, the Soft System Methodology (SSM) approach is used, which is a method suitable for solving complex and unstructured problems, such as customer data management in laundry businesses. Through this research, the developed customer data management information system can simplify recording, storing, and searching for customer data with more than 80% accuracy, and more structured reporting.
Digital Transformation of Village Administration: Development of Integrated Population and Social Assistance Information System Yera Wahda Wahdi; Muhamad Safi’i; Novi Hendri Adi; Atman Lucky Fernandes; Riski Wulandari Hutagaol
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The management of population data in rural areas creates significant barriers to the effective distribution of social assistance. This research aims to develop and validate an integrated web-based information system that combines population administration with a multi-level social assistance verification mechanism to improve targeting accuracy and administrative efficiency in Ngal Village, Karimun Regency, Riau. The system was developed using the Waterfall Method through five systematic stages: requirements analysis, system design using UML modeling, web-based implementation, Black Box testing, and instrument validation. The research results show an integrated system capable of managing population data and social assistance eligibility criteria in real-time. Black Box testing shows that all system functions were successfully executed with a 100% success rate. Validity analysis using Aiken-V on 32 respondents showed very high values across four dimensions: System Function (0.98), Content Suitability (0.98), Ease of Use (0.95), and Interface Design (0.92). System implementation successfully reduced and eliminated data duplication errors and improved administrative efficiency. This research fills a critical gap in the village information systems literature by providing an integrated solution that addresses population administration and social assistance distribution. The validated system demonstrates effectiveness in an island context with limited connectivity, offering a replicable model for digital transformation in rural governance.
Peningkatan Klasifikasi Kanker Paru-Paru Melalui Rekayasa Fitur Interaksi Faktor Resiko Ananda Rizki Fitria; Agnes Prameswari; Aas Mirawati; Lidina
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study aims to improve the accuracy of lung cancer classification by applying a feature engineering-based machine learning approach from risk factor interactions. The data used comes from the Lung Cancer Risk Dataset on Kaggle, which contains 50,000 patient records with demographic, lifestyle, and medical condition variables. The preprocessing stage includes normalization, one-hot encoding, and the formation of interaction features that represent the nonlinear relationship between smoking habits, environmental exposure, and medical history. Two Random Forest models were compared: a baseline model without interaction features and an expanded model with interaction features. The results showed that the baseline model achieved an accuracy of 0.6973, while the model with interaction features achieved 0.6949, with better interpretability. Visualization through confusion matrices, feature importance plots, and SHAP analysis showed the contribution of engineered features to the interpretability of the model. These results indicate that interaction-based feature engineering can enrich model transparency and provide deeper clinical insights, and has the potential to be applied in clinical decision support systems and precision-based prediction models.
Analisis Segmentasi Pelanggan Mall Menggunakan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran Dede Kurnia Putri; Mega Susilowati; Tria Nissa Nurhayati; Wina Nurfadilah
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

In today’s competitive retail environment, understanding customer behavior is essential for shopping malls to maintain loyalty and increase sales. Many mall managers still face challenges in identifying customer spending patterns because available data is underutilized. Based on field observations and related studies, marketing strategies often miss their targets due to limited analysis of customer characteristics, leading to wasted budgets and low campaign effectiveness. The root of the problem lies in the lack of data analytics implementation to objectively map customer behavior. To address this, the K-Means Clustering algorithm is applied to segment mall customers based on annual income and spending score. The research process involves collecting secondary data from public sources, performing data cleaning and normalization using the Min–Max method, and evaluating cluster quality using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results divide customers into five distinct groups with varying income and spending patterns. The purpose of this study is to help mall management create more targeted and efficient marketing strategies aligned with each segment’s behavior. The findings show that K-Means Clustering provides valuable insights into customer shopping patterns and can serve as a foundation for improving promotional effectiveness and customer satisfaction through data-driven decision-making.
Analisis Bibliometrik SDM AI Industri Indonesia Berbasis OpenAlex: Metode CTM Syahadatul Aditya; Aswin Rosadi
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study maps the research landscape of HR–AI in the Indonesian industry context using OpenAlex open data for the 2015–2025 period (with 2025 being partial). After curation (deduplication, language detection, normalization, and removal of very short documents), the corpus was analyzed using Contextualized Topic Models (CTM) based on multilingual embeddings and compared with a lightweight baseline, Latent Semantic Analysis(LSA). The number of topics was tested at K = 10; K* was determined by the peak of c_v coherence and cross-checked with c_npmi on candidate K values. The results show that CTM consistently outperforms LSA in coherence across the same K range and yields more stable topic labels. A topics-over-time analysis highlights four major thematic clusters—workforce upskilling/reskilling, HR analytics and process automation, AI governance and ethics (including compliance with Indonesia’s Personal Data Protection Law, UU PDP), and the digital transformation of education/curricula—with several topics exhibiting upward trends since 2019. These findings offer practical recommendations for organizations and policymakers: designing analytics-driven competency-building programs, strengthening data and model governance and audits, and integrating curricula aligned with industry needs. All artifacts (processed data, tables, and figures) are provided to support replication and verification.

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