Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
Analisis Penguatan Tata Kelola Keamanan Informasi melalui Pengelolaan Kata Sandi
Pengelolaan kata sandi masih menjadi isu krusial dalam keamanan informasi digital karena perilaku pengguna dapat berkontribusi terhadap kebocoran kredensial, kompromi akun, dan akses tidak sah. Penelitian ini bertujuan menganalisis peran tata kelola data dalam memperkuat praktik pengelolaan kata sandi pada mahasiswa sebagai pengguna akun digital. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan desain cross-sectional melalui kuesioner daring terhadap 50 responden yang dipilih menggunakan teknik purposive sampling. Data dianalisis menggunakan statistik deskriptif berupa frekuensi dan persentase. Hasil penelitian menunjukkan bahwa 46 responden (92%) pernah menggunakan kata sandi yang sama pada lebih dari satu akun, sedangkan hanya 4 responden (8%) yang tidak pernah menggunakan ulang kata sandi. Meskipun 33 responden (66%) sering atau selalu menggunakan kombinasi karakter yang kuat, indikator penggunaan password manager masih rendah karena 54% responden tidak pernah memanfaatkannya. Penerapan autentikasi dua faktor juga belum merata karena sebagian responden hanya menggunakannya pada beberapa akun atau belum menggunakannya sama sekali. Temuan ini menunjukkan bahwa praktik pengelolaan kata sandi belum sepenuhnya mencerminkan prinsip tata kelola data, khususnya kontrol akses, kerahasiaan, dan akuntabilitas. Oleh karena itu, diperlukan edukasi keamanan digital yang berkelanjutan, peningkatan literasi penggunaan password manager, penerapan autentikasi dua faktor, serta kebijakan akses institusional yang lebih jelas untuk memperkuat keamanan informasi digital.
Copyright (c) 2026 Nabil Surya Al Hakim, Abdul Majid https://creativecommons.org/licenses/by-sa/4.0
Evaluasi Usability Desain Website Global Skills Training Center Menggunakan Maze Design dan User Experience Questionnaire
Global Skills Training Center merupakan lembaga pelatihan kerja non-formal yang belum memiliki website resmi sebagai media informasi dan promosi. Perancangan website company profile dilakukan menggunakan metode Design Thinking guna menghasilkan antarmuka yang berorientasi pada kebutuhan pengguna. Penelitian ini berfokus pada evaluasi usability dan pengalaman pengguna terhadap prototipe website yang dikembangkan menggunakan dua instrumen pengujian, yaitu Maze Design dan User Experience Questionnaire (UEQ). Pengujian Maze Design melibatkan 23 responden yang menyelesaikan 12 skenario berbasis alur interaksi pada prototipe Figma. Setiap skenario dievaluasi berdasarkan tiga indikator utama, yaitu tingkat keberhasilan (success rate), waktu penyelesaian (avg duration), dan tingkat kesalahan (misclick rate), yang selanjutnya digunakan untuk menghitung Screen Usability Score (SCUS), Mission Usability Score (MIUS), dan Maze Usability Score (MAUS). Hasil pengujian Maze Design menghasilkan nilai MAUS sebesar 92, dengan seluruh skenario memperoleh nilai SCUS dan MIUS di atas 80, yang mengindikasikan tingkat usability yang tinggi. Pengujian UEQ dengan Short UEQ menghasilkan nilai mean Pragmatic Quality sebesar 2,315, Hedonic Quality sebesar 2,293, dan Overall sebesar 2,304. Berdasarkan benchmark UEQ yang mencakup 468 produk dengan 21.175 responden, seluruh skala berada pada kategori Excellent, yaitu dalam rentang 10% hasil terbaik. Temuan ini membuktikan bahwa rancangan website Global Skills Training Center memiliki kualitas usability dan pengalaman pengguna yang sangat baik, serta layak untuk diimplementasikan sebagai website resmi lembaga.
Copyright (c) 2026 Ari Putra Wibowo, Devi Sugianti, Kayla Hikmah Aulia https://creativecommons.org/licenses/by-sa/4.0
Analisis Multi-Kriteria Pemilihan Marketplace bagi UMKM Menggunakan Metode Weighted Product
Transformasi digital telah mengubah strategi perdagangan dan bisnis menjadi lebih modern melalui penggunaan marketplace sebagai media transaksi daring. Marketplace memberikan kemudahan bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) dalam memasarkan produk, memperluas jangkauan pasar, serta meningkatkan efektivitas penjualan tanpa harus memiliki toko fisik. Berbagai marketplace di Indonesia menawarkan fitur dan layanan yang beragam, seperti jumlah pengguna, biaya layanan, fitur promosi, dan sistem pengiriman. Kondisi tersebut menyebabkan pelaku UMKM menghadapi kesulitan dalam menentukan marketplace yang paling sesuai dengan kebutuhan usahanya. Oleh karena itu, diperlukan suatu metode yang dapat membantu proses pemilihan marketplace secara sistematis dan terukur. Penelitian ini bertujuan untuk menganalisis pemilihan marketplace bagi UMKM menggunakan metode Weighted Product dalam Sistem Pendukung Keputusan (SPK). Penelitian menggunakan pendekatan kuantitatif dengan lima kriteria penilaian, yaitu kemudahan penggunaan, jumlah pengguna, biaya layanan, fitur promosi, dan sistem pengiriman. Alternatif marketplace yang dianalisis meliputi Shopee, Tokopedia, TikTok Shop, Lazada, Blibli, dan Bukalapak. Data penelitian diperoleh melalui studi dokumentasi terhadap informasi yang tersedia pada situs resmi marketplace, laporan statistik pengguna, serta sumber publik yang relevan. Selanjutnya dilakukan proses pembobotan, perhitungan vektor S, dan perhitungan nilai preferensi menggunakan metode Weighted Product. Hasil penelitian menunjukkan bahwa Shopee memperoleh nilai preferensi tertinggi sebesar 0,174 sehingga menempati peringkat pertama, diikuti oleh Tokopedia dengan nilai 0,172 dan TikTok Shop dengan nilai 0,171. Hasil tersebut menunjukkan bahwa metode Weighted Product dapat digunakan untuk mengevaluasi dan membandingkan alternatif marketplace berdasarkan kriteria yang telah ditetapkan secara terstruktur. Dengan demikian, metode ini dapat menjadi alat bantu pengambilan keputusan bagi UMKM dalam memilih marketplace yang sesuai dengan karakteristik usaha dan kebutuhan pemasaran digitalnya.
