Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu (IKOSSTEMI) dengan No. ISSN: 3123-2574 (online) merupakan salah satu kegiatan yang diselenggarakan oleh Raskha Media Group Publisher. Seminar Nasional IKOSSTEMI (Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu) dilaksanakan untuk memberikan dorongan kepada para Dosen, Mahasiswa, dan Penelitidalam melakukan publikasi di Forum Ilmiah Nasional dengan harapan manfaatnya dapat segera dirasakan masyarakat secara luas. Publikasi para dosen, mahasiswa, dan peneliti akan di terbitkan dalam bentuk Prosiding IKOSSTEMI (Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu). Prosiding IKOSSTEMI (Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu) ini di terbitkan oleh oleh Raskha Media Group Publisher, yang dikelola oleh tim redaksi yang berpengalaman di bidangnya. Raskha Media Group Publisher Sesuai dengan data dalam format isian Pendaftaran yang disimpan di dalam Sistem Administrasi Badan Usaha berdasarkan Akta Nomor 196 tanggal 10 Oktober 2023 yang dibuat oleh Notaris ALWINE ROSDIANA PAKPAHAN, SH berkedudukan di Kota Medan, beserta dokumen pendukungnya yang diterima tanggal 11 Oktober 2023 mengenai pendaftaran CV RASKHA MEDIA GROUP disingkat RKMG berkedudukan di Kota Medan telah diterima dan terdaftar dalam Sistem Administrasi Badan Usaha. Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu (IKOSSTEMI) dengen No. ISSN: 3123-2574 (online) ini mencakup beberapa bidang ilmu sebagai berikut: Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu.
Rancang Bangun Sistem Pendukung Keputusan Manajemen Pemeliharaan Itik Manila Berbasis Web Dan Analisis Morfogenetik
Peternakan itik Manila di Indonesia menghadapi tantangan dalam mengoptimalkan produktivitas karena manajemen pemeliharaan yang masih bersifat tradisional dan kurang berbasis data. Penelitian ini bertujuan untuk merancang dan membangun sistem pendukung keputusan (SPK) berbasis web yang terintegrasi dengan data karakteristik morfometrik, morfologi, dan genetik itik Manila di Sumatera Utara. Metode yang digunakan adalah pengembangan sistem dengan pendekatan prototyping, yang mencakup analisis kebutuhan, perancangan basis data, pengembangan antarmuka, dan uji coba sistem. Fitur utama sistem ini meliputi basis data profil itik terintegrasi, modul prediksi bobot badan dan produksi telur menggunakan model regresi linier berganda (R²=0.378 untuk bobot badan jantan dan R²=0.799 untuk produksi telur), modul rekomendasi manajemen pemeliharaan berdasarkan klaster genetik, modul pencatatan dan monitoring produksi harian, serta dashboard analisis usaha. Hasil pengujian menunjukkan bahwa sistem mampu memprediksi produktivitas dengan akurasi yang baik dan memberikan rekomendasi manajemen yang sesuai dengan karakteristik genetik itik. Sistem ini diharapkan menjadi solusi teknologi tepat guna bagi peternak untuk meningkatkan efisiensi, produktivitas, dan keberlanjutan usaha peternakan itik Manila di Indonesia
Copyright (c) 2026 Andhika Putra, Rio Septian Hadinata (Author) https://creativecommons.org/licenses/by/4.0
Disaster-Responsive Governance dan Kompetensi Guru dalam Membangun Resiliensi Pendidikan pada Pendidikan Islam Pascabencana Banjir
Floods are one of the most frequent threats to the continuity of the education system in Indonesia. Their impacts include not only damage to school infrastructure but also reduced quality of educational services, hampered learning processes, and weakened the resilience of educational institutions in facing crises. In the context of Islamic education, these challenges are increasingly complex because teachers are not only tasked with maintaining the continuity of learning but also carry out character development, psychosocial support, and strengthening students’ spiritual values. This study aims to analyze the relationship between Disaster-Responsive Governance and teacher competency in building educational resilience in Islamic educational units after flood disasters. The study used a qualitative approach with a case study design in flood-affected areas in Aceh Tamiang Regency. Data were collected through in-depth interviews, field observations, and analysis of policy documents and reports on educational disaster management. Data analysis was conducted using thematic analysis techniques through data reduction, categorization, interpretation, and conclusion drawing. The research findings indicate that low educational resilience is not solely due to the magnitude of the disaster’s impact, but is also influenced by weak disaster-responsive educational governance, limited coordination between stakeholders, and suboptimal teacher adaptive competencies in dealing with emergency situations. Teachers are required to play a broader role as facilitators of adaptive learning, psychosocial counselors, communication mediators with the community, and reinforcers of Islamic values ​​in the educational recovery process. This research offers a conceptual model that integrates Disaster-Responsive Governance, adaptive teacher competencies, and contextual factors as a foundation for building educational resilience in Islamic educational institutions. This model is expected to serve as the basis for developing more adaptive, inclusive, and sustainable educational policies in disaster-prone areas.
