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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta Bulletin of Electrical Engineering and Informatics ILKOM Jurnal Ilmiah Data Science: Journal of Computing and Applied Informatics Journal of Robotics and Control (JRC) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Zainal Arifin Hasibuan
Universitas Indonesia
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health

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eCulture: Kerangka TIK Untuk Pelestarian Kebudayaan Nasional Salim, Agus; Hasibuan, Zainal Arifin
Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta Vol 6, No 1 (2012): Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta
Publisher : Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta

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Abstract

Kebudayaan merupakan salah satu aspek kehidupan yang mencerminkan ciri khas dari suatu bangsa. Berbagai masalah dan tantangan yang timbul, seperti : tersebar luasnya informasi budaya, tidak adanya basis data terpusat untuk menampung informasi budaya yang ada, kesulitan mengetahui ragam budaya pada daerah tertentu, belum dijadikannya budaya sebagai sumber pengetahuan, belum digunakannya budaya sebagai unsur pembangun karakter bangsa, dan infomasi budaya yang belum digunakan untuk pengembangan pariwisata. Dengan melakukan penelitian berdasarkan observasi dan studi literatur tentang budaya, kebudayaan, dan ragam budaya; ragam warisan budaya nasional; pelestarian budaya; eCulture; repositori budaya; Zachman framework; entity relationship diagram; context diagram dan data flow diagram; network diagram; dan activity diagram diharapkan dapat membantu merumuskan sebuah kerangka TIK untuk pelestarian kebudayaan nasional. Setelah melakukan observasi dan studi literatur, data yang terkumpul akan dianalisa dan diinterpretasi dengan menggunakan pendekatan seamless integration. Pada tahap akhir dari analisa dan interpretasi tersebut, maka akan terbentuk sebuah kerangka TIK yang lengkap berdasarkan Zachman framework. Kata kunci: eCulture, kerangka TIK, pelestarian kebudayaan, Zachman Framework
Indonesian e-Agriculture Strategic Framework: A Direction of ICT Usage as Enabler in Agriculture Rubhasy, Albaar; Hasibuan, Zainal Arifin
Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta Vol 6, No 1 (2012): Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta
Publisher : Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta

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Abstract

As indicated in many studies, a modern agriculture posts several problems. It tends to not environmentally friendly due to chemical usage, produced more waste, and the land is forced to produced exceeding its capacity. Beside that in the modern agricultural supply chain, farmers (producers) always in a weaker position as compare to distributors (intermediaries) and costumers. Lack of access to updated information leads to a poor judgment on what to plant, when to plant, how much to plant, and where to sale. This imbalanced of agricultural supply chain reduced the farm profitability. Furthermore, it creates a structured poverty in the farming communities due to weakened processes of farming resources ability to fulfill sufficient needs. ICTs could help small and medium farmers increase their revenues (which is related to farm profitability), improve their farming practices (which is related to environmental stewardship), and making it possible for them to access information on agricultural know-how through knowledge sharing among them (which is related to prosperous farming communities), and through research center. ICT can help to increase transparency, prevent corruption, optimal price discovery, information dissemination, usability, preservation and management of documents and content. However, it requires network and information security, interoperability, standardization of business processes and for localization and internationalization of content. All these components need to be structured in such a way into an Indonesian E-Agriculture Strategic Framework (IESF). IESF aims at deploying ICTs for sustainable development in agriculture area targeting ultimate beneficiaries (i.e. farmers) by providing direct-link among farmers, merchants, consumers, local governments with global markets, research center, banks, and so forth. Keywords: agricultural supply chain; E-Agriculture
Optimizing Parameters for Earthquake Prediction Using Bi-LSTM and Grey Wolf Optimization on Seismic Data Shidik, Guruh Fajar; Pramunendar, Ricardus Anggi; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Dolphina, Erlin; Kusumawati, Yupie; Sriwinarsih, Nurul Anisa
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22199