Copyright (c) 2026 Nawwir Junari https://creativecommons.org/licenses/by-sa/4.0
Analisis Sentimen Komentar Trailer Youtube Film Pelangi di Mars Menggunakan IndoBERT
Pertumbuhan industri perfilman Indonesia meningkatkan jumlah opini penonton pada platform digital, khususnya YouTube. Kolom komentar pada trailer film dapat dimanfaatkan untuk mengetahui respons dan persepsi penonton terhadap sebuah film. Namun, volume komentar yang besar serta penggunaan bahasa tidak baku pada media sosial menyebabkan analisis manual menjadi kurang efektif. Penelitian ini bertujuan untuk menganalisis sentimen komentar pengguna pada trailer film Pelangi di Mars di YouTube menggunakan model pre-trained IndoBERT. Data penelitian diperoleh melalui proses web scraping komentar YouTube pada periode 24 November 2025 hingga 5 Mei 2026 dan menghasilkan 2656 komentar. Setelah proses seleksi data, diperoleh 1898 komentar yang digunakan dalam penelitian. Tahap preprocessing meliputi cleaning text, case folding, tokenizing, normalisasi kata tidak baku, stopword removal, dan stemming. Proses klasifikasi sentimen dilakukan menggunakan model Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis pada Hugging Face Transformers tanpa proses fine-tuning. Hasil klasifikasi menunjukkan terdapat 839 komentar positif, 546 komentar negatif, dan 513 komentar netral. Evaluasi model menghasilkan nilai accuracy sebesar 72,23%, precision sebesar 80,86%, recall sebesar 72,23%, dan F1-score sebesar 74,11%. Hasil penelitian menunjukkan bahwa mayoritas pengguna memberikan respons positif terhadap trailer film Pelangi di Mars, serta model IndoBERT memiliki kemampuan yang cukup baik dalam melakukan klasifikasi sentimen komentar berbahasa Indonesia pada media sosial.
Copyright (c) 2026 Siska Amelia, Migel Orvin Febryan, Muhammad Rizky Pribadi https://creativecommons.org/licenses/by-sa/4.0
Komparasi Kinerja Algoritma Random Forest dan Decision Tree dalam Klasifikasi Penyakit Jantung
Penyakit jantung masih menjadi penyakit yang merenggut banyak nyawa, sehingga sangat penting untuk menemukan cara yang lebih cepat dan akurat dalam mendiagnosisnya. Perkembangan pesat dalam pembelajaran mesin kini membuka peluang untuk memanfaatkan berbagai algoritma klasifikasi guna memprediksi penyakit jantung berbekal data medis pasien. Studi ini berupaya membandingkan efektivitas algoritma Decision Tree dan Random Forest dalam mengklasifikasikan penyakit jantung dengan memanfaatkan kumpulan data Penyakit Jantung. Tahapan penelitian mencakup pengumpulan data, pembersihan dan penataan data awal, penjelajahan data (EDA), pembagian data menjadi set pelatihan dan pengujian, pelatihan model, serta penilaian kinerja melalui Confusion Matrix dan perhitungan tingkat akurasi. Berdasarkan evaluasi, algoritma Decision Tree mencapai tingkat akurasi 93,44% pada data pelatihan dan 87,34% pada data pengujian. Analisis Confusion Matrix untuk model ini mencatat 123 kasus True Negative, 146 True Positive, 27 False Positive, dan 12 False Negative. Sementara itu, algoritma Random Forest tampil lebih superior dengan akurasi pelatihan 99,02% dan akurasi pengujian 97,73%. Confusion Matrix untuk Random Forest mencatat 147 True Negative, 154 True Positive, 3 False Positive, dan 4 False Negative. Temuan ini menunjukkan bahwa Random Forest menawarkan akurasi yang lebih baik dan mengurangi kesalahan prediksi dibandingkan dengan Decision Tree. Hal ini mengindikasikan bahwa Random Forest lebih handal dalam mengidentifikasi pola data yang berkaitan dengan penyakit jantung dan lebih baik dalam menggeneralisasi hasilnya. Dengan demikian, algoritma Random Forest direkomendasikan sebagai pendekatan yang lebih efektif untuk klasifikasi penyakit jantung karena kemampuannya memberikan prediksi yang lebih akurat dan stabil.
Copyright (c) 2026 M. Nejatullah Sidqi, Rio https://creativecommons.org/licenses/by-sa/4.0
Perancangan Visual Aset Karakter dan Lingkungan 3D untuk Game Edukasi Berbasis Spasial
Kemampuan spasial merupakan salah satu keterampilan kognitif yang penting dalam pembelajaran geometri karena berkaitan dengan kemampuan memvisualisasikan objek, memahami hubungan antar bentuk, dan mengenali orientasi ruang. Media pembelajaran berbasis game 3D dinilai mampu mendukung peningkatan kemampuan tersebut melalui aktivitas eksplorasi ruang secara interaktif. Penelitian ini bertujuan menghasilkan aset 3D berupa karakter dan lingkungan labirin yang digunakan sebagai dasar pengembangan game edukasi bertema labirin 3D untuk melatih kemampuan spasial. Metode penelitian yang digunakan adalah Multimedia Development Life Cycle (MDLC) yang terdiri dari tahapan concept, design, material collecting, assembly, testing, dan distribution. Aset yang dikembangkan meliputi karakter utama bernama Urik, dua monster bernama Bramsball dan Rogarok, serta satu jalur labirin sebagai lingkungan eksplorasi. Proses pemodelan dilakukan menggunakan Autodesk Maya melalui tahapan shaping, slicing, texturing, dan lighting hingga proses rendering. Tahap pengujian dilakukan melalui alpha testing oleh pihak internal untuk memastikan kesesuaian aset terhadap rancangan awal, serta pengujian oleh dua ahli 3D yang memiliki pengalaman industri selama tiga tahun dan dua puluh dua tahun. Hasil pengujian memperoleh total skor gabungan sebesar 66 dari skor maksimal 80 dengan rata-rata persentase sebesar 82,5% yang termasuk kategori “Sangat Baik”. Hasil tersebut menunjukkan bahwa aset 3D telah memenuhi kualitas visual dan teknis yang memadai untuk digunakan dalam pengembangan game edukasi. Penelitian ini memberikan kontribusi sebagai dasar produksi visual dalam pengembangan media pembelajaran berbasis game untuk mendukung pelatihan kemampuan spasial.
Copyright (c) 2026 Yoseph Satria Praka, Rifki Setiawan, Muhammad Thoriq Suriansyah, Thompson Nathanael Simarmata, Mira Rosalina https://creativecommons.org/licenses/by-sa/4.0
Research Trend on Data Mining Using Bibliometric Analysis with VOSviewer
This study aims to analyze global research trends in data mining using bibliometric analysis. The rapid development of information technology has transformed data mining into a crucial tool for various industrial sectors to extract knowledge from large databases. The research method used is a qualitative descriptive approach with a bibliometric approach, assisted by Publish or Perish (PoP) software for data collection from ScienceDirect and Google Scholar databases. Data visualization and mapping were performed using VOSviewer to identify topic clusters, temporal developments, and research density between January 2022 and December 2026. The analysis results indicate the existence of five main clusters: technical aspects of algorithms (red), technological and industrial infrastructure (green), geographic applications and environmental impacts (blue), causality analysis (yellow), and literature synthesis (purple). Overlay visualization reveals a shift in trends from mastery of basic algorithm infrastructure (such as random forests and big data) to a critical evaluation phase focused on risk mitigation, research gap identification, and practical application in the real world. This study provides a strategic overview for researchers to identify collaboration opportunities.
Copyright (c) 2026 Dika Putra Wijaya, Mohammad Kawtsar, Manon Guinny, Ganesh Ganesh, Nur Laila, Wirda Amirotul Amiroh https://creativecommons.org/licenses/by-sa/4.0
Development of the Educational Game “Aksara Swara” for Learning Javanese Letters Based on Construct 3
Learning Javanese script still faces various challenges, particularly the low interest and understanding of students due to the limited use of interactive learning media. Javanese script is often considered difficult because of its diverse character forms and its limited use in daily life. Therefore, alternative learning media that are more interactive and engaging are needed. This study aims to develop an educational game entitled “Aksara Swara” as an interactive learning medium for introducing Javanese vowel characters. This research employs a Research and Development (R&D) method using the Four-D development model (Define, Design, Develop, Disseminate). The game was developed using Construct 3 with a quiz-based concept and different levels of difficulty. Product testing was conducted using functional testing (black box testing) to ensure that all game features functioned properly according to the design. The results indicate that the “Aksara Swara” game functions well, has an easy-to-understand gameplay mechanism, and is able to present Javanese script learning materials interactively. Therefore, the educational game “Aksara Swara” can be used as an alternative learning medium to support the introduction of Javanese script in an enjoyable way.
Copyright (c) 2026 Muhammad Syahrul Falah, Muhammad Muzaki Maruf, Faza Afwan Nugroho https://creativecommons.org/licenses/by-sa/4.0
A Comprehensive Study of Information Security Principles, Threats, and Organizational Protection Measures
Information security has become a vital specification and element in modern digital and electronic environments as organizations, governments, and individuals at an accelerating rate rely on information systems to fulfill indispensable operations. The rapid growth of digital communication, cloud computing, mobile technologies, and the Internet of Things (IoT) has augmented the volume of data generated and transmitted, making it more susceptible to cyber threats. Information security underscores on protecting data confidentiality, integrity, and availability through a synthesis of technical, organizational, and human-centered measures. This abstract provides summary of key elements of information security, examines major emerging threats, and highlights the importance of embracing comprehensive security frameworks. Cyberattacks such as ransomware, phishing, Distributed Denial of Service (DDoS), and social engineering have become more advanced, addressing system susceptibilities and human behavior. These attacks can result in financial loss, data breaches, reputational damage, and operational disruption. As a result, organizations must carry out robust security frameworks, including encryption, access control mechanisms, multi-factor authentication, intrusion detection and prevention systems, firewalls, and progressive system tracking. In addition, the integration of artificial intelligence and machine learning has enhanced cybersecurity capabilities by enabling automated threat detection and predictive analysis. However, besides technological advancements, human factors remain a major cause of security breaches. Employee negligence, weak passwords, lack of awareness, and susceptibility to social engineering attacks continue to undermine security efforts. Therefore, effective information security needs not only advanced tools but also strong organizational policies, regular training programs, and a special way of security awareness. Overall, information security is a flexible and evolving field that requires a nonstop adaptation to new threats and technologies. A holistic approach that brings togethar technical solutions, human-centered techniques, and regulatory compliance is essential for safeguarding digital assets and ensuring the resilience of information systems in an increasingly interconnected world.
Copyright (c) 2025 Mukhtar Ahmad Paiman, Serajulhaq Afghan, Abdul Karim Himmat https://creativecommons.org/licenses/by-sa/4.0
Predicting Student Dropout in E-Learning Using Simple Machine Learning and Explainable Data Analysis
Online learning allows a lot of flexibility and accessibility, but the issue of student dropout becomes one of the key problems. Much of the research that has been done to predict dropout using complex machine learning methods has not been interpretable, and many of these methods are hard to implement in practice, especially in resource constrained environments. This research closes this gap by presenting a simple and explainable machine learning method of predicting early dropout. Students in several universities were surveyed on the frequency of attendance, quiz performance, completion of assignments, satisfaction with learning, and hours spent studying per week. Because there were only a few real dropout cases, a controlled synthetic data augmentation method was used to demonstrate and train the model. The use of Logistic Regression and Decision Tree classifiers was used to predict the risk of dropout. The accuracy of both models was 87.5% with the most effective indicators being learning satisfaction, attendance, and prior consideration of dropout. This research is considered new because it focuses on the simplicity and interpretability of models rather than the complexity of creating early warning systems to predict the dropout of students, showing that they are not required to be complex or made out of heavyweight models to perform well. Even though the findings are merely exploratory due to limitations of the datasets, the results show that even simplistic models might be used to assist educators in identifying at-risk learners and incorporating prompt intervention measures.
Copyright (c) 2025 Aliyu Ibrahim Ahmad, D.A Nugroho, S.A. Aliyu, A.M. Abdullahi https://creativecommons.org/licenses/by-sa/4.0
Pengembangan Sistem Informasi Peminjaman Fasilitas Kampus Berbasis Natural Language Processing dengan Metode Extreme Programming
The facility (room and equipment) loan system within the Fakultas Ekonomi dan Bisnis (FEBI), Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan is still carried out manually, often using online forms and direct communication with staff. This manual process causes various problems, including schedule conflicts, asynchronous data, and a lack of transparency regarding space and asset availability. This study aims to develop a Natural Language Processing (NLP)-based Room and Equipment Loan Information System that is integrated, transparent, and can work in real-time. The system development method used is Extreme Programming (XP), which was chosen because of its adaptive, flexible nature, and focus on rapid iteration and continuous feedback from users. The XP stages include planning, design, coding, and testing. The information system is designed using the Laravel framework supported by a PostgreSQL database. The system's main functionality includes online loan applications by Borrowers, automatic validation of schedule conflicts, and approval by the Admin/Superadmin. The results show that this system is able to simplify processes, minimize schedule conflicts, and increase efficiency and transparency in the management of faculty assets.
Copyright (c) 2025 Mohammad Reza Maulana, Muhammad Rikzam Kamal, Agus Susilo Nugroho, Tamamudin https://creativecommons.org/licenses/by-sa/4.0
Rancang Bangun Sistem Smart Posbindu Penyakit Tidak Menular Berbasis Android untuk Deteksi Dini dan Monitoring Kesehatan Masyarakat
The rapid increase in internet usage (62%) and mobile phone ownership (90%) in Indonesia indicates the significant potential of digital technology to support health services. The Integrated Non-Communicable Disease Health Post (Posbindu PTM) in Mojo Village plays an essential role in early detection and community health monitoring. However, its implementation still faces several challenges, including manual schedule announcements, data recording errors and loss risks, as well as limited dissemination of health information and education. This study aims to design and develop a mobile Smart Posbindu PTM application based on Android as a digital solution to enhance the effectiveness of Posbindu services. The application was developed using the Dart programming language with the Flutter framework and Supabase as the database. Its main features include schedule notifications, digital data recording, fast and accurate reporting, health history monitoring, and educational information related to non-communicable diseases. The system development method used in this study is the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and maintenance stages. Data were collected through interviews with midwives and Posbindu cadres, direct observation, and questionnaires distributed to Posbindu participants. The application was tested using User Acceptance Test (UAT) methods to ensure functionality and user satisfaction. The results show that the Smart Posbindu PTM application was successfully developed and functioned properly according to user needs. This application provides significant benefits by improving data management efficiency, accelerating reporting, facilitating health record monitoring, and supporting health education and information accessibility for the community.
Copyright (c) 2025 Much. Rifqi Maulana, Nur Ika Royanti, Ratno Aji Winoto https://creativecommons.org/licenses/by-sa/4.0
Klasifikasi Penyakit Pada Buah Jambu Biji Menggunakan Algoritma Yolo V5
Horticultural agriculture, especially guava (Psidium guajava), has great economic potential in Indonesia. However, productivity often declines due to fruit disease attacks, which are still manually diagnosed by farmers. This study aims to develop an artificial intelligence-based guava disease classification system using the You Only Look Once (YOLO) version 5 algorithm. The dataset consists of 600 images divided into three disease classes: Phytophthora, Styler and Root, and Scab. Data were collected through field documentation, then preprocessed and augmented using Roboflow. The dataset was divided into 70% training data, 20% validation, and 10% testing. The YOLOv5 model was trained using Google Collaboratory and consistently evaluated using the Confusion Matrix and accuracy, precision, recall, and F1-score metrics. The test results showed that the model achieved an accuracy of more than 95% with high precision, recall, and F1-score values ​​for each disease class. This proves that YOLOv5 is effective for real-time guava disease detection. This research contributes to the application of artificial intelligence technology to help farmers make early diagnoses quickly and accurately, thereby reducing the risk of reduced crop yields.
Copyright (c) 2025 Nadiya Rezika, Elmayati, Novi Lestari https://creativecommons.org/licenses/by-sa/4.0
Smart System Kumbung Jamur Tiram dengan IoT dan Sensor DHT11-LDR
Oyster mushroom cultivation requires stable environmental conditions, particularly in terms of temperature, humidity, and lighting. Traditionally, the management of mushroom houses has been carried out manually, which often leads to inefficiency and production failures. This study aims to design and implement a Smart System for oyster mushroom houses based on the Internet of Things (IoT) using DHT11 and LDR sensors. The research employed the Research and Development (R&D) method with six main stages, namely needs analysis, planning, initial product development, limited trials, product revision, and product testing. The system was designed using NodeMCU as the main controller, with the DHT11 sensor to measure temperature and humidity, and the LDR sensor to detect light intensity. Data are displayed through an LCD and transmitted in real time to an Android application. The design validation test obtained a score of 30, categorized as “Good,” while the limited user trial involving 12 respondents achieved an average score of 35.16, categorized as “Very Good (Valid).” The system automatically activates the fan when the temperature exceeds 28°C, turns on the water pump when humidity is low, and switches on the lamp when light intensity is insufficient. Therefore, this system can assist mushroom farmers in maintaining optimal environmental conditions in real time and automatically, which is expected to improve both the quality and productivity of oyster mushroom cultivation.
Copyright (c) 2025 Rusito, Andika Bagas Rahmawan https://creativecommons.org/licenses/by-sa/4.0
Approach of Zero Trust Security to Improve Internet of Things Infrastructure Security
The heterogeneity and resource constraints of Internet of Things (IoT) devices render traditional perimeter security inadequate. This study proposes a Zero Trust Security (ZTS) framework for IoT infrastructures that integrates a novel dynamic policy engine with continuous authentication and AI-assisted anomaly detection. The framework was evaluated in a simulated IoT environment using the TON_IoT dataset. Experimental results demonstrate that the proposed model achieved a 92.5% detection accuracy, reduced average response latency to 1.76 seconds, and decreased unauthorized access attempts by 87.1%. The key novelty lies in the architecture's context-aware feedback loop, where anomaly findings directly and adaptively inform access policies in real-time, a mechanism not extensively explored in prior ZTS models for IoT. These findings confirm that integrating ZTS with intelligent analytics significantly enhances IoT security resilience. This framework offers a practical blueprint for implementing robust, context-aware security in large-scale IoT applications, such as smart cities and industrial automation.
Copyright (c) 2025 Muchamad Rusdan; Isak Ramlan https://creativecommons.org/licenses/by-sa/4.0
Aplikasi Web Pemesanan Makanan Berbasis QR Code untuk Area Food Court
The use of QR codes in food ordering systems is increasingly being used in restaurants or F&B (Food and Beverage) as a practical and efficient solution. This article discusses the planning of a web-based application prototype that integrates QR codes at each table in the food court, which allows buyers to access digital menus from various F&B tenants and place orders on the website application. This web application is also friendly to smartphones/mobile phones because it does not install additional applications and only uses a web browser and does not need to purchase hosting services because it uses the local/wi-fi network available at the food court. The purpose of this development is to improve the existing QR Code ordering system by adding table personalization features to the food court, ordering efficiency, and integration of all tenants in one platform.
Copyright (c) 2025 Muhammad Syahrul Falah https://creativecommons.org/licenses/by-sa/4.0
Consumer Decision-Making in Car Purchases: Insights from a Logistic Regression Analysis
Indonesia, with its large population, presents significant consumer demand, particularly in the automotive sector. Consequently, numerous car brands actively invest and market their products within the country. Among the 36 car brands officially operating in Indonesia, Toyota has consistently dominated the market. This study specifically examines consumer perceptions and decision-making processes regarding Toyota car purchases using logistic regression analysis. It investigates the impact of perceived risk, price sensitivity, convenience, and customer satisfaction on consumers' purchasing choices. Using a quantitative approach, data were collected from Indonesian Toyota consumers through purposive sampling and analyzed using the logistic regression. The findings reveal that perceived risk and price significantly influence consumers' decisions to purchase Toyota cars, highlighting the importance for Toyota and other automotive brands to strategically manage risk perceptions and pricing policies to enhance consumer appeal and market share.
Copyright (c) 2025 Sandy J. H. Simamora, Mujiya Ulkhaq, Naniek Utami Handayani https://creativecommons.org/licenses/by-sa/4.0
Expert System untuk Rekomendasi Pemilihan Bahasa Pemrograman bagi Pemula Menggunakan Algoritma Decision Tree
This study develops an expert system based on the Decision Tree algorithm to recommend suitable programming languages for beginners, addressing the challenge of selecting the right language amid the abundance of options and diverse learning goals. This topic is significant because choosing the appropriate language can accelerate the learning process and improve the effectiveness of programming education. The research methodology includes the creation of a synthetic dataset comprising 1,500 entries, with the addition of 5% noise. This noise is introduced to simulate real-world data imperfections and to test the model's robustness against unclean or imperfect data. The next stages involve data preprocessing through encoding and normalization, followed by modeling using the Decision Tree algorithm with hyperparameter optimization to enhance model performance. Evaluation results show an accuracy of 95%, with learning goals (38% contribution) and platform preference (35%) emerging as the most influential factors in decision-making. A 10-fold cross-validation produced an average error of 0.046, indicating model stability across various data subsets. Feature importance analysis revealed that the model logically prioritizes technical relevance, for example, by ranking learning goals and platform preference above demographic features, as these are more directly related to the context and practical use of programming languages. The implemented system successfully provided relevant recommendations, such as Python for Data Science and JavaScript for Web Development. This study concludes that the Decision Tree algorithm is effective for recommendation systems based on user profiles, although data enhancement is needed for minority classes such as Java. These findings contribute to the development of more personalized and adaptive programming learning support tools.
Copyright (c) 2025 Yuke Manza, M. Rhifky Wayahdi https://creativecommons.org/licenses/by-sa/4.0
Analisis Komparatif Efisiensi Memori dan Waktu Komputasi pada 8 Algoritma Sorting menggunakan C++
This study aims to analyze the efficiency of computation time and memory allocation of eight sorting algorithms (Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Merge Sort, Heap Sort, Counting Sort, and Radix Sort) implemented in C++ programming language. The test dataset consists of three size categories: 100, 1,000, and 10,000 elements, randomly generated with values between 1 and 99. This range was chosen so that the tests are conducted under conditions of limited value and contain a lot of duplication, in order to support consistent efficiency evaluation. The research method involved generating datasets using the random array function, measuring execution time in nanoseconds, and monitoring memory usage through the WorkingSetSize metric. Each algorithm was tested three times on each category of data to ensure consistency of results. The results showed that Heap Sort achieved the fastest execution time on small data (101,266 nanoseconds for 100 elements), Counting Sort and Radix Sort excelled on medium data, while Counting Sort delivered the best performance on large data (1,483,166 nanoseconds for 10,000 elements). Counting Sort also demonstrated stable memory efficiency compared to the other algorithms, whereas Bubble Sort consistently exhibited the poorest performance across all scales. The research conclusion recommends Heap Sort for small-scale data, Counting Sort and Radix Sort for medium-scale data, and Counting Sort for large-scale data. The findings provide practical guidance for developers in selecting algorithms according to data scale and resource availability.
Copyright (c) 2025 Muhammad Irfan Ali, Rangga Dzikri Fardiarsyah, Lukman Shodik, Fadilah Zahra Dwi Kinanti, Imam Prayogo Pujiono https://creativecommons.org/licenses/by-sa/4.0
Perbandingan Kemampuan AI Grok, ChatGPT, dan Gemini dalam Analisis Konten Media Sosial
This study aims to compare the capabilities of three artificial intelligence (AI) platforms—Grok (XAI), ChatGPT (OpenAI), and Gemini (Google)—in analyzing social media content, specifically on Twitter (X). A descriptive qualitative method with a case study approach was employed. Three types of content—text, images, and videos—were used as test materials. Each AI platform was tasked with analyzing sentiment, context, and meaning embedded within the content. The results indicate that Grok excels in direct integration with Twitter, enabling real-time contextual reading of posts and interactions. In contrast, ChatGPT and Gemini demonstrated superior performance in in-depth analysis and multimodal interpretation when provided with explicit input. This study provides early insights into the comparative strengths and limitations of AI platforms for social media content analysis, offering practical recommendations for researchers and practitioners in selecting appropriate AI tools for digital public opinion analysis.
Copyright (c) 2025 Bernardus Herdian Nugroho https://creativecommons.org/licenses/by-sa/4.0
Analisis Cluster Hierarki Pada Tingkat Kemiskinan di Provinsi Sumatera Utara Tahun 2023
Poverty is one of the problems that is still a priority in social and economic development in Indonesia. In 2023, North Sumatra Province had a poverty rate of 8.15% with a total of 1,239.71 thousand poor people. The condition of poverty by district/city in North Sumatra Province is very diverse. The purpose of this study is to group districts/cities in North Sumatra Province in 2023 based on poverty rate indicators using hierarchical clusters. The data used in this study is secondary data with four poverty variables from 33 districts/cities in the North Sumatra Province in 2023. The results of the analysis contained 3 clusters with the number of members in clusters 1, 2, and 3. Suggestions for the North Sumatra Provincial Government need to formulate adaptive and specific policies based on the characteristics of each regional cluster prioritizing high poverty problem areas through strengthening access to education, health, and productive economic development. In addition, intensive cooperation between regions within the same cluster should be developed to support information exchange, joint empowerment programs, and local economic development synergies to improve the effectiveness of interventions and the welfare of the community in a sustainable manner.
Copyright (c) 2025 Bagus Candra Setiawan, Arum Nilawati, Rahmad Ferdian, Raihan Aditya Saputra, Yemima Putri Santoso, Muhammad Riefky https://creativecommons.org/licenses/by-sa/4.0
Identifikasi Citra Jenis Rempah-Rempah Menggunakan Arsitektur RestNet50
Indonesia has various types of spices used in culinary and traditional medicine. However, changes in lifestyle and modernization have made it increasingly difficult for the younger generation to recognize spices directly. Conventional identification still relies on manual observation which is prone to errors. Therefore, an artificial intelligence-based solution is needed to improve the accuracy of spice classification. This study applies the Convolutional Neural Network (CNN) method with the ResNet50 architecture, which is part of Deep Learning, to classify digital images of spices. This model utilizes Computer Vision to recognize visual patterns, Transfer learning to improve training efficiency, and Data Augmentation Techniques such as rotation, flipping, and scaling to improve model robustness. Evaluation using Confusion Matrix was carried out with various dataset division scenarios, including ratios of 90:10, 80:20, 70:30, 60:40, and 50:50. The experimental results showed that the model with a ratio of 90:10 provided the best accuracy, reaching 98.04%, with high precision, recall, and F1-score. In conclusion, the CNN method with ResNet50 has proven effective in identifying spices based on digital images. Further development can be done by adding variations of datasets and exploring other Deep Learning architectures to improve model performance.
Copyright (c) 2025 Christy Atika Sari, Luthfiyana Hamidah Sherly Pradana, Eko Hari Rachmawanto https://creativecommons.org/licenses/by-sa/4.0
The Ethical Aspects of Applying Artificial Intelligence in the Healthcare System
Artificial intelligence is abundant nowadays, including healthcare practice. However, there are ethical aspects of applying artificial intelligence (AI) in healthcare practice. The paper aims to describe the ethical aspects of applying artificial intelligence (AI) in the healthcare system. This paper is a narrative literature review. Literature was taken from Science Direct, PubMed, and Google Scholar. Selected journals were published within 5 years. AI-based algorithms commonly work based on the provided data. Nevertheless, the variations among patients’ conditions, insurance records, and diagnostic results are wide. Some data such as social determinants might not be seen by the AI system. This kind of data determines the biases in data sets and health disparities. Therefore, the algorithms should be well designed to minimize bias and align with the ethical dimensions. Predictive algorithms might be used to predict care decisions and outcomes. Incorrect diagnosis might be developed while using AI tools directly without expert opinion. In conclusion, the ethical dimensions of applying AI in the healthcare practice are minimizing bias, determining the variations of social conditions among patients, and using expert opinion other than relying on AI only.
Copyright (c) 2025 Yuliana . https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence and the Transformation of Digital Services in Islamic Banking: A Case Study of Bank Syariah Indonesia
Digital transformation in the Islamic banking sector is necessary in the era of technological disruption. However, implementing artificial intelligence (AI) still faces challenges, especially in non-metropolitan areas. This research aims to explore the role of AI in transforming digital services at Bank Syariah Indonesia (BSI) Semarang Branch, while emphasizing its compliance with maqashid Sharia principles. This research uses a phenomenological qualitative approach through in-depth interviews, participatory observation, and analysis of internal documentation of employees and customers. The main findings show that AI significantly improves service efficiency through digital onboarding, chatbot “Hasanah Assistant”, and a pattern-based automated transaction system. This innovation is considered to accelerate services while maintaining the principles of property protection (hifzh al-mal) and life (hifzh al-nafs). However, challenges arise regarding AI’s limited adaptation to local cultural contexts, senior employees’ resistance to digitalization, and customers’ limited digital literacy. This highlights the importance of a hybrid approach between technology and humanization of Islamic banking services. The uniqueness of this research lies in its focus on regional branches that have not been widely explored in the academic literature, as well as its contribution to expanding the understanding of AI as a strategic medium, not just an automation tool. The implications of this research emphasize the need for an efficient, ethical, and inclusive AI system design, oriented towards Islamic values. The research suggests a participatory digital transformation strategy that is adaptive to local values and consistent with maqashid Sharia to ensure the sustainability of technological innovation in Islamic banking.
Copyright (c) 2025 Luqman Syakirunni’am, Zohain Hasan Sain, Syauqie Muhammad Marier, Syukron Jamil https://creativecommons.org/licenses/by-sa/4.0
Pengujian Sistem Informasi Pendaftaran Job Fair di Institut Widya Pratama dengan teknik Equivalence Partitioning
Institut Widya Pratama (IWIMA) continuously strives to produce high-quality graduates. To provide better opportunities for its graduates, IWIMA routinely organizes job fairs to connect job seekers with potential employers. One of the challenges in conducting these events is the registration and data management process for job seekers. To address this issue, IWIMA developed a job fair registration information system for job applicants. A comprehensive testing process is essential to ensure the system functions effectively and efficiently. One of the techniques that can be used for system testing is equivalence partitioning. This technique ensures that the input and output align with the application design by applying predefined testing scenarios. The research stages include designing the testing process, conducting the tests, recording the results, and evaluating the outcomes. In the initial research stage, the testing design is developed by generating use cases, test parameters, test partitions, test data, and test cases. Subsequently, from the system testing conducted across seven test cases, the results indicate that the information system operates effectively as expected. However, based on the evaluation and analysis, it is recommended to further enhance the quality of the information system. These recommendations can serve as a reference for future system development.
Copyright (c) 2024 Mohammad Reza Maulana, Muhammad Faizal Kurniawan, Wahyu Setianto https://creativecommons.org/licenses/by-sa/4.0
Sistem Keamanan Kotak Paket Cerdas Berbasis IoT dengan Teknologi MQTT
MQTT protocol is used by smart package box security systems based on the Internet of Things (IoT) to ensure the security and speed of package delivery. This system uses ESP32 devices and several sensors, including a keypad and magnetic sensors, as well as actuators like door lock solenoids and an LCD connected to an MQTT server that can be accessed via Android devices. The main features of this system include notifications about package arrivals, recipient identity verification using the last four digits of the phone number, and two operating modes, automatic and manual. After being tested under various conditions, this system has shown that it can effectively maintain security and monitor package status in real-time. The test results show that the smart package box hardware and the MQTT server can communicate well, allowing couriers and owners to communicate.
Copyright (c) 2024 Dani Sasmoko, Reni Veliyanti, S. Anang Wijayanto https://creativecommons.org/licenses/by-sa/4.0
Implementasi QR Code Untuk Sistem Informasi Pemesanan Menu Pada Restoran Omah Gedhe Pandean Kaliwungu Berbasis Web
This study aims to develop and implement a web-based menu ordering information system using QR Codes at Omah Gedhe Pandean Kaliwungu Restaurant. The system is designed to enhance efficiency and convenience in the food ordering process at the restaurant. QR Codes are used as a medium to access the digital menu, allowing customers to scan the codes with their mobile devices to directly view the menu and place orders without waiting for a waiter. The results of this implementation show that the QR Code-based information system can improve service speed, reduce ordering errors, and provide a more interactive experience for customers. This research employs the Research and Development (R&D) method by Brog & Gall, which is used to produce and test specific products. The study uses 6 out of the 10 steps of this method. Validation results from experts and users show an average validation score of 31.5 from 2 respondents, which falls within the 31-40 range on the value indicator table, leading to the conclusion that the system design is "Highly Valid."
Copyright (c) 2024 Rusito Rusito, Diyah Intan Mawarni Putri https://creativecommons.org/licenses/by-sa/4.0
Museum Guide Application Menggunakan Augmented Reality Berbasis Android
This research develops a museum guide application using Android-based Augmented Reality (AR) technology, with a focus on the Lampung Transmigration Museum. The purpose of this research is to improve visitors' educational experience and attract more tourists by presenting museum collection information interactively and visually. The research methods used are qualitative and quantitative approaches, as well as the Multimedia Development Life Cycle (MDLC) model consisting of concept, design, material collection, assembly, testing, and distribution stages. The results showed that this application succeeded in increasing visitor attraction and engagement, as well as providing easier access to information about museum collections. Tests show that the marker can be detected well within a distance of 8 to 50 cm, and can still be recognized even if it is obstructed by 50%. It is hoped that this application can be a practical solution in delivering information in museums, as well as increasing public interest in cultural heritage
Copyright (c) 2024 Agustinus Eko Setiawan Eko, Dinda Rahmadanty, Ratnasari, Dwi Yana Ayu Andini https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence dan Filsafat Ilmu: Bagaimana Filsafat Memandang Kecerdasan Buatan Sebagai Ilmu Pengetahuan
Artificial intelligence is a concept of implanting knowledge in machines to be able to carry out activities or activities like humans do, which by implanting artificial intelligence, has the aim of helping humans in completing everything that humans cannot do. This understanding then raises the question of whether artificial intelligence then becomes part of science itself. This article is compiled with a qualitative approach based on literature studies assisted by content analysis methods. From the results of the study, it is obtained that artificial intelligence is included in the understanding of the intermediary domain which in principle runs and becomes part of science, both theoretical and empirical. This makes artificial intelligence not only have philosophical implications for epistemological status, but also axiologically and ontologically which are part of the philosophy of science. Not only that, artificial intelligence also requires practical consequences with which artificial intelligence becomes part of science itself
Copyright (c) 2024 Arditya Prayogi, Riki Nasrullah https://creativecommons.org/licenses/by-sa/4.0
Pengembangan Desain Antarmuka Sistem Informasi Keuangan Kas RT Berbasis Mobile dengan Metode Design Thinking
This research aims to develop a mobile-based financial information system design for RT Cash, using the design thinking method. The object of this research is RT 06, Kelurahan Kuripan Yosoreko, Kecamatan Pekalongan Selatan, Kota Pekalongan. RT 06 is one of the RTs that has utilized information technology for recording and reporting residents' cash. Currently, the information system used in RT 06, Yosorejo Village is a web-based information system with several user accounts for RT officials, including the RT Chairman, Treasurer, and Secretary. The design thinking method is used because it can be used to explore various possibilities that prioritize the user as the main actor. The stages performed in this research include empathize, define, ideate, prototype, and testing. The research results indicate that the development of this mobile-based financial information system design for RT Cash is acceptable and can be used as a solution for users. Based on the usability testing conducted, all selected respondents were able to complete all test scenarios on the new interface design
Copyright (c) 2024 Ari Putra Wibowo, Arief Soma Dharmawan, Muhammad Faizal Kurniawan, Devi Sugianti, Risqiati https://creativecommons.org/licenses/by-sa/4.0