Copyright (c) 2026 Alfian Sani (Author) https://creativecommons.org/licenses/by/4.0
Spatial Data Analysis for Environmental and Climate Monitoring Using Google Earth Engine and Random Forest Classification
Land-cover change is a primary driver of environmental degradation and local climate alteration, yet up-to-date, spatially explicit information remains scarce in rapidly developing regions such as North Sumatra, Indonesia. This study aims to develop an accurate, reproducible workflow for environmental and climate monitoring by integrating cloud-based spatial data processing with machine-learning classification. Multitemporal Sentinel-2 surface-reflectance imagery (2019 and 2024) and Landsat-derived land-surface temperature were processed on the Google Earth Engine (GEE) platform. Spectral bands were combined with vegetation, water, and built-up indices (NDVI, NDWI, NDBI, EVI, SAVI, BSI) and topographic data, after which a Random Forest (RF) classifier distinguished six land-cover classes from stratified training samples. The RF model achieved an overall accuracy of 92.4% and a kappa coefficient of 0.906, outperforming Classification and Regression Tree (86.1%) and Support Vector Machine (88.7%) baselines. NDVI, the near-infrared band, and NDWI were the most influential predictors. Between 2019 and 2024 the built-up area expanded by 18.7% while forest and cropland contracted, and these changes coincided with a measurable rise in land-surface temperature, confirming a strong inverse relationship between vegetation cover and surface heating. The results demonstrate that the GEE–RF approach provides a low-cost, scalable, and replicable basis for routine environmental and climate monitoring to support evidence-based regional planning.
Copyright (c) 2026 Supiyandi Supiyandi, Ramlah binti Mailok (Author) https://creativecommons.org/licenses/by/4.0
UI/UX Design of ”Cari Kos PKU” Application Using Design Thinking Method
Students and employees often face difficulties in finding boarding houses that meet specific criteria such as location, price, and facilities. Inefficient and time-consuming search processes are the primary obstacles. This research aims to design the user interface and user experience (UI/UX) of the "Cari Kos PKU" application as a digital solution for boarding house searches in Pekanbaru City that is user-friendly and meets user needs. The method employed is Design Thinking, which includes the stages of Empathize, Define, Ideate, Prototype, and Test. Prototype design was conducted using Figma, resulting in both low-fidelity and high-fidelity designs. Testing utilized the System Usability Scale (SUS) with 10 questions. Based on responses from 31 participants, the test yielded a SUS score of 70.24, categorized as "Acceptable."The "Cari Kos PKU" solution provides location and price filters for targeted searching. Verified property information assists users in making better and faster decisions. This application is expected to serve as a reference for developing more comprehensive mobile-based housing search platforms in the future. This study also demonstrates that the Design Thinking approach is effectively applied in UI/UX design based on user needs, as each stage focuses on a deep understanding of the real problems faced by students and employees. Featuring filter-based searches, a favorites system, direct messaging to property owners, and informative property details, the "Cari Kos PKU" application is designed to provide an intuitive, efficient, and pleasant user experience. The results of this research are expected to guide developers of similar applications in designing better digital housing platforms, particularly to meet the needs of students and employees in Pekanbaru City.
Copyright (c) 2026 Anton Afandi, Fahriel Abdul Rasyid, Nurva Fitri Andini, Salsabila Sahbani, Yoyon Efendi, Tashid (Author) https://creativecommons.org/licenses/by/4.0
Media Pembelajaran Interaktif AR Book Pengenalan Jenis Hewan Berbasis Augmented Reality Pada Anak Usia Dini
Augmented Reality (AR) merupakan teknologi yang dapat menampilkan objek virtual dua dimensi (2D) maupun tiga dimensi (3D) secara real time melalui layar smartphone atau perangkat digital lainnya. Dalam penerapannya, teknologi AR memanfaatkan marker berupa gambar atau citra tertentu untuk menampilkan objek 3D yang telah dirancang pada sistem. Implementasi Augmented Reality saat ini banyak dimanfaatkan dalam berbagai bidang, salah satunya pada bidang pendidikan sebagai media pembelajaran interaktif bagi anak usia dini. Permasalahan yang sering ditemukan dalam proses pembelajaran adalah penggunaan media belajar yang masih bersifat konvensional sehingga anak mudah merasa bosan dan kurang tertarik saat mengenal jenis hewan. Penelitian ini bertujuan untuk merancang media pembelajaran interaktif AR Book pengenalan jenis hewan berbasis Augmented Reality pada anak usia dini menggunakan Assemblr Studio. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model pengembangan ADDIE yang terdiri dari tahap analysis, design, development, implementation, dan evaluation. Jenis hewan yang digunakan meliputi hewan darat, laut, dan udara yang ditampilkan dalam bentuk objek tiga dimensi (3D) melalui proses pemindaian marker menggunakan smartphone. Media pembelajaran juga dilengkapi dengan informasi ciri-ciri hewan sehingga membantu anak memahami materi dengan lebih menarik dan interaktif. Hasil penelitian menunjukkan bahwa media pembelajaran AR Book berbasis Augmented Reality dapat digunakan dengan baik sebagai sarana pembelajaran serta membantu meningkatkan minat belajar anak usia dini dalam mengenal jenis hewan.
Copyright (c) 2026 Farhan Fadhel Muhammad, Saiba Anjeli, Yuke Try Hafsani, Yoyon Efendi, Nurul Utami (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Federated Learning untuk Klasifikasi Penyakit dengan Dataset Tidak Seimbang
Imbalanced data is one of the main challenges in applying machine learning to stroke disease classification, where the number of stroke patient data is significantly smaller than non-stroke patient data. This condition causes classification models to become biased toward the majority class, resulting in poor detection of stroke cases. In addition, centralized learning approaches raise privacy and security concerns due to the sensitive nature of patient data. Therefore, this study proposes a Federated Learning Random Forest (FL-RF) approach to improve classification performance on imbalanced data while preserving data privacy. The study uses the Cerebral Stroke Prediction – Imbalanced Dataset obtained from Kaggle. The research stages include data preprocessing, semi non-IID data distribution across multiple clients, local training using Balanced Random Forest, and model aggregation within a federated learning environment. Model evaluation was conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show that the conventional Random Forest achieved an accuracy of 0.9817 but failed to detect the minority class. Meanwhile, the proposed FL-RF model obtained an accuracy of 0.7976 with an improved recall of 0.56 and an F1-score of 0.0910 for the stroke class. These findings indicate that the FL-RF approach is more effective in improving sensitivity toward minority classes compared to conventional Random Forest.
Copyright (c) 2026 Syarifah Yusnaini Putri, Nia Ramadani, Andre Hasudungan Lubis (Author) https://creativecommons.org/licenses/by/4.0
Pergeseran Otoritas Guru di Tengah Maraknya Bimbingan Belajar
Fenomena bimbingan belajar dalam lanskap pendidikan kontemporer tidak lagi dipandang hanya sebagai layanan tambahan di luar sekolah. Dalam banyak konteks, bimbingan belajar bergerak mengikuti sistem persekolahan formal, terutama dalam hal mata pelajaran, target akademik, dan orientasi evaluasi. Artikel ini bertujuan menelaah bagaimana kaitan antara maraknya bimbingan belajar dan pergeseran otoritas pedagogik guru di sekolah. Penelitian ini menggunakan pendekatan kualitatif berbasis literature review dengan menelaah berbagai pustaka yang relevan yang diperoleh melalui berbagai sumber/basis literatur. Hasil kajian menunjukkan bahwa bimbingan belajar tidak hanya berfungsi sebagai pendamping akademik, tetapi juga menjadi ruang belajar yang semakin dipercaya siswa karena dianggap lebih praktis, komunikatif, dan responsif terhadap kebutuhan ujian. Kondisi ini mendorong terjadinya pergeseran pusat kepercayaan belajar dari guru kepada tutor, meskipun guru tetap memiliki legitimasi formal di sekolah. Pergeseran tersebut dipengaruhi oleh budaya pendidikan yang berorientasi hasil, keterbatasan pembelajaran di kelas, serta meningkatnya ekspektasi orang tua terhadap capaian akademik.
Copyright (c) 2026 Arditya Prayogi, Lilik Riandita, Bagas Mukti Nasrowi, Riki Nasrullah (Author) https://creativecommons.org/licenses/by/4.0
Kelapa Sawit Dan Keberlanjutan: Integrasi Pertanian Untuk Meningkatkan Kesejahteraan Masyarakat
Penelitian ini bertujuan untuk menganalisis peran integrasi pertanian berbasis kelapa sawit dalam meningkatkan keberlanjutan dan kesejahteraan masyarakat di Kecamatan Kuala. Metode yang digunakan adalah pendekatan deskriptif kualitatif dengan studi kasus. Data diperoleh melalui wawancara, observasi, dan studi literatur. Hasil penelitian menunjukkan bahwa integrasi pertanian, khususnya model sawit–ternak dan tanaman sela, mampu meningkatkan pendapatan petani, efisiensi biaya produksi, serta mendukung keberlanjutan lingkungan. Analisis kuantitatif menunjukkan bahwa pendapatan bersih sistem integrasi (Rp 32,1 juta/ha/tahun) lebih tinggi dibandingkan sistem non-integrasi (Rp 23 juta/ha/tahun), dengan nilai R/C ratio masing-masing 3,4 dan 2,9. Dengan demikian, integrasi pertanian menjadi strategi efektif dalam meningkatkan kesejahteraan masyarakat dan keberlanjutan agribisnis kelapa sawit.
kelapa sawit, integrasi pertanian, keberlanjutan, kesejahteraan, agribisnis
Copyright (c) 2025 M. Wasito, Yudi Siswanto, Dimas Alfarid (Author) https://creativecommons.org/licenses/by/4.0
Moodify: An Intelligent Music Player Based on User Mood Using Machine Learning
Mood plays a vital role in influencing human productivity, emotions, and learning effectiveness. Music is widely known as one of the media that can influence and adjust human mood. However, most music player applications still rely on manual playlist selection and do not adapt dynamically to the user's emotional condition. This study proposes Moodify, an intelligent music player application that utilizes machine learning techniques to detect user mood and provide personalized music recommendations. The problem addressed in this research is the lack of adaptive music recommendation systems based on real-time user mood. The purpose of this research is to design and analyze a mood-based music recommendation system that can enhance user experience. The research method uses a machine learning-based classification approach to identify user mood from input data, followed by a recommendation algorithm that matches music characteristics with detected moods. The results show that Moodify is able to recommend music that aligns with user emotional states, improving comfort and engagement. This research contributes to the development of intelligent multimedia applications that support emotional well-being and personalized digital experiences.
Copyright (c) 2025 Abdul Hadi Lubis, Rendra Jogia Sakti, Abdullah Royhan, Ika Bagus Sunandri, Andrian Agustin Sitanggang, Yoyon Efendi (Author) https://creativecommons.org/licenses/by/4.0
AlfaOne Application Redesign Using Lean UX Method
AlfaOne application is a digital service application owned by PT Sumber Alfaria Tri Jaya TBK which not only provides transaction features, but also various supporting services that make it easier for users to carry out daily activities digitally. This application is designed to increase ease of access to services for users without having to come directly to physical outlets. The existence of the AlfaOne application is expected to increase service effectiveness, reduce queues at outlets, and encourage increased use of digital services. However, based on the results of initial observations, the features and interface design of the AlfaOne application are considered less than optimal and less attractive, resulting in a decrease in user interest and comfort in using the application. Therefore, a redesign of the user interface (UI) and user experience (UX) is needed as a basis for building a platform that is more attractive, easy to use, and in accordance with user needs. The Lean UX method was chosen in this study because it focuses on user satisfaction through an iterative, collaborative, and user feedback-based design process. Based on the results of the analysis, implementation, and evaluation, this study produced a final prototype which is a combination of prototype A and prototype B which has been validated in terms of appearance, functionality, as well as criticism and suggestions from users and technical parties. Prototype A was selected for 3 features and prototype B was selected for the other 3 features. On the login page, design B was selected with a percentage of 53%, the home page was selected with a percentage of 57%, and the history page was selected with a percentage of 62%. In addition, the results of the study showed that the proposed interface design has consistency in the use of color, typography, icons, and layout, and provides a more understandable user experience in accessing features and information as needed. The final design was tested using the Usability Testing method to measure the level of ease of use and user acceptance of the proposed alfaone application design.
Copyright (c) 2025 Saiba Anjeli, Yuke Try Hafsani, Yoyon Efendi, Tashid Tashid, Nurul Utami (Author) https://creativecommons.org/licenses/by/4.0
Measuring Awareness of the Importance of Green Computing Among Indonesian Senior High School Students: A Sample Study of One School
Green computing socialization was conducted at SMK Muhammadiyah 8 Medan on May 31, 2025, to enhance student awareness regarding environmentally friendly technology practices. The primary issue addressed was the need to build a collective responsibility towards the environment within the school's digital culture. The purpose of this study is to measure the level of student awareness and evaluate the effectiveness of the education program which utilized lectures, demonstrations, and interactive discussions. The research method used is descriptive with a participatory approach, collecting data through observation and questionnaires. The results indicated a significant improvement in understanding among the 27 participants, with 8 participants responding "very understood" and 14 participants responding "understood" regarding the material. The activity had a positive impact, fostering enthusiasm for applying energy-saving principles in daily life. These findings highlight the potential of green computing education to form a generation that is not only academically competent but also ecologically responsible.
Copyright (c) 2025 Rio Rinaldi, Fayyaz Haqqani, Dhafa Hibrizi Sitorus (Author) https://creativecommons.org/licenses/by/4.0
Education on Green Computing Awareness at SMK Free Methodist Medan
Green computing education was conducted at SMK Free Methodist Medan on March 25, 2024, to improve students’ understanding of environmentally friendly information technology practices. The main issue identified was the low awareness of energy consumption in ICT devices, electronic waste management, and efficient computing behavior. The purpose of this study is to evaluate the effectiveness of the education program using lectures, demonstrations, and interactive discussions. The research method used is descriptive, with data collected through observation and questionnaires. The results show a 67% improvement in student understanding after the program. The activity had a positive impact on initial behavioral changes among students in adopting more energy-efficient ICT usage. These findings highlight the importance of implementing green computing education in vocational schools as a foundation for environmentally aware digital behavior.
Copyright (c) 2025 Muhammad Zikri Al Hakim Nasution Nasution, Muhammad Zikri Al Hakim Nasution, Rizky Agung Simatupang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Use of Threads and X Applications as Digital Social Interaction Media Among Students
Penggunaan media sosial telah meningkat dengan cepat, dan platform seperti X (sebelumnya Twitter) dan Threads telah menjadi platform utama bagi siswa untuk terlibat secara sosial. Studi ini bertujuan untuk membandingkan pola penggunaan kedua aplikasi ini dan menganalisis preferensi siswa terhadapnya. Penelitian ini menggunakan desain kuantitatif deskriptif; dengan pengumpulan data dilakukan melalui kuesioner yang disebarkan kepada mahasiswa dari berbagai universitas. Analisis statistik yang digunakan meliputi analisis frekuensi, analisis persentase, uji Chi-Square, dan uji-T. Hasil penelitian menunjukkan bahwa X (Twitter) lebih dominan digunakan oleh siswa dibandingkan dengan Threads. Siswa menggunakan X (Twitter) untuk informasi pendidikan dan diskusi sosial, sementara Threads lebih sering digunakan untuk percakapan pribadi dan kasual. Selain itu, tingkat kepuasan dengan X lebih tinggi daripada dengan Threads. Implikasi dari studi ini menunjukkan bahwa meskipun X digunakan lebih sering, Threads berpotensi menjadi platform alternatif untuk percakapan sosial yang lebih intim dan kasual .
Copyright (c) 2025 Gehan Ibra Alfath Hasibuan, Nazwa Aliya Muthmainnah Hsb, Supiyandi Supiyandi (Author) https://creativecommons.org/licenses/by/4.0
Evaluation of Renewable Energy Consumption Patterns: Based on National Consumption Data Analysis
This study evaluates renewable energy consumption patterns in Indonesia based on national data from 2019 to 2023. The main issue addressed is the low proportion of renewable energy use and its unequal distribution across regions. The objectives are to analyze sectoral and regional trends in renewable energy consumption, identify key influencing factors, and develop a dynamic evaluation model for national energy policy. A mixed-method approach with quantitative emphasis was applied, utilizing data from the Central Statistics Agency (BPS) and the Ministry of Energy and Mineral Resources, supported by spatial analysis using Geographic Information Systems (GIS) and spatial regression to assess regional disparities. Results indicate an average annual increase of 5.2% in renewable energy consumption, contributing 12% to total national energy in 2023. The transportation sector showed the fastest growth (22.5% annually), while the household sector remained dominant in absolute terms. Spatial analysis revealed high inequality (Gini index = 0.62), with Java and Bali accounting for the majority of consumption. Key factors include electricity infrastructure affordability (β = 0.34, p < 0.01) and local policy (β = 0.28, p < 0.05). These findings highlight the need for a holistic policy approach to promote a sustainable energy transition that enhances efficiency and supports socio-economic equity.
Copyright (c) 2025 Revina Putri Damayanti, Dinda Ayu Ningsih , Aulia Balqis Hummairoh (Author) https://creativecommons.org/licenses/by/4.0
Promoting Green Computing to The Community as A Strategy for E-Waste Reduction and Cleaner Rivers
Electronic waste (e-waste) poses a serious threat to environmental sustainability, particularly to river ecosystems that serve as vital water sources and centers for community activities. However, public awareness regarding the dangers of e-waste and its proper management remains low, specifically in the Pantai Walikota area, Medan Tuntungan. This community service activity aims to socialize the principles of green computing to the local community as a strategic effort to reduce e-waste and maintain river cleanliness. The activity was conducted using a participatory education method, which included interactive lectures, discussions, and the distribution of educational brochures and posters to 10 residents living along the riverbank. The effectiveness of the program was measured through observation and direct feedback. The results showed a significant increase in public understanding; initially, only 25% of participants were familiar with green computing concepts. Post-socialization, 75% of participants gained new knowledge regarding e-waste hazards, and the majority expressed a willingness to adopt wiser device management practices. This study concludes that direct, contextual socialization in riverside areas is an effective strategy for fostering environmental responsibility and contributing to cleaner river ecosystems.