Abstract

Earthquakes pose a significant threat to societies worldwide, underscoring the urgent need for advanced prediction technologies. This study introduces an optimization technique aimed at reducing the error rate in earthquake prediction by selecting the most suitable parameters for a Bi-LSTM (Bidirectional Long Short-Term Memory) model. Despite Bi-LSTM's promising outcomes, variations in parameters can impact performance, necessitating careful parameter selection. This research employs Grey Wolf Optimization (GWO) to optimize parameters and evaluates its effectiveness against other group optimization approaches to identify the most efficient parameters for earthquake prediction. Additionally, a multiple input multiple output (MIMO) architecture is implemented to enhance prediction accuracy. The evaluation results demonstrate that GWO outperforms other optimization techniques, achieving a reduced loss score of 0.364. The ANOVA method yields a p-value approaching 0, indicating statistical significance. This study contributes to the development of early warning systems for earthquake disasters by emphasizing the importance of parameter optimization in earthquake prediction and showcasing the effectiveness of Bi-LSTM and GWO methodologies.
Driver Facial Detection Across Diverse Road Conditions Shofiah, Siti; Sediyono, Eko; Hasibuan, Zainal Arifin; Kristianto, Budhi; Setiawan, Santo; Pratindy, Raka; Hakim, M. Iman Nur; Humami, Faris
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1996.108-114

Abstract

This study emphasizes the importance of facial detection for improving road safety through driver behavior analysis. Its employs quantitative methodology to underscore the importance of facial detection in enhancing road safety through driver behavior analysis. The research utilizes the Python programming language and applies the Haar cascade method to investigate how environmental factors such as low light, shadows, and lighting changes influence the reliability of facial detection. Employing the AdaBoost algorithm, the study achieves face detection rates exceeding 95%. Practical testing with an ASUS A416JA laptop and Raspberry Pi under varied lighting conditions and distances demonstrates optimal performance in detecting faces between 30 cm and 70 cm, with reduced efficacy outside this range, particularly in low light conditions and at night. Challenges identified include decreased performance in low light conditions, emphasizing the need for improved algorithmic calibration and enhancement. Future research directions involve refining detection algorithms to effectively handle diverse environmental conditions and integrating advanced machine learning techniques, thereby enhancing the accuracy of driver behavior analysis in real-world scenarios and contributing to advancements in road safety
An optimation of advanced encryption standard key expansion using genetic algorithm and least significant bit integration Marjuni, Aris; Rijati, Nova; Susanto, Ajib; Sinaga, Daurat; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Yaacob, Noorayisahbe Mohd.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8367

Abstract

Ensuring data security in today’s digital landscape is of paramount importance, driving the exploration of advanced techniques for safeguarding confidential information. This study introduces a robust approach that combines advanced encryption standard (AES) encryption with key expansion, genetic algorithms (GA), and least significant bit (LSB) embedding to achieve secure data concealment within digital images. Motivated by the pressing need for enhanced data protection, our work addresses the critical challenge of securing sensitive information from unauthorized access. Specifically, we present a systematic methodology that integrates AES encryption for robust data security, GA for optimization, and LSB embedding for subtle information concealment. Through comprehensive experimentation, involving images such as ‘Lena.jpg,’ ‘Peppers.jpg,’ and ‘Baboon.jpg,’ we demonstrate the efficacy of our approach. The imperceptible modification rates mean squared error (MSE) of 0.199, 0.101, and 0.105, coupled with high peak signal-to-noise ratios (PSNR) of 10.04 dB, 9.95 dB, and 9.79 dB respectively, underscore the fidelity and subtlety of the embedded information. This study contributes to the ongoing discourse on data security by offering a comprehensive and innovative approach that addresses the evolving challenges in safeguarding digital information.
Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Abstract

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.
PENGEMBANGAN ARSITEKTUR REST API UNTUK INTEGRASI DATA REAL-TIME PADA WEBSITE PEMANTAUAN KUALITAS UDARA LAHAN PERTANIAN Bramantyo, Satrio Bisma; Dewi, Ika Novita; Reza, Ivan Muhammad; Saputra, Filmada Ocky; Hasibuan, Zainal Arifin
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 27, No 1 Januari (2025): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.27.1.49-56