Copyright (c) 2025 Dedek Juliani Ritonga, Ermaliza, Siti Dian, Supiyandi (Author) https://creativecommons.org/licenses/by/4.0
Computer-Based Data Visualization Analysis for Simplifying Complex Information
This study aims to analyze global temperature data by employing computer visualization as a tool to simplify complex information. The dataset was obtained from Kaggle, specifically the Global Land Temperatures by City dataset, which contains monthly average temperature data from various cities worldwide. The methods applied include data preprocessing, descriptive statistical analysis, and data visualization using the Python programming language with the Pandas, Matplotlib, and Seaborn libraries. The visualization results reveal an upward trend in the global average temperature from 1900 to 2020, with an increase of approximately 1°C, indicating the occurrence of global warming. Computer visualization has proven to be effective in helping researchers and policymakers better understand temperature change patterns compared to numerical table-based analysis. Therefore, this study emphasizes that the application of computer visualization is an efficient solution for presenting and analyzing large-scale data, making it more interpretable.
Copyright (c) 2025 Salsabila Nasution, Fatwa Aulia, Saprina Putri Utama Ritonga, Anggi Jelita Sitepu, Supiyandi Supiyandi (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Effect of Sieve Size on the Efficiency and Quality of Milling Results of Dried Shrimp
This study was conducted to investigate the operation of the dried shrimp grinding machine. This study was also undertaken to determine the differences in grinding results using three different sieve sizes. There are three sieve sizes used, namely, 5 mm, 7mm, and 9mm. This study was conducted to determine the results of grinding dried shrimp from fine to coarse, as the demand for ground shrimp in the market varies. Therefore, the researcher conducted this study with the aim of meeting market demand.
Copyright (c) 2025 Juanda Purba, Jhon Supriadi Purba, Tambos August Sianturi (Author) https://creativecommons.org/licenses/by/4.0
Cybersecurity Analysis of Information Technology Infrastructure in the Era of Cloud Computing
In the era of cloud computing, cybersecurity has become a critical concern for organizations relying on digital infrastructure. The rapid adoption of cloud-based systems offers scalability, flexibility, and efficiency, yet it simultaneously introduces complex security challenges such as data breaches, unauthorized access, and system vulnerabilities. This paper presents an analytical study of cybersecurity issues affecting information technology (IT) infrastructure in the cloud computing environment. The research explores potential threats, evaluates existing security frameworks, and discusses strategies for enhancing data protection and risk management. A mixed-method approach, combining literature review and case analysis, was employed to identify key factors influencing cloud security performance. The findings reveal that while cloud service providers implement robust security measures, human error, weak authentication mechanisms, and inadequate policy enforcement remain major risk factors. The study emphasizes the importance of adopting a multi-layered security model, integrating encryption, identity management, and continuous monitoring to ensure data integrity and confidentiality. In conclusion, maintaining cybersecurity in the cloud era requires a balance between technological innovation and organizational awareness. Strengthening cybersecurity resilience demands collaboration among stakeholders, continuous updates of security protocols, and the development of adaptive policies aligned with emerging threats.
Copyright (c) 2025 Siti Khodijah, Nur Syifa’u Sitha, Raihan Syafawi (Author) https://creativecommons.org/licenses/by/4.0
Teknologi Digital untuk Meningkatkan Pendidikan dan Melestarikan Warisan Budaya melalui Museum Interaktif Indonesia
Museum adalah sumber penting pendidikan dan pelestarian budaya. Namun, pengunjung museum tradisional semakin berkurangan karena perilaku yang berubah dari generasi muda, yang lebih tertarik pada media digital. Tujuan penelitian ini adalah untuk membuat prototipe museum interaktif yang menggunakan AI untuk meningkatkan pendidikan dan pelestarian budaya di Indonesia. Analisis kebutuhan, desain sistem, pengembangan prototipe, dan pengujian pengguna adalah bagian dari proses penelitian ini, yang dilakukan melalui pendekatan penelitian dan pengembangan (R&D). Skala Kegunaan Sistem (SUS) dan wawancara semi-terstruktur yang dilakukan dengan tiga puluh responden merupakan instrumen penelitian. Temuan penelitian menunjukkan bahwa prototipe museum interaktif mencapai skor SUS rata-rata 75,6, menempatkannya dalam kategori yang sangat baik, dan menerima umpan balik positif mengenai peningkatan keterlibatan pengunjung dalam memahami koleksi budaya. Penelitian ini berkontribusi pada pengembangan model museum digital yang memenuhi kebutuhan masyarakat Indonesia.
Copyright (c) 2025 Chairul Rizal, Muhammad Irfan Sarif, Muhammad Amin (Author) https://creativecommons.org/licenses/by/4.0
Real-Time Object Color Detection Using the HSV Method in Python
This study discusses real-time object color detection using the HSV (Hue, Saturation, Value) method in the Python programming language. A common problem in RGB-based color recognition is its high sensitivity to changes in light intensity, resulting in unstable detection results. Therefore, the HSV color space is used, which better aligns with human perception of color and is able to separate color (hue) information from lighting (value). By utilizing the HSV color space, the system can distinguish colors based on hue angles without being significantly affected by lighting levels. This approach is important because in real conditions, environmental lighting often changes, such as indoors or outdoors during the day and evening. The use of Python and the OpenCV library also facilitates the implementation of color detection algorithms with efficient computation time and interactive visualization results in real time. The research method was conducted experimentally using a laptop camera as video input, then each image frame was converted to the HSV color space. Next, a masking process was performed by determining the lower and upper limits of certain colors so that colored objects could be recognized. This stage is important to ensure that the system can recognize specific areas in the image that contain the target color. By creating an HSV range (threshold) for each color, the system will only display areas that match those criteria. This technique is often used in object tracking and color segmentation applications in the field of computer vision. The results of the study show that the system is able to accurately detect red, green, and blue objects with a success rate above 90% under normal lighting conditions. This system can be applied in robotics, surveillance systems, and computer vision-based industrial applications. This success demonstrates the great potential of the HSV method in various fields. In robotics, for example, color detection is used for autonomous navigation or path tracking. In surveillance systems, color can be an indicator of certain things, such as identifying dangerous objects. Meanwhile, in industry, this system can be used to separate products based on color in automated production lines.
Syahada Mawarda Hutagalung Syahada Mawarda Hutagalung
Muhammad Alfariz Rasyid Muhammad Alfariz Rasyid
Copyright (c) 2025 Muhammad Farhan, Syahada Mawarda Hutagalung, Muhammad Alfariz Rasyid, Dafa Ikhwanu Shafa, Supiyandi (Author) https://creativecommons.org/licenses/by/4.0
Automatic Lightweight CNN Waste Identification for Green Campus Program Support
Effective waste management is a key indicator of success for Green Campus Programs and a major factor in the UI GreenMetric World University Rankings. However, many Indonesian universities still face difficulties due to low accuracy in manual waste sorting, particularly for inorganic and hazardous (B3) waste. This study develops a lightweight, high-accuracy automatic waste classification system using the MobileNetV2 Convolutional Neural Network (CNN) architecture. The model was trained via transfer learning on the Kaggle “Trash Classification Dataset” (Sathish, 2023) containing 19,762 images from 10 waste classes: Metal, Glass, Biological, Paper, Plastic, Cardboard, Battery, Shoes, Clothes, and Trash. To align with operational needs of the Green Campus Program, these 10 classes were mapped into three functional categories: Organic, Recyclable, and Inorganic/B3. Experimental results show that MobileNetV2 achieved 93.2% accuracy with efficient inference time (~4.8 ms per image) on a CPU. The prototype, built using Python Streamlit, outputs both predicted waste type and confidence percentage, making it practical for real-time campus waste sorting. The proposed model provides an intelligent, energy-efficient, and transparent solution to support sustainable waste management, addressing key operational challenges in inorganic (WS4) and hazardous (B3) waste (WS5) handling
Copyright (c) 2025 Nouval Khairi, Yuda Apriyansyah, Haikal Habibi Siregar, Muhammad Farhan Aditya (Author) https://creativecommons.org/licenses/by/4.0