Abstract

Transformasi teknologi dalam sektor pertanian semakin berkembang dengan adopsi perangkat Internet of Things (IoT) yang mampu menghasilkan data real-time terkait kualitas udara, seperti konsentrasi CO2, NO2, dan CH4. Meskipun data yang dihasilkan sangat berharga, namun tantangan besar masih dihadapi dalam hal penyimpanan dan manajemen data yang terfragmentasi. Penelitian ini bertujuan untuk mengembangkan arsitektur REST API yang mampu mengintegrasikan perangkat IoT, basis data, dan antarmuka pengguna dalam bentuk website pemantauan kualitas udara lahan pertanian. REST API digunakan untuk memvalidasi, memproses, dan memformat data yang kemudian dikirimkan ke pusat database melalui protokol HTTP standar (GET, POST). Selain itu, protokol WebSocket diterapkan untuk memastikan komunikasi dua arah yang memungkinkan transmisi data secara real-time antara perangkat IoT dan antarmuka pengguna. Hasil pengujian menunjukkan bahwa arsitektur ini mampu memberikan informasi yang akurat dan cepat kepada petani, mendukung pengambilan keputusan yang lebih baik dalam pengelolaan lahan, serta berkontribusi pada pengurangan emisi gas rumah kaca.
Towards Automated Motor Impulsivity Monitoring in Real-world Scenarios: A Multiple Object Tracking Approach Dalimarta, Fahmy; Andono, Pulung Nurtantio; Soeleman, Moch. Arief; Hasibuan, Zainal Arifin
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i1-16686

Abstract

Assessment of motor impulsivity often faces several challenges. Conventional assessments that rely on controlled settings often fail to capture impulsive behaviors in real-world contexts. This study proposes an automated approach using Multiple Object Tracking (MOT) technology to assess motor impulsivity. The aim was to develop a system for detecting and quantifying motor impulsivity in naturalistic, multi-person environments. By employing cutting-edge MOT algorithms, the solution tracks multiple individuals concurrently, enabling movement and interaction analyses. This methodology integrates MOT with behavioral models to identify motor impulsivity patterns such as abrupt trajectory changes or impulsive gesturing. Trained on real-world annotated datasets, the system ensures adaptability across settings. Our approach successfully distinguished impulsive movements from typical behavioral patterns, with an accuracy of 95.43%. This approach could revolutionize assessments by providing objective and quantitative measurements and facilitating enhanced diagnostics and personalized interventions. Extensive evaluations are required to assess real-time capabilities, robustness in occluded environments, and accurate impulsive pattern identification. These findings could enable broader clinical, research, and behavioral monitoring applications, advancing our understanding of the implications of motor impulsivity.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Development of an Evidence-Based Tool to Assess the Relative Vulnerability of Different Communities to Tuberculosis Isworo, Slamet; Handayani, Sri; Hinchcliff, Reece; Hasibuan, Zainal Arifin
Kesmas Vol. 18, No. 4
Publisher : UI Scholars Hub

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Abstract

Identifying specific tuberculosis (TB) vulnerabilities in populations based on their geographical, demographic, and epidemiological characteristics is anessential yet challenging requirement to help reduce and eliminate TB. Assessment tools that can accurately quantify the risks associated with key factorscould be used to measure TB vulnerability efficiently and indicate the most appropriate range of interventions. This study aimed to develop TB vulnerability assessment tools based on a TB vulnerability assessment conceptual framework developed with Leximancer. Three steps to produce the tools were facetanalysis, interpreting the facet to create a list of questions, and expert judgment to confirm the suitability of the questionnaire. The “everything is data” principlewas used to identify the data sources and build the tools. The data came from multiple primary data sources, with a questionnaire survey and observational form, and secondary data from various governmental statistical departments in Indonesia to collect data related to demography, health indicators, climate,temperature, and air quality. These tools will be optimized at scale next year to evaluate their utility for prioritizing and prescribing health system responses to TB in different communities in Central Java Province.
Co-Authors A.A. Ketut Agung Cahyawan W Ajib Susanto Albaar Rubhasy Andi Kambau, Ridwan Aristo, Nabila Cynthia Bayu Surarso Bramantyo, Satrio Bisma Budhi Kristianto Che Pee, Ahmad Naim Dalimarta, Fahmy Ferdian Dwi Andayani, Dwi Eko Sediyono Erlin Dolphina Guruh Fajar Shidik Hakim, M. Iman Nur Hinchcliff, Reece Humami, Faris Husni Teja Sukmana Ika Novita Dewi Imam Tahyudin Irfan Santiko Isworo, Slamet Kusumawati, Yupie Luhur Bayuaji, Luhur Marjuni, Aris Nova Rijati Pratindy, Raka Pulung Nurtantio Andono Purwanto Purwanto Purwanto, Purwanto Rahman, Abdul Wahab Abdul Reza, Ivan Muhammad Ricardus Anggi Pramunendar Saputra, Filmada Ocky Setiawan, Santo Sinaga, Daurat Siti Shofiah Siti Ummi Masruroh Soeleman, Moch. Arief Sri Handayani Tri Retnaningsih Soeprobowati Untung Rahardja Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd.